Dissertations / Theses on the topic 'Systems biology model'
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Coskun, Sarp Arda. "PATHCASE-SB MODEL SIMULATION AND MODEL COMPOSITION TOOLS FOR SYSTEMS BIOLOGY MODELS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1328556115.
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Veliz-Cuba, Alan A. "The Algebra of Systems Biology." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28240.
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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.Ph. D.
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Karlstädt, Anja [Verfasser]. "A systems biology approach to model cardiomyocyte metabolism / Anja Karlstädt." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1043197656/34.
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Konieczka, Jay, Kevin Drew, Alex Pine, Kevin Belasco, Sean Davey, Tatiana Yatskievych, Richard Bonneau, and Parker Antin. "BioNetBuilder2.0: bringing systems biology to chicken and other model organisms." BioMed Central, 2009. http://hdl.handle.net/10150/610006.
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BACKGROUND:Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.RESULTS:Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across speciesthis engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.CONCLUSION:BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/ webcite.
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Avva, Jayant. "Complex Systems Biology of Mammalian Cell Cycle Signaling in Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295625781.
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Gay, Steven. "Subgraph Epimorphisms : Theory and Application to Model Reductions in Systems Biology." Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCC265.
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Cette thèse développe une méthode de morphismes de graphes et l'applique à la réduction de modèles en biologie des systèmes. Nous nous intéressons au problème suivant: l'ensemble des modèles en biologie des systèmes est en expansion, mais aucune relation formelle entre les modèles de cet ensemble n'a été entreprise. Ainsi, la tâche d'organisation des modèles existants, qui est essentielle pour le raffinement et le couplage de modèles, doit être effectuée par le modélisateur. En biomathématiques, les techniques de réduction de modèle sont étudiées depuis longtemps, mais ces techniques sont bien trop restrictives pour être appliquées aux échelles requises en biologie des systèmes. Nous proposons un cadre de réduction de modèle, basé uniquement sur des graphes, qui permet d'organiser les modèles en un ordre partiel. Les modèles de biologie des systèmes seront représentés par leur graphe de réaction. Pour capturer le processus de réduction lui-même, nous étudierons un type particulier de morphismes de graphes : les épimorphismes de sous-graphe, qui permettent la fusion et l'effacement de sommets. Nous commencerons en analysant l'ordre partiel qui émerge des opérations de fusion et d'effacement, puis nous développerons des outils théoriques pour résoudre les problèmes calculatoires de notre méthode, et pour finir nous montrerons la faisabilité de l'approche et la précision du cadre "graphes de réactions/épimorphismes de sous-graphe", en utilisant un dépôt de modèles de biologie des systèmesThis thesis develops a framework of graph morphisms and applies it to model reduction in systems biology. We are interested in the following problem: the collection of systems biology models is growing, but there is no formai relation between models in this collection. Thus, the task of organizing the existing models, essential for model refinement and coupling, is left to the modeler. In mathematical biology, model reduction techniques have been studied for a long time, however these techniques are far too restrictive to be applied on the scales required by systems biology. We propose a model reduction framework based solely on graphs, allowing to organize models in a partial order. Systems biology models will be represented by their reaction graphs. To capture the process of reduction itself, we study a particular kind of graph morphisms: subgraph epimorphisms, which allow both vertex merging and deletion. We first analyze the partial order emerging from the merge/delete graph operations, then develop tools to solve computational problems raised by this framework, and finally show both the computational feasibility of the approach and the accuracy of the reaction graphs/subgraph epimorphisms framework on a large repository of systems biology models
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Prescott, Thomas Paul. "Large-scale layered systems and synthetic biology : model reduction and decomposition." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:205a18fb-b21f-4148-ba7d-3238f4b1f25b.
