Dissertations / Theses on the topic 'Systems biology'

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 219-233).<br>Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. Despite the growing interest in these models, a number of challenges frustrate the construction of high-quality models. First, the chemical reactions that control biochemical processes are only partially known, and multiple, mechanistically distinct models often fit all of the available data and known chemistry. We address this by providing methods for designing dynamic stimuli that can distinguish among models with different reaction mechanisms in stimulus-response experiments. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. Inspired by these computational results, we tested the idea that pulses of EGF could help elucidate the relative contribution of different feedback loops within the EGFR network. These experimental results suggest that models from the literature do not accurately represent the relative strength of the various feedback loops in this pathway. In particular, we observed that the endocytosis and feedback loop was less strong than predicted by models, and that other feedback mechanisms were likely necessary to deactivate ERK after EGF stimulation. Second, chemical kinetic models contain many unknown parameters, at least some of which must be estimated by fitting to time-course data. We examined this question in the context of a pathway model of EGF and neuronal growth factor (NGF) signaling. Computationally, we generated a palette of experimental perturbation data that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, we identified a set of five complementary experiments that could. These results suggest that there is reason to be optimistic about the prospects for parameter estimation in even large models. Third, there is no standard formulation for chemical kinetic models of biological signaling. We propose a general and concise formulation of mass action kinetics based on sparse matrices and Kronecker products. This formulation allows any mass action model and its partial derivatives to be represented by simple matrix equations, which enabled straightforward application of several numerical methods. We show that models that use other rate laws such as MichaelisMenten can be converted to our formulation. We demonstrate this by converting a model of Escherichia coli central carbon metabolism to use only mass action kinetics. The dynamics of the new model are similar to the original model. However, we argue that because our model is based on fewer approximations it has the potential to be more accurate over a wider range of conditions. Taken together, the work presented here demonstrates that experimental design methodology can be successfully used to improve the quality of mechanism-based chemical kinetic models.<br>by Joshua Farley Apgar.<br>Ph.D.

Embryonic development depends on the precise coordination of cell fate specification, patterning and morphogenesis. Although great strides have been made in the molecular understanding of each of these processes, how their interplay governs the formation of complex tissues remains poorly understood. New techniques for experimental manipulation and image quantification enable the study of development in unprecedented detail, resulting in new hypotheses on the interactions between known components. By expressing these hypotheses in terms of rules and equations, computational modeling and simulation allows one to test their consistency against experimental data. However, new computational methods are required to represent and integrate the network of interactions between gene regulation, signaling and biomechanics that extend over the molecular, cellular and tissue scales. In this thesis, I present a framework that facilitates computational modeling of multiscale multicellular systems and apply it to investigate pancreatic development and the formation of vascular networks. This framework is based on the integration of discrete cell-based models with continuous models for intracellular regulation and intercellular signaling. Specifically, gene regulatory networks are represented by differential equations to analyze cell fate regulation; interactions and distributions of signaling molecules are modeled by reaction-diffusion systems to study pattern formation; and cell-cell interactions are represented in cell-based models to investigate morphogenetic processes. A cell-centered approach is adopted that facilitates the integration of processes across the scales and simultaneously constrains model complexity. The computational methods that are required for this modeling framework have been implemented in the software platform Morpheus. This modeling and simulation environment enables the development, execution and analysis of multi-scale models of multicellular systems. These models are represented in a new domain-specific markup language that separates the biological model from the computational methods and facilitates model storage and exchange. Together with a user-friendly graphical interface, Morpheus enables computational modeling of complex developmental processes without programming and thereby widens its accessibility for biologists. To demonstrate the applicability of the framework to problems in developmental biology, two case studies are presented that address different aspects of the interplay between cell fate specification, patterning and morphogenesis. In the first, I focus on the interplay between cell fate stability and intercellular signaling. Specifically, two studies are presented that investigate how mechanisms of cell-cell communication affect cell fate regulation and spatial patterning in the pancreatic epithelium. Using bifurcation analysis and simulations of spatially coupled differential equations, it is shown that intercellular communication results in a multistability of gene expression states that can explain the scattered spatial distribution and low cell type ratio of nascent islet cells. Moreover, model analysis shows that disruption of intercellular communication induces a transition between gene expression states that can explain observations of in vitro transdifferentiation from adult acinar cells into new islet cells. These results emphasize the role of the multicellular context in cell fate regulation during development and may be used to optimize protocols for cellular reprogramming. The second case study focuses on the feedback between patterning and morphogenesis in the context of the formation of vascular networks. Integrating a cell-based model of endothelial chemotaxis with a reaction-diffusion model representing signaling molecules and extracellular matrix, it is shown that vascular network patterns with realistic morphometry can arise when signaling factors are retained by cell-modified matrix molecules. Through the validation of this model using in vitro assays, quantitative estimates are obtained for kinetic parameters that, when used in quantitative model simulations, confirm the formation of vascular networks under measured biophysical conditions. These results demonstrate the key role of the extracellular matrix in providing spatial guidance cues, a fact that may be exploited to enhance vascularization of engineered tissues. Together, the modeling framework, software platform and case studies presented in this thesis demonstrate how cell-centered computational modeling of multi-scale and multicellular systems provide powerful tools to help disentangle the complex interplay between cell fate specification, patterning and morphogenesis during embryonic development.

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach. This thesis will also demonstrate the novel application of ANNs in systems biology of ER, PR and Her2. Furthermore in this research, the integration of ER, PR and Her2 systems have been undertaken to represent a broader view of the breast cancer system. Finally, this thesis will discuss the advantages, limitations, potential application and future potential applications of the methods evaluated.

In order to understand biochemical networks we need to know not only how their parts work but also how they interact with each other. The goal of systems biology is to look at biological systems as a whole to understand how interactions of the parts can give rise to complex dynamics. In order to do this efficiently, new techniques have to be developed. This work shows how tools from mathematics are suitable to study problems in systems biology such as modeling, dynamics prediction, reverse engineering and many others. The advantage of using mathematical tools is that there is a large number of theory, algorithms and software available. This work focuses on how algebra can contribute to answer questions arising from systems biology.<br>Ph. D.

The present Ph.D. thesis is devoted to study, develop and apply approaches commonly used in chemometrics to the emerging field of systems biology. Existing procedures and new methods are applied to solve research and industrial questions in different multidisciplinary teams. The methodologies developed in this document will enrich the plethora of procedures employed within omic sciences to understand biological organisms and will improve processes in biotechnological industries integrating biological knowledge at different levels and exploiting the software packages derived from the thesis. This dissertation is structured in four parts. The first block describes the framework in which the contributions presented here are based. The objectives of the two research projects related to this thesis are highlighted and the specific topics addressed in this document via conference presentations and research articles are introduced. A comprehensive description of omic sciences and their relationships within the systems biology paradigm is given in this part, jointly with a review of the most applied multivariate methods in chemometrics, on which the novel approaches proposed here are founded. The second part addresses many problems of data understanding within metabolomics, fluxomics, proteomics and genomics. Different alternatives are proposed in this block to understand flux data in steady state conditions. Some are based on applications of multivariate methods previously applied in other chemometrics areas. Others are novel approaches based on a bilinear decomposition using elemental metabolic pathways, from which a GNU licensed toolbox is made freely available for the scientific community. As well, a framework for metabolic data understanding is proposed for non-steady state data, using the same bilinear decomposition proposed for steady state data, but modelling the dynamics of the experiments using novel two and three-way data analysis procedures. Also, the relationships between different omic levels are assessed in this part integrating different sources of information of plant viruses in data fusion models. Finally, an example of interaction between organisms, oranges and fungi, is studied via multivariate image analysis techniques, with future application in food industries. The third block of this thesis is a thoroughly study of different missing data problems related to chemometrics, systems biology and industrial bioprocesses. In the theoretical chapters of this part, new algorithms to obtain multivariate exploratory and regression models in the presence of missing data are proposed, which serve also as preprocessing steps of any other methodology used by practitioners. Regarding applications, this block explores the reconstruction of networks in omic sciences when missing and faulty measurements appear in databases, and how calibration models between near infrared instruments can be transferred, avoiding costs and time-consuming full recalibrations in bioindustries and research laboratories. Finally, another software package, including a graphical user interface, is made freely available for missing data imputation purposes. The last part discusses the relevance of this dissertation for research and biotechnology, including proposals deserving future research.<br>Esta tesis doctoral se centra en el estudio, desarrollo y aplicación de técnicas quimiométricas en el emergente campo de la biología de sistemas. Procedimientos comúnmente utilizados y métodos nuevos se aplican para resolver preguntas de investigación en distintos equipos multidisciplinares, tanto del ámbito académico como del industrial. Las metodologías desarrolladas en este documento enriquecen la plétora de técnicas utilizadas en las ciencias ómicas para entender el funcionamiento de organismos biológicos y mejoran los procesos en la industria biotecnológica, integrando conocimiento biológico a diferentes niveles y explotando los paquetes de software derivados de esta tesis. Esta disertación se estructura en cuatro partes. El primer bloque describe el marco en el cual se articulan las contribuciones aquí presentadas. En él se esbozan los objetivos de los dos proyectos de investigación relacionados con esta tesis. Asimismo, se introducen los temas específicos desarrollados en este documento mediante presentaciones en conferencias y artículos de investigación. En esta parte figura una descripción exhaustiva de las ciencias ómicas y sus interrelaciones en el paradigma de la biología de sistemas, junto con una revisión de los métodos multivariantes más aplicados en quimiometría, que suponen las pilares sobre los que se asientan los nuevos procedimientos aquí propuestos. La segunda parte se centra en resolver problemas dentro de metabolómica, fluxómica, proteómica y genómica a partir del análisis de datos. Para ello se proponen varias alternativas para comprender a grandes rasgos los datos de flujos metabólicos en estado estacionario. Algunas de ellas están basadas en la aplicación de métodos multivariantes propuestos con anterioridad, mientras que otras son técnicas nuevas basadas en descomposiciones bilineales utilizando rutas metabólicas elementales. A partir de éstas se ha desarrollado software de libre acceso para la comunidad científica. A su vez, en esta tesis se propone un marco para analizar datos metabólicos en estado no estacionario. Para ello se adapta el enfoque tradicional para sistemas en estado estacionario, modelando las dinámicas de los experimentos empleando análisis de datos de dos y tres vías. En esta parte de la tesis también se establecen relaciones entre los distintos niveles ómicos, integrando diferentes fuentes de información en modelos de fusión de datos. Finalmente, se estudia la interacción entre organismos, como naranjas y hongos, mediante el análisis multivariante de imágenes, con futuras aplicaciones a la industria alimentaria. El tercer bloque de esta tesis representa un estudio a fondo de diferentes problemas relacionados con datos faltantes en quimiometría, biología de sistemas y en la industria de bioprocesos. En los capítulos más teóricos de esta parte, se proponen nuevos algoritmos para ajustar modelos multivariantes, tanto exploratorios como de regresión, en presencia de datos faltantes. Estos algoritmos sirven además como estrategias de preprocesado de los datos antes del uso de cualquier otro método. Respecto a las aplicaciones, en este bloque se explora la reconstrucción de redes en ciencias ómicas cuando aparecen valores faltantes o atípicos en las bases de datos. Una segunda aplicación de esta parte es la transferencia de modelos de calibración entre instrumentos de infrarrojo cercano, evitando así costosas re-calibraciones en bioindustrias y laboratorios de investigación. Finalmente, se propone un paquete software que incluye una interfaz amigable, disponible de forma gratuita para imputación de datos faltantes. En la última parte, se discuten los aspectos más relevantes de esta tesis para la investigación y la biotecnología, incluyendo líneas futuras de trabajo.<br>Aquesta tesi doctoral es centra en l'estudi, desenvolupament, i aplicació de tècniques quimiomètriques en l'emergent camp de la biologia de sistemes. Procediments comúnment utilizats i mètodes nous s'apliquen per a resoldre preguntes d'investigació en diferents equips multidisciplinars, tant en l'àmbit acadèmic com en l'industrial. Les metodologies desenvolupades en aquest document enriquixen la plétora de tècniques utilitzades en les ciències òmiques per a entendre el funcionament d'organismes biològics i milloren els processos en la indústria biotecnològica, integrant coneixement biològic a distints nivells i explotant els paquets de software derivats d'aquesta tesi. Aquesta dissertació s'estructura en quatre parts. El primer bloc descriu el marc en el qual s'articulen les contribucions ací presentades. En ell s'esbossen els objectius dels dos projectes d'investigació relacionats amb aquesta tesi. Així mateix, s'introduixen els temes específics desenvolupats en aquest document mitjançant presentacions en conferències i articles d'investigació. En aquesta part figura una descripació exhaustiva de les ciències òmiques i les seues interrelacions en el paradigma de la biologia de sistemes, junt amb una revisió dels mètodes multivariants més aplicats en quimiometria, que supossen els pilars sobre els quals s'assenten els nous procediments ací proposats. La segona part es centra en resoldre problemes dins de la metabolòmica, fluxòmica, proteòmica i genòmica a partir de l'anàlisi de dades. Per a això es proposen diverses alternatives per a compendre a grans trets les dades de fluxos metabòlics en estat estacionari. Algunes d'elles estàn basades en l'aplicació de mètodes multivariants propostos amb anterioritat, mentre que altres són tècniques noves basades en descomposicions bilineals utilizant rutes metabòliques elementals. A partir d'aquestes s'ha desenvolupat software de lliure accés per a la comunitat científica. Al seu torn, en aquesta tesi es proposa un marc per a analitzar dades metabòliques en estat no estacionari. Per a això s'adapta l'enfocament tradicional per a sistemes en estat estacionari, modelant les dinàmiques dels experiments utilizant anàlisi de dades de dues i tres vies. En aquesta part de la tesi també s'establixen relacions entre els distints nivells òmics, integrant diferents fonts d'informació en models de fusió de dades. Finalment, s'estudia la interacció entre organismes, com taronges i fongs, mitjançant l'anàlisi multivariant d'imatges, amb futures aplicacions a la indústria alimentària. El tercer bloc d'aquesta tesi representa un estudi a fons de diferents problemes relacionats amb dades faltants en quimiometria, biologia de sistemes i en la indústria de bioprocessos. En els capítols més teòrics d'aquesta part, es proposen nous algoritmes per a ajustar models multivariants, tant exploratoris com de regressió, en presencia de dades faltants. Aquests algoritmes servixen ademés com a estratègies de preprocessat de dades abans de l'ús de qualsevol altre mètode. Respecte a les aplicacions, en aquest bloc s'explora la reconstrucció de xarxes en ciències òmiques quan apareixen valors faltants o atípics en les bases de dades. Una segona aplicació d'aquesta part es la transferència de models de calibració entre instruments d'infrarroig proper, evitant així costoses re-calibracions en bioindústries i laboratoris d'investigació. Finalment, es proposa un paquet software que inclou una interfície amigable, disponible de forma gratuïta per a imputació de dades faltants. En l'última part, es discutixen els aspectes més rellevants d'aquesta tesi per a la investigació i la biotecnologia, incloent línies futures de treball.<br>Folch Fortuny, A. (2016). Chemometric Approaches for Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/77148<br>TESIS<br>Premiado

The modern biological sciences are fraught with statistical difficulties. Biomolecular stochasticity, experimental noise, and the “large p, small n” problem all contribute to the challenge of data analysis. Nevertheless, we routinely seek to draw robust, meaningful conclusions from observations. In this thesis, we explore methods for assessing the effects of data variability upon downstream inference, in an attempt to quantify and promote the stability of the inferences we make. We start with a review of existing methods for addressing this problem, focusing upon the bootstrap and similar methods. The key requirement for all such approaches is a statistical model that approximates the data generating process. We move on to consider biomarker discovery problems. We present a novel algorithm for proposing putative biomarkers on the strength of both their predictive ability and the stability with which they are selected. In a simulation study, we find our approach to perform favourably in comparison to strategies that select on the basis of predictive performance alone. We then consider the real problem of identifying protein peak biomarkers for HAM/TSP, an inflammatory condition of the central nervous system caused by HTLV-1 infection. We apply our algorithm to a set of SELDI mass spectral data, and identify a number of putative biomarkers. Additional experimental work, together with known results from the literature, provides corroborating evidence for the validity of these putative biomarkers. Having focused on static observations, we then make the natural progression to time course data sets. We propose a (Bayesian) bootstrap approach for such data, and then apply our method in the context of gene network inference and the estimation of parameters in ordinary differential equation models. We find that the inferred gene networks are relatively unstable, and demonstrate the importance of finding distributions of ODE parameter estimates, rather than single point estimates.

In the post-"omic" era the analysis of high-throughput data is regarded as one of the major challenges faced by researchers. One focus of this data analysis is uncovering biological network topologies and dynamics. It is believed that this kind of research will allow the development of new mathematical models of biological systems as well as aid in the improvement of already existing ones. The work that is presented in this dissertation addresses the problem of the analysis of highly complex data sets with the aim of developing a methodology that will enable the reconstruction of a biological network from time series data through an iterative process. The first part of this dissertation relates to the analysis of existing methodologies that aim at inferring network structures from experimental data. This spans the use of statistical tools such as correlations analysis (presented in Chapter 2) to more complex mathematical frameworks (presented in Chapter 3). A novel methodology that focuses on the inference of biological networks from time series data by least squares fitting will then be introduced. Using a set of carefully designed inference rules one can gain important information about the system which can aid in the inference process. The application of the method to a data set from the response of the yeast Saccharomyces cerevisiae to cumene hydroperoxide is explored in Chapter 5. The results show that this method can be used to generate a coarse-level mathematical model of the biological system at hand. Possible developments of this method are discussed in Chapter 6.<br>Ph. D.

The widespread use of high-throughput experimental assays designed to measure the entire complement of a cells genes or gene products has led to vast stores of data which are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. If the goal is to use this data to infer network models, these sparse datasets can lead to under-determined systems. While model parameter variation and its effects on model robustness has been well studied, most of this work has looked exclusively at accounting for variation only from measurement error. In contrast, little work has been done to isolate and quantify the amount of parameter variation caused by the uncertainty in the unmeasured regions of time course experiments. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured inter- vals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within un- measured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. We also present an application of this method to isolate and quantify two distinct sources of model parameter variation. In the concluding chapter we discuss another source of uncertainty in systems biology, namely gene function prediction, and compare several algorithms designed for that purpose.<br>Ph. D.

Biological regulatory systems are complex due to their role in living organisms in modulating precise responses to changes in internal and external conditions. In this respect, mathematical models have become essential tools to address their complexity for a better understanding of their mechanisms. The vision here, based on integrating experimental and theoretical techniques, provides a systematic means to quantitatively study the characteristics of the interactions that occur in living organisms. The outcome of such an endeavour should provide insights in terms of predictions and quantifications for further investigations in systems and synthetic biology. In this thesis, we establish an integrated modelling framework that can ensure the interaction of experimental biology with the development of quantitative mathematical descriptions of biological systems. To this end, we develop a framework to simulate and analyse biological regulatory systems by integrating different layers of regulatory information. The work herein presents a biological model development workflow in terms of a step by step approach, highlighting challenges and “real life” problems associated with each stage of model development. In the first part, we have focused on applying systems and synthetic biology modelling tools to the phosphate system at the cellular and genetic levels in Escheria coli. Then, we have analysed the interaction mechanisms and the dynamic behaviour of the phosphate starvation response deactivation and evaluated the role of phosphatase activity. We have investigated how the properties of these signalling systems depend on the network structure. Moreover, we have constructed detailed transcriptional regulatory network models and models for promoter design. In the second part, we have designed a multi-level dynamical set up by providing a novel closed loop whole body model of glucose homeostasis coupled with molecular signalling. We have then developed a system embracing the intracellular metabolic level, the cellular level involving the dynamics of the cells, the organ level, and the processes within the whole body. The output of each model directly has been fed with the variables and the parameters of the next aggregated model. This allowed us to observe the metabolic changes that occur at all levels and monitor inter-level communications for Type 2 Diabetes disease.