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This thesis is concerned with large-scale systems of Ordinary Differential Equations that model Biomolecular Reaction Networks (BRNs) in Systems and Synthetic Biology. It addresses the strategies of model reduction and decomposition used to overcome the challenges posed by the high dimension and stiffness typical of these models. A number of developments of these strategies are identified, and their implementation on various BRN models is demonstrated. The goal of model reduction is to construct a simplified ODE system to closely approximate a large-scale system. The error estimation problem seeks to quantify the approximation error; this is an example of the trajectory comparison problem. The first part of this thesis applies semi-definite programming (SDP) and dissipativity theory to this problem, producing a single a priori upper bound on the difference between two models in the presence of parameter uncertainty and for a range of initial conditions, for which exhaustive simulation is impractical. The second part of this thesis is concerned with the BRN decomposition problem of expressing a network as an interconnection of subnetworks. A novel framework, called layered decomposition, is introduced and compared with established modular techniques. Fundamental properties of layered decompositions are investigated, providing basic criteria for choosing an appropriate layered decomposition. Further aspects of the layering framework are considered: we illustrate the relationship between decomposition and scale separation by constructing singularly perturbed BRN models using layered decomposition; and we reveal the inter-layer signal propagation structure by decomposing the steady state response to parametric perturbations. Finally, we consider the large-scale SDP problem, where large scale SDP techniques fail to certify a system’s dissipativity. We describe the framework of Structured Storage Functions (SSF), defined where systems admit a cascaded decomposition, and demonstrate a significant resulting speed-up of large-scale dissipativity problems, with applications to the trajectory comparison technique discussed above.
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Feist, Adam Michael. "Model-driven metabolic engineering of Escherichia coli a systems biology approach /." Diss., [La Jolla] : University of California, San Diego, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3354731.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed June 2, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Vesty, Eleanor Fay. "Understanding developmental processes in early-diverging plant model systems." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7498/.
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The study of evolutionary developmental biology relies on a detailed understanding of model systems. Whilst the flowering plants are the most successful and valuable plant group today, they don’t tell us much about the change and progression that was initiated by an ancestral aquatic photosynthetic unicell millions of years ago. The expansion of bryophyte and algal model systems was developed as part of this research The moss \(Physcomitrella\) \( patens\) is descended from the ancestral bryophytes that first colonised land. As such it is well-placed, as a model organism, to provide insight into terrestrialisation. The germination of spores or seeds is one of the key stages in the land plant life cycle. Comparison of the influences on spore and seed germination provides insight into the conservation of functions spanning 450 million years of evolution. The role of phytohormones in the control of spore germination was assessed by analysing the response of \(P. patens\) spores to different exogenously applied hormones. Endogenous roles were explored using hormone biosynthesis mutants and semi-quantitative analysis of signalling genes. This research shows that \(P. patens\) spore germination is regulated by some of the same hormones that regulate seed germination. The extent of regulation varies between hormone types but this has demonstrated previously unknown characteristics of the \(P. patens\) hormone signalling network. This work also highlights the importance of establishing tractable model systems with robust methodological procedures.
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Ghosh, Krishnendu. "Formal Analysis of Automated Model Abstractions under Uncertainty: Applications in Systems Biology." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1330024977.
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Toni, Tina. "Approximate Bayesian computation for parameter inference and model selection in systems biology." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/11481.
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In this thesis we present a novel algorithm for parameter estimation and model selection of dynamical systems. The algorithm belongs to the class of approximate Bayesian computation (ABC) methods, which can evaluate posterior distributions without having to calculate likelihoods. It is based on a sequential Monte Carlo framework, which gives our method a computational advantage over other existing ABC methods. The algorithm is applied to a wide variety of biological systems such as prokaryotic and eukaryotic signalling and stress response pathways, gene regulatory networks, and infectious diseases. We illustrate its applicability to deterministic and stochastic models, and draw inferences from different data frameworks. Posterior parameter distributions are analysed in order to gain further insight into parameter sensitivity and sloppiness. The comprehensive analysis provided in this thesis illustrates the flexibility of our new ABC SMC approach. The algorithm has proven useful for efficient parameter inference, systematic model selection and inference-based modelling, and is a novel and useful addition to the systems biology toolbox.
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Chen, Daphne Wei-chen. "Integrative modelling of glucocorticoid induced apoptosis with a systems biology approach." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/integrative-modelling-of-glucocorticoid-induced-apoptosis-with-a-systems-biology-approach(a05039a1-f3e3-44d2-959e-0fade52a28a1).html.