The focus of my thesis is to apply systems biology approaches to obtain a better understanding of complex cellular systems. In particular, my work concentrates on the function of haematological cells. The first part of the thesis applies a novel, predictive strategy to identify new regulators of a signalling pathway in human immune cells. Cdc42 is a membrane associated GTPase that is an important regulator of cytoskeleton rearrangements and is required for natural killer (NK) cell activation. Previous work had shown that Cdc42 activity oscillates during NK immune surveillance after an initial increase, suggesting that Cdc42 activity is regulated in NK cells by a number of signalling molecules. The aim of the project is to predict proteins required for Cdc42 activity in NK cells and test the predictions. Using a bioinformatics methodology, 13 different proteins were predicted to interact with Cdc42 and form feedback loops. To determine whether any of the predicted proteins were required for Cdc42 activity, NK cells were transfected with a Cdc42 biosensor, each of the predicted targets was downregulated with siRNA and Cdc42 activity was quantified by FRET/FLIM microscopy in the presence of target cells. The screen identified AKT1 and the p85a subunit of PI3K as novel regulators of Cdc42 activity. Depletion of each of these targets also results in an impaired cellular cytotoxic response. This proof of principle study demonstrates the power of a predictive approach in the NK immune surveillance context to identify novel regulators of Cdc42. The second part of my thesis is based on using a predictive methodology, called the ’Phenolog approach’ to predict novel proteins involved in cancer. To determine whether the predicated proteins are required to maintain genome stability, two of the predicted targets were tested using a human primary T cell system, in which cellular mechanisms are normal. Quiescent T cells were transfected with siRNA, the cells were stimulated to enter the cell cycle and chromosome integrity was analysed by interphase FISH. The two predicted targets tested were AND1, a DNA replication protein, and SEC13, a component of the nuclear pore complex. Depletion of each of the targets led to a number of chromosomal abnormalities,indicating that their normal expression during the G0—>G-i transition is required to maintain genome stability. The third part of my thesis focuses on the systematic analyses of the chromatin proteome of T cells during cell cycle progression and identifying changes in the proteome caused by depleting the DMA replication protein Mcm7. Reducing the induction of Mcm7 causes DMA damage, premature chromatid separation and genome instability (291). Initially, the chromatin proteome obtained by a native, non-crosslinked chromatin extraction method was compared with that obtained after formaldehyde crosslinking. In addition, the methodology was compared with the proteome obtained previously using the CSK extraction method (290). To analyse the effects of depleting Mcm7, quiescent primary T cells were transfected with Mcm7 siRNA and the cells were stimulated to enter the cell cycle. The proteins were crosslinked with formaldehyde, chromatin was isolated and the chromatin-bound proteome in Mcm7-depleted and control cells were analysed by LC-MS/MS. Changes in the nuclear proteome caused by depleting Mcm7 were quantified by a label-free spectral counting method. Network analyses (HumanNet) were used to identify protein interaction sub-networks. The analyses identified that downregulation of Mcm7 affects a number of processes, including DNA replication, DNA damage, transcription and ribosome biogenesis.

Implementation and analysis of mathematical models can serve as a powerful tool in understanding how intracellular processes in bacteria affect the bacterial phenotype. In this thesis I have implemented and analysed models of a number of different parts of the bacterium E. coli in order to understand these types of connections. I have also developed new tools for analysis of stochastic reaction-diffusion models. Resistance mutations in the E. coli ribosomes make the bacteria less susceptible to treatment with the antibiotic drug erythromycin compared to bacteria carrying wildtype ribosomes. The effect is dependent on efficient drug efflux pumps. In the absence of pumps for erythromycin, there is no difference in growth between wildtype and drug target resistant bacteria. I present a model explaining this unexpected phenotype, and also give the conditions for its occurrence. Stochastic fluctuations in gene expression in bacteria, such as E. coli, result in stochastic fluctuations in biosynthesis pathways. I have characterised the effect of stochastic fluctuations in the parallel biosynthesis pathways of amino acids. I show how the average protein synthesis rate decreases with an increasing number of fluctuating amino acid production pathways. I further show how the cell can remedy this problem by using sensitive feedback control of transcription, and by optimising its expression levels of amino acid biosynthetic enzymes. The pole-to-pole oscillations of the Min-proteins in E. coli are required for accurate mid-cell division. The phenotype of the Min-oscillations is altered in three different mutants: filamentous cells, round cells and cells with changed membrane lipid composition. I have shown that the wildtype and mutant phenotypes can be explained using a stochastic reaction-diffusion model. In E. coli, the transcription elongation rate on the ribosmal RNA operon increases with increasing transcription initiation rate. In addition, the polymerase density varies along the ribosomal RNA operons. I present a DNA sequence dependent model that explains the transcription elongation rate speed-up, and also the density variation along the ribosomal operons. Both phenomena are explained by the RNA polymerase backtracking on the DNA.<br>Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 715

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”.

The Warburg effect describes the survival advantage of cancer cells in that they can proliferate under low oxygen/hypoxic conditions via a less efficient pathway known as glycolysis. It has not yet been documented at which point, in an oxygen gradient, phenotypic changes occur. Measuring the intracellular redox potential (IRP) and its impact on cellular dynamics would provide greater insight into how disruption of redox homeostasis caused by changes in oxygen concentration leads to aberrant cell signalling and diseases such as cancer. Current techniques in measuring IRP include redox-sensitive fluorescent proteins such as roGFP which is glutathione-specific. Measuring the concentration of one redox couple is, however, not an accurate representation of IRP as it does not necessarily inform about the state of other redox couples. Furthermore, fluorescent biosensors can suffer from photobleaching and may interact with other oxidants. The IRP was measured, in this work, using our newly developed novel-class of surface enhanced Raman scattering nanoparticles which can quantitatively measure the redox potential of cells in vitro. A 'homemade' device was created to keep the cells under fixed pO2 whilst obtaining measurements. The IRP was correlated with the transcriptomic and downstream metabolic profiles of MCF7 breast cancer cells, under perturbed pO2, using 1H NMR spectroscopy (NMR), mass spectrometry (MS) and RNA-sequencing. Discriminatory metabolites were all associated with energy and glucose metabolism. Discriminatory microRNAs were all affiliated with the hallmarks of cancer; the regulation of some is controlled by transcription factors containing redox-sensitive motifs in their DNA binding domains. Multivariate analysis techniques were used to analyse the different data streams in a holistic way that allows the correlation of redox potential, metabolism and transcription.

Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>Understanding gene expression and the regulation thereof that confer cell type-specific (CTS) functionality holds primary importance in devising therapeutics capable of emulating these functions, especially within blood. Hematopoiesis and further differentiation require epigenetic mechanisms to establish and maintain diverse cell identity and function, given constant genomic content. Gene expression and binding of chromatin-associated proteins coincide, and both change during differentiation from hematopoietic stem cells (HSCs) through progenitors with progressively restricted lineage capabilities to terminally differentiated cells. To understand the CTS expression patterns that underlie hematopoiesis, I investigated transcriptomes from discrete stages of blood progenitors, including human HSCs, B lymphocytes, T lymphocytes, and erythrocyte precursors as well as many stages of mouse T lymphocyte development and differentiation. Here, I identify hundreds of genes and numerous gene networks showing CTS expression. I next contextualize CTS expression within chromatin environments, including modified histones and other DNA-binding factors using genome-wide binding data. Specific histone modifications and chromatin proteins are enriched at the transcription start sites (TSSs) of CTS genes and correlate with expression. Surprisingly, certain chromatin marks remain at these CTS TSSs in other cell types. I show that TSSs of differentiation regulators are bivalently primed in HSCs, and become selectively activated in their specific cell type. I predict enhancers of CTS genes and show that their chromatin profiles act in mediating expression. To address regulation of epigenetic modifications during differentiation, I analyzed genome-wide binding profiles oftranscription factor GATA3, which (1) determines T cell lineage commitment, (2) is crucial for differentiation ofT lymphocytes into effector cells, and (3) promotes transcription ofmany T subset-specific genes. I show that GATA3 parsimoniously changes binding patterns during differentiation, and binds a core set of genes as well as T-subset-specific sets. Although GATA3 regulates a small percentage of genes in a cell-type-specific manner, histone modifications at a majority of GATA3-bound genes change significantly after Gata3 deletion, implicating GATA3 in regulatory chromatin organization. I further show that GATA3 binding and function may be mediated by co-binding factors in accord with the presence of their target DNA sequence motifs.

<p> The sheer complexity of evolutionary systems biology requires us to develop more sophisticated tools for analysis, as well as more probing and biologically relevant representations of the data. My research has focused on three aspects of evolutionary systems biology. I ask whether a gene&rsquo;s position in the human metabolic network affects the degree to which natural selection prunes variation in that gene. Using a novel orthology inference tool that uses both sequence similarity and gene synteny, I inferred orthologous groups of genes for the full genomes of 8 mammals. With these orthologs, I estimated the selective constraint (the ratio of non-synonymous to synonymous nucleotide substitutions) on 1190 (or 80.2%) of the genes in the metabolic network using a maximum likelihood model of codon evolution and compared this value to the betweenness centrality of each enzyme (a measure of that enzyme&rsquo;s relative global position in the network). Second, I have focused on the evolution of metabolic systems in the presence of gene and genome duplication. I show that increases in a particular gene&rsquo;s copy number are correlated with limiting metabolic flux in the reaction associated with that gene. Finally, I have investigated the proliferative cell programs present in 6 different cancers (breast, colorectal, gastrointestinal, lung, oral squamous and prostate cancers). I found an overabundance of genes that share expression between cancer and embryonic tissue and that these genes form modular units within regulatory, proteininteraction, and metabolic networks. This despite the fact that these genes, as well as the proteins they encode and reactions they catalyze show little overlap among cancers, suggesting parallel independent reversion to an embryonic pattern of gene expression.</p>