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Glucocorticoids (GCs) have an important role in anti-inflammation, apoptosis and immunomodulatory activity. GCs exert their effect by binding to their receptor, glucocorticoid receptor (GR), which subsequently triggers receptor dimerisation, nuclear translocation and eventually causes impact on transcriptional activity. Such regulatory mechanism is complex as it is not only controlled at the transcription level, but also at the post translational level with other contributing factors such as protein stability and cofactor recruitment. Glucocorticoids are commonly used as part of the chemotherapeutical protocols for lymphoid malignancies and have been successfully implicated in treating childhood acute lymphoblastic leukaemia (ALL). Nevertheless, resistance and side effects such as muscle atrophy and osteoporosis still occur frequently.With the advance in high-throughput technology, vast amount of data on various scales, including genomics, proteomics, and metabolomics make the molecular study of cancer more complicated. The rise of systems biology helps the scientist to address this problem with the use of computation. Although the concept and the approach may vary depending on the research fields, the ultimate goal remains the same which is to create a comprehensive understanding of biological processes and to forecast outcome.The goal of this body of work is to better understand glucocorticoid induced apoptosis in acute lymphoblastic leukaemia by adopting a systems biology approach. As the Bcl-2 family, particularly Bim is known to be a key determinant of GC-induced apoptosis, we investigated the molecular mechanism of GC induction of Bim. By adopting ordinary differential equation modelling approach, we were able to make prediction and investigate details of Bim regulation by GCs. Further to this, we carried out an integrated microarray analysis in various ALL to study GC resistance and identified crucial candidate gene c-Jun as a regulator of Bim and Erg as a determinant for GC resistance. These results allowed us to refine our models and enabled more answers to be addressed. In conclusion, our findings not only suggest potential regulatory mechanisms for determining GC sensitivity, they also aid us to find potential biomarkers for determining GC resistance. More importantly, this study represents a successful example for utilising systems biology to study the genetic complexity in cancer.
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Arat, Seda. "A Mathematical Model of a Denitrification Metabolic Network in Pseudomonas aeruginosa." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/46208.
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Lake Erie, one of the Great Lakes in North America, has witnessed recurrent summertime low oxygen dead zones for decades. This is a yearly phenomenon that causes microbial production of the greenhouse gas nitrous oxide from denitrification. Complete denitrification is a microbial process of reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and greenhouse gas nitrous oxide. After scanning the microbial community in Lake Erie, Pseudomonas aeruginosa is decided to be examined, not because it is abundant in Lake Erie, but because it can perform denitrification under anaerobic conditions. This study focuses on a mathematical model of the metabolic network in Pseudomonas aeruginosa under denitrification and testable hypotheses generation using polynomial dynamical systems and stochastic discrete dynamical systems. Analysis of the long-term behavior of the system changing the concentration level of oxygen, nitrate, and phosphate suggests that phosphate highly affects the denitrification performance of the network.Master of Science
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Eydgahi, Hoda. "A quantitative framework For large-scale model estimation and discrimination In systems biology." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82347.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 103-111).
Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but co-variation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g. by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (~20-fold) for competing "direct" and "indirect" apoptosis models having different numbers of parameters. The methods presented in this thesis were then extended to make predictions in eight apoptosis mini-models. Despite topological uncertainty, the simulated predictions can be used to drive experimental design. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminating between competing hypotheses in the face of parametric and topological uncertainty.
by Hoda Eydgahi.
Ph.D.
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Gómez, Uribe Carlos Alberto. "Systems of chemical reactions in biology : dynamics, stochasticity, spatial effects and model reduction." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43803.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 221-232).
Cells are continuously sensing and processing information from their environments and responding to it in sensible ways. The communication networks on which such information is handled often consist of systems of chemical reactions, such as signaling pathways or metabolic networks. This thesis studies the dynamics of systems of chemical reactions in the context of biological cells. The first part of this thesis analyzes the osmo-regulation network in yeast, responsible for the regulation of internal osmolarity. We measure the system's step response in single cells, and find that the steady state is independent of the input, a property termed perfect adaptation that relies on integral feedback control. We then consider the signaling cycle, a pattern of chemical reactions that is often present in signaling pathways, in which a protein can be either active (e.g., phosphorylated) or inactive (e.g., unphosphorylated). We identify new regimes of static and dynamic operation, and find that these cycles can be tuned to transmit or digitize time-varying signals, while filtering input noise. The second part of this thesis considers systems of chemical reactions where stochastic effects are relevant, and simplifies the standard models. We develop an approximate model for the time-evolution of the average concentrations and their variances and covariances in systems with and without spatial gradients. We also describe a framework to identify and derive approximate models for variables that evolve at different time scales in systems without spatial gradients. These tools can help study the impact of stochastic and spatial effects on system behavior.
by Carlos Alberto Gómez Uribe.
Ph.D.
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Urquiza, García José María Uriel. "Mathematical model in absolute units for the Arabidopsis circadian oscillator." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31132.
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The Earth’s oblique rotation results in changes in light and temperature across the day and time of year. Living organisms evolved rhythmic behaviours to anticipate these changes and execute appropriate responses at particular times. The current paradigm for the biological clocks in several branches of life is an underlying biochemical oscillator mainly composed by a network of repressive transcription factors. The slow decay in their activity is fundamental for generating anticipatory dynamics. Interestingly, these dynamics can be well appreciated when the biological system is left under constant environmental conditions, where oscillation of several physiological readouts persists with a period close to 24 hours, hence the term “circadian clocks”, circa=around dian=day. In plants the model species Arabidopsis thaliana has served as an invaluable tool for analysing the genetics, biochemical, developmental, and physiological effects of the oscillator. Many of these experimental results have been integrated in mechanistic and mathematical theories for the circadian oscillator. These models predict the timing of gene expression and protein presence in several genetic backgrounds and photoperiodic conditions. The aim of this work is the introduction of a correct mass scale for both the RNA transcript and protein variables of the clock models. The new mass scale is first introduced using published RNA data in absolute units, from qRT-PCR. This required reinterpreting several assumptions of an established clock model (P2011), resulting in an updated version named U2017. I evaluate the performance of the U2017 model in using data in absolute mass units, for the first time for this clock system. Introducing absolute units for the protein variables takes place by generating hypothetical protein data from the existing qRT-PCR data and comparing a data-driven model with western blot data from the literature. I explore the consequences of these predicted protein numbers for the model’s dynamics. The process required a meta-analysis of plant parameter values and genomic information, to interpret the biological relevance of the updated protein parameters. The predicted protein amounts justify, for example, the revised treatment of the Evening Complex in the U2017 model, compared to P2011. The difficulties of introducing absolute units for the protein components are discussed and components for experimental quantification are proposed. Validating the protein predictions required a new methodology for absolute quantification. The methodology is based on translational fusions with a luciferase reporter than has been little used in plants, NanoLUC. Firstly, the characterisation of NanoLUC as a new circadian reporter was explored using the clock gene BOA. The results show that this new system is a robust, sensitive and automatable approach for addressing quantitative biology questions. I selected five clock proteins CCA1, LHY, PRR7, TOC1 and LUX for absolute quantification using the new NanoLUC methodology. Functionality of translation fusions with NanoLUC was assessed by complementation experiments. The closest complementing line for each gene was selected to generate protein time series data. Absolute protein quantities were determined by generation of calibration curves using a recombinant NanoLUC standard. The developed methodology allows absolute quantification comparable to the calibrated qRT-PCR data. These experimental results test the predicted protein amounts and represent a technical resource to understand protein dynamics of Arabidopsis’ circadian oscillator quantitatively. The new experimental, meta-analysis and modelling results in absolute units allows future researchers to incorporate further, quantitative biochemical data.
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Batenchuk, Cory. "Development of a Mathematical Model to Understand, Design & Improve Oncolytic Virus Therapies." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31182.