Jede Zelle des Säugetierorganismus benötigt Eisen als Spurenelement für zahlreiche oxidativ-reduktive Elektronentransfer-Reaktionen und für Transport und Speicherung von Sauerstoff. Der Organismus unterhält daher ein komplexes Regulationsnetzwerk für die Aufnahme, Verteilung und Ausscheidung von Eisen. Die intrazelluläre Regulation in den verschiedenen Zelltypen des Körpers ist mit einer globalen hormonellen Signalstruktur verzahnt. Sowohl Eisenmangel wie Eisenüberschuss sind häufige und ernste menschliche Krankheitsbilder. Sie betreffen jede Zelle, aber auch den Organismus als Ganzes. In dieser Dissertation wird ein mathematisches Modell des Eisenstoffwechsels der erwachsenen Maus vorgestellt. In ihm wird die Flussbilanz des Eisens in den wichtigsten Zelltypen in Form von transmembranalen und intrazellulären kinetischen Gleichungen dargestellt, und es werden diese Zellmodelle mit dem zentralen Eisenaustausch-Kompartiment (Blutplasma) des Körpers integriert. Der Eisenstatus wird charakterisiert als Gehalt an labil gebundenen Eisen und an ferritin-gebundenen Eisen für jede Zelle. Der Stoffwechsel wird als Netzwerk von Flussdynamik formuliert. Der experimentelle Input in dieses Modells stammt von verschiedenen Quellen. Radioaktive Tracerdaten, gemessen am intakten Tier (Mausstamm C57BL6 – das am intensivsten studierte Tiermodell) unter varrierten physiologischen Bedingungen lieferten den experimentellen Hintergrund, von dem aus Clearance-Parameter durch numerisches Fitting ermittelt wurden. Es wird gezeigt, dass das Modell mit entsprechend adaptierten Parametersätzen die wichtigsten metabolischen und regulatorischen Ereignisse in Übereinstimmung mit den Messungen darstellen kann. In Zukunft soll die quantitative Übereinstimmung mit Daten aus weiteren genetischen Rekonstruktionen (globale und zell-spezifische knock-outs und konstitutive Expression relevanter Gene des Modellorganismus Maus) hergestellt werden.<br>Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Here a mathematical model of iron metabolism of the adult mouse is presented. It formulates the iron flux balance of the most important cell types of the organism in the form of transmembraneous and intracellular kinetic equations and integrates these cell models with the central exchange compartment (blood plasma) of the body. The iron status is represented as content of labile iron and of ferritin-bound iron in every cell type, and the metabolism is formulated as a network of flux dynamics. The experimental input into the model stems from different sources. Radioactive tracer data measured in the intact animal (mouse strain C57BL6 - the most intensively studied animal model) under various physiological conditions provided the experimental background from which clearance parameters could be obtained by numerical parameter fitting. Future research should render more precise the quantitative representation of genetic reconstructions (global and cell-type-addressed knock-out and constitutive expression of relevant genes of the model mouse strain).

Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.

Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.

Studies on membrane trafficking have expanded massively over the last 40 years. During this time, research has led to an understanding of the molecular mechanisms underlying membrane trafficking pathways, providing crucial insights into several fundamental events. Although we have gained detailed knowledge about the molecular organization of membrane trafficking machineries there is a lack of a global view of its function, organization and regulation. In addition, many genes of the membrane trafficking machinery have been associated with diseases. In the majority of cases, disease manifestation is tissue-specific despite the ubiquitous expression of the causal gene. Explanations for this phenomenon may be found either in the specific requirements and demands of a cell within a given tissue or in differences in the expression of disease gene interactors. The main aim of this project was to delineate sets of co-expressed membrane trafficking genes and proteins (membrane trafficking modules; MTMs) across tissues. For this purpose we curated a list of 1,261 genes that have been described as part of membrane trafficking machineries in different cellular organelles, around which we have developed a bioinformatics pipeline in order to address two specific questions: a) are membrane trafficking genes organized in MTMs, defined as communities of co-expressed genes, and are they associated with general cellular functions? b) do disease genes have specific membrane-trafficking co-expressed communities in those tissues that are affected by the disease? To address these questions we used data from the Genotype-Tissue Expression (GTEx) project, a catalog of human tissue-specific gene expression patterns obtained from “non-diseased” tissues sampled from recently deceased human donors. With regards to the first question, we analyzed the expression patterns of the trafficking genes in twenty-five different tissues and used weighted correlation network analysis (WGCNA) to derive highly preserved MTMs. We have analyzed in more detail one that includes genes apparently involved in collagen secretion. Instead for the second question we applied differential co-expression before the WGCNA to generate tissue-specific MTMs to understand how specific membrane trafficking gene modules might be organized in human tissues.

Machine learning has been a source for continuous methodological advances in the field of computational learning from data. Systems biology has profited in various ways from machine learning techniques but in particular from network inference, i.e. the learning of interactions given observed quantities of the involved components or data that stem from interventional experiments. Originally this domain of system biology was confined to the inference of gene regulation networks but recently expanded to other levels of organization of biological and ecological systems. Especially the application to species interaction networks in a varying environment is of mounting importance in order to improve our understanding of the dynamics of species extinctions, invasions, and population behaviour in general. The aim of this thesis is to demonstrate an extensive study of various state-of-art machine learning techniques applied to a genetic regulation system in plants and to expand and modify some of these methods to infer species interaction networks in an ecological setting. The first study attempts to improve the knowledge about circadian regulation in the plant Arabidopsis thaliana from the view point of machine learning and gives suggestions on what methods are best suited for inference, how the data should be processed and modelled mathematically, and what quality of network learning can be expected by doing so. To achieve this, I generate a rich and realistic synthetic data set that is used for various studies under consideration of different effects and method setups. The best method and setup is applied to real transcriptional data, which leads to a new hypothesis about the circadian clock network structure. The ecological study is focused on the development of two novel inference methods that exploit a common principle from transcriptional time-series, which states that expression profiles over time can be temporally heterogeneous. A corresponding concept in a spatial domain of 2 dimensions is that species interaction dynamics can be spatially heterogeneous, i.e. can change in space dependent on the environment and other factors. I will demonstrate the expansion from the 1-dimensional time domain to the 2-dimensional spatial domain, introduce two distinct space segmentation schemes, and consider species dispersion effects with spatial autocorrelation. The two novel methods display a significant improvement in species interaction inference compared to competing methods and display a high confidence in learning the spatial structure of different species neighbourhoods or environments.

Cancer is one of the most devastating human diseases that cause a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer that would improve diagnostics, prognostics and therapy. Since cancer is known to involve a wide range of processes, applying methods to study it from a systems perspective could reveal new properties of cancer. Systems biology is becoming an increasingly popular tool in the life sciences. The approach has been applied to many biological and biomedical analyses drawing upon recent advancements in technology that make high throughput analyses of samples and computational modelling possible. In this thesis, the effect of hypoxia inducible factor-1 (HIF-1) on cancer metabolism, the entity considered most closely related to phenotype has been investigated. This transcription factor is known to regulate a multitude of genes and proteins to promote survival in a low oxygen environment that is prevalent in solid tumours. However its effect on the metabolome is less well characterised. By revealing the effect of HIF-1 on the metabolome as a system it is hoped that phenotypic signatures, key metabolic pathways indicative of cancer function and potential targets for future cancer therapy, can be revealed.The system has been studied using two cell models: mouse hepatocellular carcinoma and human colon carcinoma, whereby metabolism has been profiled using a range of analytical platforms. In each model, wild type cells have been compared to cells deficient in HIF-1 to reveal its effect on cellular metabolism. Gas chromatography mass spectrometry (GC MS) and ultra high performance liquid chromatography - mass spectrometry (UHPLC MS) have been employed for metabolic profiling of cells exposed to a range of oxygen conditions. Additionally, time-of-flight secondary ion mass spectrometry (ToF SIMS) has been employed for imaging mass spectrometric analysis of multicellular tumour spheroids cultured from wild type cells and cells with dysfunctional HIF-1 to represent small initiating tumours. Using these techniques in metabolic profiling it has been possible to reveal metabolites associated with the effect of oxygen and HIF-1 on cancer metabolism along with key pathways and hubs that could be targeted in future therapy. Using imaging mass spectrometry it has been possible to localise metabolites in situ revealing how tumour structure relates to function. Finally, a novel approach to consider how metabolites are correlated with one another in the response to oxygen level or presence or absence of functional HIF-1 has been undertaken to better understand the systems properties of cancer metabolism. Metabolites found to be differently correlated with respect to oxygen and/or HIF-1 have been mapped onto a human metabolic network to determine their network-based origins. This allowed the simulation of sub-networks of metabolism most affected by oxygen and HIF-1, highlighting the key mechanisms in HIF 1 mediated cancer cell survival.

Introduction: Hypoxia is found in solid cancerous tumours. The presence of hypoxia within tumours inhibits anti-cancer treatment strategies such as chemotherapy from being completely effective and it is suspected that multiple mechanisms contribute to the resistance. Methods: In this project a systems biology approach was applied to determine how the toxicity of doxorubicin is affected by hypoxia at the metabolome level. A multitude of analytical techniques were applied to analyse the intracellular metabolism of a monolayer of cancer cells (MDA-MB-231). Metabolic profiling was used to determine metabolite markers related to hypoxia-induced chemoresistance. For this gas chromatography mass spectrometry (GC-MS) and ultra high performance liquid chromatography mass spectrometry (UHPLC-MS) were used. Furthermore, network-based correlation analysis was developed as a novel tool to bridge the gap between metabolomics dataset and systems biology modelling. This methodology was applied to elucidate novel metabolic pathways as potential therapeutic targets to overcome hypoxia-induced chemoresistance. This algorithm determines significant correlation differences between different physiological states, and through applying graph-theory on large genome scale models; it is possible to construct a metabolic network of the pathways connecting the pair-wise correlation. Finally, imaging mass spectrometry using time-of-flight secondary ion mass spectrometry (ToF-SIMS) was developed as a tool for in situ metabolite analysis to investigate the metabolic response to chemotherapy in multi-tumour spheroids (MTSs). Results: Metabolic fingerprinting analysis characterised a snapshot of cells exposed to various environmental perturbations. Metabolite markers associated with hypoxia-induced chemoresistance were related to metabolic pathways including gluconeogenesis, DNA synthesis and fatty acid synthesis. Furthermore, network-based correlation analysis revealed specific metabolites in the fatty acid synthesis pathways were contributing to drug resistance, which included malonyl-CoA, 3-oxoeicosanoyl-CoA, stearoyl-CoA and octadecanoic acid. To facilitate the detection of metabolites in ToF SIMS datasets, a series of metabolites standard spectra were acquired. Hypoxic metabolite markers detected in ToF-SIMS data of cell lysates included glycine, lactic acid and succinic acid, which were also shown to be metabolite markers in GC-MS metabolic data. Furthermore, MTS sections were imaged using ToF-SIMS to profile the chemical response to chemotherapy treatment within the oxygen gradient. Loadings from image PCA were explored to determine the metabolic response in the highly oxygenated outer region and hypoxic inner region of the MTS. Conclusion: A multitude of analytical techniques were able to contribute to elucidating the metabolic mechanisms associated with hypoxia-induced chemoresistance. Metabolic profiling combined with a systems biology approach was further able to identify potential underlying metabolic regulation of resistance. Finally ToF-SIMS was developed as a tool for metabolite analysis in complex biological systems in situ.

Analysis of time series data constitutes an important activity in many scientific disciplines. Over the last years there has been an increase in the collection of time series data in all scientific fields and disciplines, such as the industry and engineering. Due to the increasing size of the time series datasets, new automated time series data mining techniques have been devised for comparing time series data and present information in a logical and easily comprehensible structure.In systems biology in particular, time series are used to the study biological systems. The time series representations of a systems’ dynamics behaviour are multivariate time series. Time series are considered multivariate when they contain observations for more than one variable component. The biological systems’ dynamics time series contain observations for every feature component that is included in the system; they thus are multivariate time series. Recently, there has been an increasing interest in the collection of biological time series. It would therefore be beneficial for systems biologist to be able to compare these multivariate time series.Over the last decade, the field of time series analysis has attracted the attention of people from different scientific disciplines. A number of researchers from the data mining community focus their efforts on providing solutions on numerous problems regarding different time series data mining tasks. Different methods have been proposed for instance, for comparing, indexing and clustering, of univariate time series. Furthermore, different methods have been proposed for creating abstract representations of time series data and investigating the benefits of using these representations for data mining tasks.The introduction of more advanced computing resources facilitated the collection of multivariate time series, which has become common practise in various scientific fields. The increasing number of multivariate time series data triggered the demand for methods to compare them. A small number of well-suited methods have been proposed for comparing these multivariate time series data.All the currently available methods for multivariate time series comparison are more than adequate for comparing multivariate time series with the same dimensionality. However, they all suffer the same drawback. Current techniques cannot process multivariate time series with different dimensions. A proposed solution for comparing multivariate time series with arbitrary dimensions requires the creation of weighted averages. However, the accumulation of weights data is not always feasible.In this project, a new method is proposed which enables the comparison of multivariate time series with arbitrary dimensions. The particular method is evaluated on multivariate time series from different disciplines in order to test the methods’ applicability on data from different fields of science and industry. Lastly, the newly formed method is applied to perform different time series data mining analyses on a set of biological data.

Cancer cells have been known for some time to have very different metabolismas compared to that of normal non proliferating cells. As metabolism is involvedin almost every aspect of cell function, there has been a recent resurgence ofinterest in inhibiting cancer metabolism as a therapeutic strategy. Inhibitors thatspecifically target altered metabolic components in cancer cells are being developedas antiproliferative agents. However, many such inhibitors have not progressedinto the clinic due to limited efficacy either in vitro or in vivo. In this study weexplore the hypothesis that this is often due to the robustness of the metabolicnetwork and the differences between individual cancer cell lines in their metaboliccharacteristics. We take a systems biology approach. We investigate the cellular bioenergetic profiles of a panel of five non-small celllung cancer cell lines before and after treatment with a novel inhibitor of theglutaminase-1 (GLS1) enzyme. Additionally, we explore the effects of this inhibitoron intracellular metabolism of these cell lines as well as on the uptake and secretionof glucose, lactate and amino acids. To be able to do the latter robustly, wehad to modify the experimental assay considerably from procedures that seemto be standard in the literature; using these earlier procedures the metabolicenvironment of the cells was highly variable, leading to misleading results onthe metabolic effects of the inhibitor. We reduced cell density, altered mediumvolume and changed the time-window of the assay. This led to the cells growingexponentially, appearing indifferent to the few remaining changes. In this newassay, the metabolic effects of the glutaminase inhibitor became robust. One of the most significant results of this study is the metabolic heterogeneitydisplayed across the cell line panel under basal conditions. Differences in themetabolic functioning of the cell lines were observed in terms of both theirbioenergetic and metabolic profile. The amount of respiration attributed tooxidative phosphorylation differed between cell lines and respiratory capacity wasattenuated in most cells. However, the rate of glycolysis was similar betweencell lines in this assay. These results suggest that the Warburg effect arisesthrough a greater diversity of mechanisms than traditionally assumed, involvingvarious combinations of changes in the expression of glycolytic and mitochondrialmetabolic enzymes. The effects of GLS1 inhibition on cellular bioenergetics and metabolism alsodiffered between cell lines, even between resistant cell lines, indicating that theremay also be a diversity of resistance mechanisms. The metabolomic response ofcell lines to treatment suggests potential resistance mechanisms through metabolicadaptation or through the prior differences in the metabolic function of resistantcell lines. Part of the metabolome response to GLS1 inhibition was quite specificfor sensitive cells, with high concentrations of IMP as the strongest marker. Our results suggest that the metabolome is a significant player in what determinesthe response of cells to metabolic inhibitors, that its responses differ between cancercells, that responses are not beyond systems understanding, and that thereforethe metabolome should be taken into account in the design of and therapy withanti-cancer drugs.

A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articular cartilage in the affected joint, debilitating pain and decreased mobility. At present there are no disease modifying drugs for treatment of osteoarthritis. This represents a significant, unmet medical need as there is a large and increasing prevalence of OA. Using a systems biology approach, we aimed to better understand the pathogenic mechanisms of OA and ultimately aid development of therapeutics. This thesis focuses on the analysis of gene expression data from human OA cartilage obtained at total knee replacement (TKR). This transcriptomics approach gives a genome-wide overview of changes, but can be challenging to interpret. Network-based algorithms provide a framework for the fusion of knowledge so allowing effective interpretation. The PhenomeExpress algorithm was developed as part of this thesis to aid the interpretation of gene expression data. PhenomeExpress uses known disease gene associations to identify relevant dysregulated pathways in the data. PhenomeExpress was further developed into an 'app' for Cytoscape, the widely used network analysis and visualisation platform. To investigate the processes that occur during the degradation of cartilage we examined the gene expression of damaged and intact OA cartilage using RNA-Seq and identified key altered pathways with PhenomeExpress. A regulatory network driven by four transcription factors accounts for a significant proportion of the observed differential expression of damage-associated genes in the PhenomeExpress identified pathways. We further explored the role of the cytokines IL-1 and TNF that have been reported to β drive the progression of OA. Comparison of the expression response of in vitro cytokine-treated explants with the in vivo damage response revealed major differences, providing little evidence for any significant role of IL-1 and TNF as drivers of OA β damage in vivo. Finally, we examined the heterogeneity of OA through analysis of cartilage expression profiles at TKR. Through a network-based clustering method, we found two subgroups of patients on the basis of their gene expression profiles. These subgroups were found to have distinct OA expression perturbations and we identified TGF and S100A8/9 β signalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into these subgroups so allowing future assessment of the clinical significance of these subgroups. The work presented in this thesis includes a novel, widely-accessible tool for the analysis of disease gene expression data, which we used to give new insights into the pathogenesis of osteoarthritis. We have produced a rich dataset for future research and our analysis of this data has increased our understanding of cartilage damage processes and the heterogeneity of OA.

<p>In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance.This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options.The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.In the field of systems biology the task of finding optimal model parameters is a common procedure. The optimization problems encountered are often multi-modal, i.e., with several local optima. In this thesis, a class of algorithms for multi-modal problems called metaheuristics are studied. A downside of metaheuristic algorithms is that they are dependent on algorithm settings in order to yield ideal performance.</p><p>This thesis studies an approach to tune these algorithm settings using user constructed test functions which are faster to evaluate than an actual biological model. A statistical procedure is constructed in order to distinguish differences in performance between different configurations. Three optimization algorithms are examined closer, namely, scatter search, particle swarm optimization, and simulated annealing. However, the statistical procedure used can be applied to any algorithm that has configurable options.</p><p>The results are inconclusive in the sense that performance advantages between configurations in the test functions are not necessarily transferred onto real biological models. However, of the algorithms studied a scatter search implementation was the clear top performer in general. The set of test functions used must be studied if any further work is to be made following this thesis.</p>

Thesis (Ph.D.) - University of Glasgow, 2007.<br>Ph.D. thesis submitted to the Information and Mathematical Sciences Faculty, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