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Oncolytic viruses (OVs) are emerging as a potent therapeutic platform for the treatment of malignant disease. The tumor cells inability to induce antiviral defences in response to a small cytokine known as interferon (IFN) is a common defect exploited by OVs. Heterogeneity in IFN signalling across tumors is therefore a pillar element of resistance to these therapies. I have generated a mathematical model and simulation platform to study the impact of IFN on OV dynamics in normal and cancerous tissues. In the first part of my thesis, I used this model to identify novel OV engineering strategies which could be implemented to overcome IFN based resistance in tumor tissues. From these simulations, it appears that a positive feedback loop, established by virus-mediated expression of an interferon-binding decoy receptor, could increase tumor cytotoxicity without compromising normal cells. The predictions set forth by this model have been validated both qualitatively and quantitatively in in-vitro and in-vivo models using two independent OV strains. This model has subsequently been used to investigate OV attenuation mechanisms, the impact of tumor cell heterogeneity, as well as drug-OV interactions. Following these results, it became apparent that selectivity should equally be observed when overwhelming the cell with a non replicating virus. While normal tissues will clear this pseudo-infection rapidly, owing to their high baseline in antiviral products at the onset of infection, tumor cells with defective anti-viral pathways should not have readily available biomachinery required to degrade this pro-apoptotic signal. Recapitulated by the mathematical model, non-replicating virus-derived particles generated by means of UV irradiation selectively kill tumor cells in cultured cell lines and patient samples, leading to long term cures in murine models. Taken together, this thesis uses a novel mathematical model and simulation platform to understand, design & improve oncolytic virus-based therapeutics.
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Ovidiu, Parvu. "Computational model validation using a novel multiscale multidimensional spatio-temporal meta model checking approach." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/11863.
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Computational models of complex biological systems can provide a better understanding of how living systems function but need to be validated before they are employed for real-life (e.g. clinical) applications. One of the most frequently employed in silico approaches for validating such models is model checking. Traditional model checking approaches are limited to uniscale non-spatial computational models because they do not explicitly distinguish between different scales, and do not take properties of (emergent) spatial structures (e.g. density of multicellular population) into account. This thesis defines a novel multiscale multidimensional spatio-temporal meta model checking methodology which enables validating multiscale (spatial) computational models of biological systems relative to how both numeric (e.g. concentrations) and spatial system properties are expected to change over time and across multiple scales. The methodology has two important advantages. First it supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to produce them. Secondly the methodology is generic because it can be automatically reconfigured according to case study specific types of spatial structures and properties using the meta model checking approach. In addition the methodology could be employed for multiple domains of science, but we illustrate its applicability here only against biological case studies. To automate the computational model validation process, the approach was implemented in software tools, which are made freely available online. Their efficacy is illustrated against two uniscale and four multiscale quantitative computational models encoding phase variation in bacterial colonies and the chemotactic aggregation of cells, respectively the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. This novel model checking approach will enable the efficient construction of reliable multiscale computational models of complex systems.
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Aisenberg, Jeremy Charles. "A Critical Review of Telomerase Biology and Model Systems for the Study of Telomerase." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/2120.
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Jones, Thomas Carroll Jr. "JigCell Model Connector: Building Large Molecular Network Models from Components." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78277.
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The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist.Master of Science
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Gowen, Christopher. "Model-Guided Systems Metabolic Engineering of Clostridium thermocellum." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2529.
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Metabolic engineering of microorganisms for chemical production involves the coordination of regulatory, kinetic, and thermodynamic parameters within the context of the entire network, as well as the careful allocation of energetic and structural resources such as ATP, redox potential, and amino acids. The exponential progression of “omics” technologies over the past few decades has transformed our ability to understand these network interactions by generating enormous amounts of data about cell behavior. The great challenge of the new biological era is in processing, integrating, and rationally interpreting all of this information, leading to testable hypotheses. In silico metabolic reconstructions are versatile computational tools for integrating multiple levels of bioinformatics data, facilitating interpretation of that data, and making functional predictions related to the metabolic behavior of the cell. To explore the use of this modeling paradigm as a tool for enabling metabolic engineering in a poorly understood microorganism, an in silico constraint-based metabolic reconstruction for the anaerobic, cellulolytic bacterium Clostridium thermocellum was constructed based on available genome annotations, published phenotypic information, and specific biochemical assays. This dissertation describes the analysis and experimental validation of this model, the integration of transcriptomic data from an RNAseq experiment, and the use of the resulting model for generating novel strain designs for significantly improved production of ethanol from cellulosic biomass. The genome-scale metabolic reconstruction is shown to be a powerful framework for understanding and predicting various metabolic phenotypes, and contributions described here enhance the utility of these models for interpretation of experimental datasets for successful metabolic engineering.