Systems biology is a rapidly growing field which seeks a refined quantitative understanding of organisms, particularly studying how molecular species such as metabolites, proteins and genes interact in cells to form the complex emerging behaviour exhibited by living systems. Synthetic biology is a related and emerging field which seeks to engineer new organisms for practical purposes. Both fields can benefit from formal languages for modelling, simulation and analysis. In systems biology there is however a trade-off in the landscape of existing formal languages: some are modular but may be difficult for some biologists to understand (e.g. process calculi) while others are more intuitive but monolithic (e.g. rule-based languages). The first major contribution of this thesis is to bridge this gap with a Language for Biochemical Systems (LBS). LBS is based on the modular Calculus of Biochemical Systems and adds e.g. parameterised modules with subtyping and a notion of nondeterminism for handling combinatorial explosion. LBS can also incorporate other rule-based languages such as Kappa, hence adding modularity to these. Modularity is important for a rational structuring of models but can also be exploited in analysis as is shown for the specific case of Petri net flows. On the synthetic biology side, none of the few existing dedicated languages allow for a high-level description of designs that can be automatically translated into DNA sequences for implementation in living cells. The second major contribution of this thesis is exactly such a language for Genetic Engineering of Cells (GEC). GEC exploits the recent advent of standard genetic parts (“biobricks”) and allows for the composition of such parts into genes in a modular and abstract manner using logical constraints. GEC programs can then be translated to DNA sequences using a constraint satisfaction engine based on a given database of genetic parts.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, February 2013<br>"December 2012." Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Multiple signaling pathways are employed by endothelial cells to differentially respond to distinct hemodynamic environments and acquire functional phenotypes, including regulation of inflammation, angiogenesis, blood coagulation, and the vascular tone. In order to understand how these pathways interact, this thesis applies a systems biology approach through a two-step process. First, we constructed an integrated mathematical model for shear-stress-induced nitric oxide (NO) production to assemble the current understanding of this signaling system. Second, we conducted experiments to define how shear stress dynamically modulates the expression of components of the endothelial glycocalyx, a mechanosensor that regulates shear-stressdependent NO production. Nitric oxide produced by vascular endothelial cells is an anti-inflammatory mediator and a potent vasodilator. In order to understand the rich diversity of responses observed experimentally in endothelial cells exposed to shear stress, we assembled four quantitative molecular pathways previously defined for shear-stress-induced NO production. In these pathways, endothelial nitric oxide synthase (eNOS) is activated (a) via calcium release, (b) via phosphorylation reactions, and (c) via enhanced protein expression. To these pathways we added (d) an additional pathway describing the actual NO production from the interactions of eNOS with its various protein partners. These pathways were then combined and simulated. The integrated model is able to describe the experimentally observed change in NO production with time following the application of fluid shear stress, and to predict the specific effects to the system following interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system and provides a platform to aggregate information in an additive way. The endothelial glycocalyx is a glycosaminoglycan layer located on the apical surface of vascular endothelial cells. Previous studies have documented a strong correlation between the glycocalyx expression, local hemodynamic environment, and atheroprotection. Based on these observations, we hypothesized that the expression of components of the endothelial glycocalyx is differentially regulated by distinct hemodynamic environments. In order to test this hypothesis, human endothelial cells were exposed to shear stress waveforms characteristic of atherosclerosis-resistant or atherosclerosis-susceptible regions of the human carotid, and the expression of several components of the glycocalyx was then assessed. Interestingly, we found that heparan sulfate expression is higher and evenly distributed on the apical surface of endothelial cells exposed to the atheroprotective waveform, and is irregularly present in cells exposed to the atheroprone waveform. Furthermore, the expression of a heparan sulfate proteoglycan, syndecan-1, is also differentially regulated by the two waveforms, and its suppression mutes the atheroprotective-flow-induced cell surface expression of heparan sulfate. Collectively, these data links distinct hemodynamic environments to the differential expression of critical components of the endothelial glycocalyx. Taken together, these projects present in this doctoral thesis increase our understanding of endothelial mechano-activated pathways, and have demonstrated how we could use systems biology approach to unravel complex biological problems.<br>by Andrew Jia-An Koo.<br>Ph.D.

Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on thereconstruction of signal transduction mechanisms in the signaling pathway components. Wepropose gene set based approaches for inferring the structure of a signaling pathway.First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case modelby accommodating prior knowledge of replication mechanisms. Second, we generalize theblind-case model by designing a two-component mixture model. Our idea is to strike anoptimal balance between a fully constrained correlation structure and an unconstrained one.Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements.We utilize our correlation estimators for clustering real-world replicated complete and incompletemolecular profiling data sets. The above three components constitute the first partof the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random.The first algorithm has been developed under the Gibbs sampling framework and the secondalgorithm utilizes the framework of simulated annealing. Finally, we summarize our findingsand discuss possible future directions.

Dissertation presented to obtain a Master degree in Biotechnology<br>In complex biological systems, it is unlikely that all relevant cellular functions can be fully described either by a mechanistic (parametric) or by a statistic (nonparametric) modelling approach. Quite often, hybrid semiparametric models are the most appropriate to handle such problems. Hybrid semiparametric systems make simultaneous use of the parametric and nonparametric systems analysis paradigms to solve complex problems. The main advantage of the semiparametric over the parametric or nonparametric frameworks lies in that it broadens the knowledge base that can be used to solve a particular problem, thus avoiding reductionism. In this M.Sc. thesis, a hybrid modelling method was adopted to describe in silico Escherichia coli cells. The method consists in a modified projection to latent structures model that explores elementary flux modes (EFMs) as metabolic network principal components. It maximizes the covariance between measured fluxome and any input “omic” dataset. Additionally this method provides the ranking of EFMs in increasing order of explained flux variance and the identification of correlations between EFMs weighting factors and input variables. When applied to a subset of E. coli transcriptome, metabolome, proteome and envirome (and combinations thereof) datasets from different E. coli strains (both wild-type and single gene knockout strains) the model is able, in general, to make accurate flux predictions. More particularly, the results show that envirome and the combination of envirome and transcriptome are the most appropriate datasets to make an accurate flux prediction (with 88.5% and 85.2% of explained flux variance in the validation partition, respectively). Furthermore, the correlations between EFMs weighting factors and input variables are consistent with previously described regulatory patterns, supporting the idea that the regulation of metabolic functions is conserved among distinct envirome and genotype variants, denoting a high level of modularity of cellular functions.

In Systems Biology, it is usual to use a set of ordinary differential equations to characterize biological function at a system level. The parameters in these equations generally reflect the reaction or decay rates of a molecular species, while states characterize the concentration values of species of interest, e.g. mRNA, proteins and metabolites. Often parameter values are estimated from in vitro experiments which may not be true reflections of the in vivo environments. With internal states, some may not be accessible for experimental measurement. Hence there is interest in estimating parameter values and states from noisy or incomplete observations taken at inputs/outputs of a system. This thesis explores several probabilistic inference approaches to do this. The study starts from a thorough investigation of the effectivenesses of the most commonly used one-pass inference methods, from which the non-parametric particle filtering approach is shown to be the most powerful method in the sequential category. After this study, the family of Approximate Bayesian Computation (ABC) methods, also known as likelihood-free batch approach, is reviewed chronologically and its advantages and deficiencies are summarized via a statistical toy example and two biological models. Additionally, a novel ABC method coupled with the sensitivity analysis technique has been developed and demonstrated on three periodic and one transient biological models. This approach has the potential to solve problem in high dimension by selectively allocating computational budget. In order to assess the capability of the proposed method in real-world problems, we have modeled the polymer pathway and conducted quantitative analysis via the proposed inference approach.

This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this thesis? They range from the simplest biological agents, the viruses, in this case the Potyvirus genus to prokariotes such as Escherichia coli and complex eukariotes (Arabidopsis thaliana, Nicotiana benthamiana). The second axis refers to which biological networks were studied. Those are protein-protein interaction (PPIN) and metabolic networks (MN). The final axis relates to the mathematical and modelling tools used to generate knowledge from those networks. These tools can be classify in three main branches: graph theory, constraint-based modelling and multivariate statistics. The document is structured in six parts. The first part states the justification for the thesis, exposes a general thesis roadmap and enumerates its main contributions. In the second part important literature is reviewed, summarized and integrated. From the birth and development of Systems Biology to one of its most popular branches: biological network analysis. Particular focus is put on PPIN and MN and their structure, representations and features. Finally a general overview of the mathematical tools used is presented. The third, fourth and fifth parts represent the central work of this thesis. They deal respectively with genotypephenotype interaction and classical network analysis, constraint-based modelling methods comparison and modelling metabolic networks and community structure. Finally, in the sixth part the main conclusions of the thesis are summarized and enumerated. This thesis highlights the vital importance of studying biological entities as systems and how powerful and promising this integrated analysis is. Particularly, network analysis becomes a fundamental avenue of research to gain insight into those biological systems and to extract, integrate and display this new information. It generates knowledge from just data.<br>Esta tesis está dedicada al estudio y comprensión de redes biológicas a nivel molecular. Los objetivos fueron analizar su topología, integrar esta en un análisis de genotipo-fenotipo, desarrollar descripciones matemáticas más completas para ellas, estudiar su estructura de comunidades y comparar diferentes metodologías para estimar sus flujos internos. El trabajo presentado en este documento gira entorno a tres ejes principales. El primero es el biológico. ¿Qué organismos han sido estudiados en esta tesis? Estos van desde los agentes biológicos mas simples, los virus, en este caso el género Potyvirus, hasta procariotas como Escherichia coli y eucariotas complejos (Arabidopsis thaliana, Nicotiana benthamiana). El segundo eje hace referencia a las redes biológicas estudiadas, que fueron redes de interacción de proteínas (PPIN) y redes metabólicas (MN). El eje final es el de las herramientas matemáticas y de modelización empleadas para interrogar esas redes. Estas herramientas pueden clasificarse en tres grandes grupos: teoría de grafos, modelización basada en restricciones y estadística multivariante. Este documento está estructurado en seis partes. La primera expone la justificación para la tesis, muestra un mapa visual de la misma y enumera sus contribuciones principales. En la segunda parte, la bibliografía relevante es revisada y resumida. Desde el nacimiento y desarrollo de la Biología de Sistemas hasta una de sus ramas más populares: el análisis de redes biomoleculares. Especial interés es puesto en PPIN y MN: su estructura, representación y características. Finalmente, un resumen general de las herramientas matemáticas usadas es presentado. Los capítulos tercero, cuarto y quinto representan el cuerpo central de esta tesis. Estos tratan respectivamente sobre la interacción de genotipo-fenotipo y análisis topolólogico clásico de redes, modelos basados en restricciones y modelización de redes metabólicas y su estructura de comunidades. Finalmente, en la sexta parte las principales conclusiones de la tesis son resumidas y expuestas. Esta tesis pone énfasis en la vital importancia de estudiar los fenómenos biológicos como sistemas y en la potencia y prometedor futuro de este análisis integrativo. En concreto el análisis de redes supone un camino de investigación fundamental para obtener conocimiento sobre estos sistemas biológicos y para extraer y mostrar información sobre los mismos. Este análisis genera conocimiento partiendo únicamente desde datos.<br>Aquesta tesi està dedicada a l'estudi i comprensió de xarxes biològiques a nivell molecular. Els objectius van ser analitzar la seva topologia, integrar aquesta en una anàlisi de genotip-fenotip, desenvolupar descripcions matemàtiques més completes per a elles, estudiar la seva estructura de comunitats o modularitat i comparar diferents metodologies per estimar els fluxos interns. El treball presentat en aquest document gira entorn de tres eixos principals. El primer és el biològic. ¿Què organismes han estat estudiats en aquesta tesi? Aquests van des dels agents biològics mes simples, els virus, en aquest cas el gènere Potyvirus, fins procariotes com Escherichia coli i eucariotes complexos (Arabidopsis thaliana, Nicotiana benthamiana). El segon eix fa referència a les xarxes biològiques estudiades, que van ser les xarxes d'interacció de proteïnes (PPIN) i les xarxes metabòliques (MN). L'eix final és el de les eines matemàtiques i de modelització emprades per interrogar aquestes xarxes. Aquestes eines poden classificarse en tres grans grups: teoria de grafs, modelització basada en restriccions i estadística multivariant. Aquest document està estructurat en sis parts. La primera exposa la justificació per a la tesi, mostra un mapa visual de la mateixa i enumera les seves contribucions principals. A la segona part, la bibliografia rellevant és revisada i resumida. Des del naixement i desenvolupament de la Biologia de Sistemes fins a una de les seves branques més populars: l'anàlisi de xarxes moleculars. Especial interès és posat en PPIN i MN: la seva estructura, representació i característiques. Finalment, un resum general de les eines matemàtiques utilitzades és presentat. Els capítols tercer, quart i cinquè representen el cos central d'aquesta tesi. Aquests tracten respectivament sobre la interacció de genotip-fenotip i anàlisi topolólogico clàssic de xarxes, models basats en restriccions i modelització de xarxes metabòliques i la seva estructura de comunitats. Finalment, en la sisena part les principals conclusions de la tesi són resumides i exposades. Aquesta tesi posa èmfasi en la vital importància d'estudiar els fenòmens biològics com sistemes i en la potència i prometedor futur d'aquesta anàlisi integratiu. En concret l'anàlisi de xarxes suposa un camí d'investigació fonamental per obtenir coneixement sobre aquests sistemes biològics i per extreure i mostrar informació sobre els mateixos. Aquest anàlisi genera coneixement partint únicament des de dades.<br>Bosque Chacón, G. (2017). Network Analysis and Modeling in Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79082<br>TESIS

Quantitative one-dimensional proton NMR metabolomics is performed on growth medium samples gathered at up to ten time-points during the in vitro culture of P. falciparum in human red blood cells. From this study, exchange fluxes between the parasite-host complex and the growth medium are calculated for glucose, lactate, glycerol, glutamine, hypoxanthine, valine, leucine, isoleucine, alanine, tyrosine and phenylanaine. Carbon-source exchange fluxes are added as constraints to a new model of malaria metabolism — built using my published MetNetMaker software — consisting of 249 reactions, 143 genes and a novel experimentally derived biomass function. Analysis of this network including by flux-balance analysis and flux-variability analysis are projected onto a live map of the network providing the most accessible view of malaria metabolism to date. This model reproduces key phenotypes of the malaria parasite such as the unusual branched TCA cycle, and accurately predicts internal fluxes through the pentose-phosphate cycle and the low oxygen-dependence of the parasite’s metabolism during its erythrocytic life stages. The model is carbon balanced and accurately predicts the parasite’s growth-rate at measured glucose uptake rates. Furthermore, it accurately reproduces measured amino acid and purine-source exchange fluxes at the optimal solution and implies that the parasite digests 30% of its red blood cell host’s haemoglobin but incorporates just 40% of the resulting freed amino acids into its proteome. Lethal single and double gene deletions are predicted and suggest potential drug and vaccine targets. The metabolic model is available in MetNetMaker format for easy editing, SBML format including constraints for metabolic modelling and the independent reproduction of the reported results, and cytoscape format with metadata for visualisation of both the network and the results of simulations performed on it.

The focus of this thesis was determined by the FP7-funded e-infrastructure project Multiscale Applications on European e-Infrastructures (MAPPER). The main goal of MAPPER was to develop a distributed multiscale computing frame­work facilitating the development, deployment and use of multiscale modelling and simulation applications in various domains. MAPPER was strongly in­volved with the computing aspects of multiscale modelling and simulation. Within the MAPPER project, the research described in this thesis was focused on the development of novel (a) general multiscale modelling and simulation methods and technologies, and (b) multiscale computational systems biology methods and tools. We chose gene regulation as the main biological problem domain to drive our R&D efforts. An important way to investigate gene regulation is through automated reverse ­engineering of mechanistic dynamic GRN models from gene expression time- series data. This, however, is limited by the quality and amount of available data and the computational complexity of the reverse-engineering process. The specific objective of this thesis was to develop and assess novel solutions for reverse-engineering GRN models from gene expression data. This objective was explored from three main perspectives. First, to facilitate the development of improved approaches to GRN model reverse-engineering, we explored the representational and computational as­pects of various GRN rate laws. Second, we explored how the computational aspects of the GRN model reverse-engineering problem could be viewed as a distributed multiscale computing problem. A major piece of R&D that res­ulted from this was the development of MultiGrain/MAPPER, a software tool that allows the multiscale modelling and simulation of GRNs. Third, based on MultiGrain/MAPPER and other software we created, we developed and assessed various new reverse-engineering algorithms and investigated their performance in terms of effectiveness and efficiency.

Cancers develop in complex microenvironments whose importance was emerging during last years. In fact, cancer microenvironment influences tumor progression and leads to the raising of chemotherapics resistance. Thus, a shift of the focus from cancer cells to cancer cells in their environment is crucial for studying the molecular and metabolic features of tumors in physiological contexts. Within the microenvironment, cancer associated fibroblasts (CAF) are attracting the attention of scientific community since, up to date, it is clear that they are the main component involved in the organization of tumor tissue and that they interact with cancer cells and affect their behavior. Importantly, stromal cell types within the microenvironment are genetically stable, contrary to the tumor counterpart that frequently shows genetic instability and seriously altered physiological mechanisms. Accordingly, they represent a valuable therapeutic target. For these reasons, in this work we investigated the interplay between prostate cancer cells and tumor microenvironment, focusing on CAFs' role and on alterations of cancer cells occurring due their presence. In the first part of the study we collected expression profile data from cultures and co-cultures as well as metabolic data in order to characterize prostate cancer cells (PC3), CAFs and normal fibroblasts and alterations occurring in these cells n co-culture conditions. We found deregulated genes, strictly related to activation of processes which ease conditions favoring tumor progression in PC3 cells and in CAFs. More, we found deregulated genes in CAFs which usually are described as deregulated in transformed cells from patients and studied in PC3s, highlighting the importance of considering the stromal component as playing a major role in driving tumor progression. Through analyses of metabolic data we shed light on metabolic needs of PC3s and CAFs and on the metabolic behavior adopted by CAFs to enhance PC3s' growth. In the second part of the study, the retrieved data were used to build a metabolic model which in turn was used to validate an algorithm we developed, Metpath, designed toto improvestudies of unknown pathways' alterations. The models we built, a single cell model for PC3 and S9 and one model representing interacting PC3 and CAF cells, showed to be able to recapitulate the main metabolic reactions occurring in the corresponding conditions and represent a valid starting ground on which refined models for further studies could be developed. Moreover, MetPathrevealed altered metabolic pathways in PC3 and in CAF, which deserve further studies to deepen the knowledge about the way they can confer advantages to tumor. Taken together, all the results obtained in this work demonstrate the importance of considering and studying the tumors not only as a set of cancer cells but as part ofa complex microenvironment which influences tumor's progression. Importantly, this workhighlights the importance of the modern approaches to cancer study. In fact, simulation studies on --omics integrated models, like the ones elaborated in this work, can really push forward research in the multidisciplinary field of cancer research.

Cellular regulation is governed by dense biomolecular networks consisting of proteins, nucleotides, lipids, and metabolites that dynamically coordinate cellular decision making in the face of complex and time-varying environmental stimuli. Obtaining predictive models of these complex networks is a central goal of systems biology and requires sophisticated technologies for the acquisition and integration of many disparate data types. Recent genomic, proteomic and cellular imaging developments have greatly enabled systems-level studies, but further technological advances are needed. For instance, current high-throughput biochemical and cellular measurement techniques are generally limited to the analysis of cell populations, and the development of single-cell technologies are needed to advance predictive models of cellular networks. Large-scale genetic analyses are highly informative of the complex architecture of cellular networks but further computational methods are required to manage data complexity. In this thesis I present the development of two technologies, a microfluidic single-cell experimental platform and a genetic-network computational analysis platform, to address these issues and apply them to the study of prototypical eukaryotic signaling systems in Saccharomyces cerevisiae. First I describe microfluidic technology for the high-throughput analysis of single-cells subject to complex environmental conditions. Using this platform, I studied cellular response of the mating pathway in Saccharomyces cerevisiae under a series of genetic and time-varying environmental perturbations. This analysis revealed dynamic phenotypes that are not observable under static conditions and allowed for the stratification of system components into distinct functional roles. In addition, I describe advances to this technology that allow for the tracking of individual cells over long experimental time frames. These developments enabled the investigation of sources of cell-to-cell variability not detectable otherwise. Second I describe a computational platform for analyzing complex genetic interaction networks. These networks describe functional relationships between gene systems and can be used to delineate information flows through complex cellular circuits. Genetic interactions networks are dense and information rich, and require sophisticated computational methods for their analysis. In this work, I developed network algorithms to identify biologically informative patterns within a multi-mode genetic interaction network to reveal functional sub-networks and information-hubs of the filamentation pathway in Saccharomyces cerevisiae.

The field of Synthetic Biology seeks to develop engineering principles for biological systems. Modular biological parts are repurposed and recombined to develop new synthetic biological devices with novel functions. The proper functioning of these devices is dependent on the cellular context provided by the host organism, and the interaction of these devices with host systems. The field of Systems Biology seeks to measure and model the properties of biological phenomena at the network scale. We present the application of systems biology approaches to synthetic biology, with particular emphasis on understanding and remodeling metabolic networks. Chapter 2 demonstrates the use of a Flux Balance Analysis model of the Saccharomyces cerevisiae metabolic network to identify and construct strains of S. cerevisiae that produced increased amounts of formic acid. Chapter 3 describes the development of synthetic metabolic pathways in Escherichia coli for the production of hydrogen, and a directed evolution strategy for hydrogenase enzyme improvement. Chapter 4 introduces the use of metabolomic profiling to investigate the role of circadian regulation in the metabolic network of the photoautotrophic cyanobacterium Synechococcus elongatus PCC 7942. Together, this work demonstrates the utility of network-scale approaches to understanding biological systems, and presents novel strategies for engineering metabolism.

Systems biology is a comprehensive quantitative analysis of the manner in which all the components of a biological system interact functionally along with time. Mathematical modeling and computational methods are indispensable in such kind of studies, especially for interpreting and predicting the complex interactions among all the components so as to obtain some desirable system properties. System dynamics, system robustness and control method are three crucial properties in systems biology. In this thesis, the above properties are studied in four different biological systems. The outbreak and spread of infectious diseases have been questioned and studied for years. The spread mechanism and prediction about the disease could enable scientists to evaluate isolation plans to have significant effects on a particular epidemic. A differential equation model is proposed to study the dynamics of HIV spread in a network of prisons. In prisons, screening and quarantining are both efficient control manners. An optimization model is proposed to study optimal strategies for the control of HIV spread in a prison system. A primordium (plural: primordia) is an organ or tissue in its earliest recognizable stage of development. Primordial development in plants is critical to the proper positioning and development of plant organs. An optimization model and two control mechanisms are proposed to study the dynamics and robustness of primordial systems. Probabilistic Boolean Networks (PBNs) are mathematical models for studying the switching behavior in genetic regulatory networks. An algorithm is proposed to identify singleton and small attractors in PBNs which correspond to cell types and cell states. The captured problem is NP-hard in general. Our algorithm is theoretically and computationally demonstrated to be much more efficient than the naive algorithm that examines all the possible states. The goal of studying the long-term behavior of a genetic regulatory network is to study the control strategies such that the system can obtain desired properties. A control method is proposed to study multiple external interventions meanwhile minimizing the control cost. Robustness is a paramount property for living organisms. The impact degree is a measure of robustness of a metabolic system against the deletion of single or multiple reaction(s). An algorithm is proposed to study the impact degree in Escherichia coli metabolic system. Moreover, approximation method based on Branching process is proposed for estimating the impact degree of metabolic networks. The effectiveness of our method is assured by testing with real-world Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and Homo Sapiens metabolic systems.<br>published_or_final_version<br>Mathematics<br>Doctoral<br>Doctor of Philosophy

The evolution of a genome is shaped by spatial interactions at multiple scales. At the angstrom level, structural constraints on both RNA molecules and proteins contribute to the evolution of a gene sequence. Such optimized genes are weaved together in a particular order, out of a near-infinite number of combinations, to result in a genome. The fate of a genome is intricately linked to the evolutionary fate of its host organism; in turn, the fate of an organism is governed by where it resides in space. In this dissertation, I investigate how structure shapes the evolution of a gene, genome content, and pathogen populations residing in a diseased human lung. Chapter 1 provides a brief historical overview of population genetics in structured environments. I motivate why it is important to determine the migration rate of new alleles. Chapter 2 investigates how pathogens evolve within the structure of the cystic fibrosis lung. I find that migration rate and mutation rate are on similar timescales. Selection, rather than spatial isolation, maintains diversity within a pathogen population. Chapter 3 presents a new method to probe how codon choice is optimized throughout a gene. I find that codon choice is dictated by preference for particular RNA secondary structures, rather than intrinsic properties of a codon. Chapter 4 describes an ongoing study of how rapidly P. aeruginosa populations evolve in short-term infections. Preliminary results show that gene duplication events can sweep through a population in just 11 days. Chapter 5 introduces ideas for future directions. I pose questions regarding how pathogens evolve molecular mimicry that can trigger autoimmune disease in the human host, and how cancer-inducing inflammation might be detected from mutational signatures in the microbiome.<br>Systems Biology

This dissertation develops algebraic theory for discrete models in systems biology. Many discrete model types can be translated into the framework of polynomial dynamical systems (PDS), that is, time- and state-discrete dynamical systems over a finite field where the transition function for each variable is given as a polynomial. This allows for using a range of theoretical and computational tools from computer algebra, which results in a powerful computational engine for model construction, parameter estimation, and analysis methods. Formal definitions and theorems for PDS and the concept of PDS as models of biological systems are introduced in section 1.3. Constructing a model for given time-course data is a challenging problem. Several methods for reverse-engineering, the process of inferring a model solely based on experimental data, are described briefly in section 1.3. If the underlying dependencies of the model components are known in addition to experimental data, inferring a "good" model amounts to parameter estimation. Chapter 2 describes a parameter estimation algorithm that infers a special class of polynomials, so called nested canalyzing functions. Models consisting of nested canalyzing functions have been shown to exhibit desirable biological properties, namely robustness and stability. The algorithm is based on the parametrization of nested canalyzing functions. To demonstrate the feasibility of the method, it is applied to the cell-cycle network of budding yeast. Several discrete model types, such as Boolean networks, logical models, and bounded Petri nets, can be translated into the framework of PDS. Section 3 describes how to translate agent-based models into polynomial dynamical systems. Chapter 4, 5, and 6 are concerned with analysis of complex models. Section 4 proposes a new method to identify steady states and limit cycles. The method relies on the fact that attractors correspond to the solutions of a system of polynomials over a finite field, a long-studied problem in algebraic geometry which can be efficiently solved by computing Gröbner bases. Section 5 introduces a bit-wise implementation of a Gröbner basis algorithm for Boolean polynomials. This implementation has been incorporated into the core engine of Macaulay2. Chapter 6 discusses bistability for Boolean models formulated as polynomial dynamical systems.<br>Ph. D.

Temperature is arguably the leading factor that drives adaptation of organisms and ecosystems. Remarkably, many sister species share the same habitat because of their different temporal or micro-spatial thermal adaptation. In this PhD, the underlying molecular mechanisms of the adaptation of closely related species to different temperatures are sought. A thermodynamic analysis was applied to a genome-scale metabolic model of S. cerevisiae at warm and cold temperatures to identify thermo-dependent reactions. Gene Ontology (GO) analysis of predicted cold-dependent reactions found that redox reactions were significantly enriched. A complementary large scale experimental approach was taken by competing 6,000 mutant strains at 16°C to identify genes that were responsible for the fitness at low temperatures. The experiment was carried out in three different nutritional conditions to test the plasticity of temperature dependency. A list of strains whose copy number significantly increased or decreased in all media conditions was constructed and analysed using Gene Ontology. Vitamin biosynthesis, lipid/fatty acid processes and oxido-reduction reactions were all found to be significantly affected by the cold condition. Combining the data from the two studies a list of candidate genes affected by temperature changes were generated. In particular, two genes, GUT2 and ADH3, were identified as potential cold favouring genes and studied in more detailed. Mutants for these two genes were created in a pair of natural sympatric cryotolerant and thermotolerant Saccharomyces yeasts, namely S. kudriavzevii CA111 and S. cerevisiae 96.2, representing an excellent ecological experimental model for differential temperature adaption. My results showed that when compared to the parental strains, both mutants showed lower fitness at cold temperatures as predicted, and in S. kudriavzevii CA111 these mutations significantly improve growth at warm temperatures. Results from all aspects of this work indicate that oxidation reduction reactions are important for cold acclimation. It is known that heat stress causes redox imbalances which are compensated by increasing glycerol production or cytosolic acetaldehyde. Since GUT2 and ADH3 are involved in these processes, mutations in these genes may not be able to compensate for temperature changes. My data also shows that vitamins may also play an important role in cold acclimation which would be an interesting line of investigation for future work. Overall this PhD thesis has incorporated in silico and in vivo work to identify potential processes and genes involved in the temperature adaptation of sister Saccharomyces yeast species. The approach and results provided in this study support the use of a systems biology framework to studying species adaptation to environmental changes, and show that such models can yield testable predictions that may lead to new biological discoveries.

A fundamental property of living cells is the ability to sense and respond appropriately to changing environmental conditions. In budding yeast (Sacharomyces cerevisiae), changes in extracellular osmotic conditions are sensed by the HOG SAPK pathway, which orchestrates the cell adaptation program required to maximize cell survival upon stress. Although most of the HOG pathway components have been described, little was known about the dynamics of the response. The aim of this thesis was to analyze the dynamic behavior of the HOG pathway. By using a chemical inhibitor and extensive signal quantification we showed that the HOG pathway is controlled by high basal signaling counteracted by a negative feedback regulatory system. This property determines dynamic signaling in terms of faster response times and higher sensitivity to small variations in extracellular stimuli. This thesis also aimed to implement novel strategies for biological computation that allow increasing complexity of circuits. By engineering signaling pathways in yeast, we have shown that distribution of computation tasks among several wired cells reduces wiring constraints and allows scalability of circuit complexity. Moreover, reusability of cells permits implementation of multiple circuits. Overall, our results define novel dynamic properties of the HOG pathway and have been important to achieve a better view of signal transduction process though MAPK pathways. Moreover, we have developed and implemented novel strategies for biological computation that solved fundamental constrains in the field of synthetic biology.<br>Una propietat cel•lular fonamental és l’habilitat de detectar estímuls i respondre coherentment a un ambient dinàmic. En cèl•lules de llevat (Saccharomyces cerevisiae), els canvis en l’osmolaritat externa són detectats per la via de senyalització de HOG que organitza tot el programa d’adaptació cel•lular, indispensable per assegurar la supervivència cel•lular en estrès osmòtic. Tot i que la gran majoria dels components de la via de HOG han estat identificats, la dinàmica del procés de senyalització és encara força desconeguda. L’objectiu d’aquest projecte de tesis ha estat analitzar el comportament dinàmic de la via de HOG. Gràcies a la utilització d’un al•lel inhibible de la MAPK Hog1 i a la quantificació sistemàtica del procés de senyalització, hem pogut demostrar que en la via de HOG existeix una intensa senyal basal reprimida constantment per un feedback negatiu depenent de la MAPK Hog1. Aquesta tesi també té com a objectiu la implementació de noves estratègies de computació biològica que permetin un increment de la complexitat dels circuits. Gràcies a la bioenginyeria de les vies de senyalització de llevat, hem demostrat que la distribució de la computació en diferents cèl•lules connectades entre elles disminueix les limitacions de connexió i permet incrementar la complexitat dels circuits a un baix cost. En conjunt, els nostres resultats defineixen noves propietats dinàmiques de la via de HOG i han estat importants per tenir una visió global millorada del procés de senyalització per vies de MAPK. A més, hem dissenyat i implementat noves estratègies de computació biològica que han resolt problemes fonamentals del camp de la biologia sintètica.