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Perron, Amélie. "NTS2 neurotensin receptors : distribution, interactions, and cellular dynamics in model systems and rat brain." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102692.
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Although available evidence suggests that NTS1 receptors play a key role in the transduction of many of the central effects of neurotensin (NT), recent data indicate that the NTS2 subtype may be responsible for mediating NT-induced analgesia. However, little is known about the cellular and molecular mechanisms underlying NTS2 function. It is also unclear whether endogenous NT is an agonist at the NTS2 site and to which second messenger system(s) this receptor is coupled since conflicting data has emerged from studies carried out in systems heterologously expressing the NTS2 receptor. In this context, the overall objective of the present study was to characterize the distribution of the NTS2 protein in rat brain, to further document the role played by NTS2 receptors in antinociception, and to unravel the cellular mechanisms underlying NTS2-mediated transduction of NT's effects. Our immunohistochemical studies revealed that NTS2 receptors are widely expressed within the rat CNS with high densities of labeled neurons and/or processes in descending antinociceptive pathways including the periaqueductal gray (PAG), and the rostroventral medial medulla. Both the full-length and alternatively-spliced isoforms induced a rapid and sustained activation of the mitogen-activated protein kinase (ERK1/2) pathway in both transfected CHO and COS-7 cells upon stimulation with NT. Combined biochemical and confocal microscopic studies revealed that NTS2 receptors were maintained at the cell surface following long-term agonist exposure despite efficient internalization mechanisms. This preservation was neither due to NTS2 neosynthesis nor recycling but rather appeared to involve translocation of spare receptors from intracellular stores as observed in both transfected cells and rat spinal cord neurons. NTS2 was demonstrated to heterodimerize with NTS1 in cells co-transfected with the two receptors. This heterodimerization was found to affect cell surface recruitment of NTS1, making it more similar to that of NTS2. Also, upon prolonged NT stimulation, cell surface NTS1 were more resistant to down-regulation in cells co-expressing NTS1 and NTS2 than in cells expressing NTS1 alone. Such maintenance of NT receptors bioavailability might prove of great importance for the use of NTS2-selective agonists for the treatment of chronic pain.
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Harland, Christopher William 1983. "Desiccation resistance and viscoelasticity in model membrane systems." Thesis, University of Oregon, 2010. http://hdl.handle.net/1794/10993.
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xii, 89 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.Lipid membranes are a basic structural element of all cells. They provide a framework for the physical organization of the cell, act as a scaffold for numerous proteins, and serve as the host site for countless chemical reactions integral to cell function. Several key problems in membrane biophysics hinge on reliable methods for measuring membrane material properties. Properties such as rigidity, fluidity, charge density, etc., are important factors that govern membrane structure and function. As such, we need controllable, reliable, and quantitative methods of probing membrane material properties. In pursuit of such methods, we completed two related projects that, while distinct, aimed to create and apply quantitative measures of membrane material properties to current problems in biophysics. The first of these two lines of inquiry centered on the pervasive, pathogenic family of mycobacteria that is known to not only cause several diseases but also to survive prolonged periods of dehydration. We developed an experimental model system that mimics the structure of the mycobacterial envelope consisting of an immobile hydrophobic layer supporting a two-dimensionally fluid, glycolipid-rich outer monolayer. With this system, we show that glycolipid containing monolayers, in great contrast to phospholipid monolayers, survive desiccation with no loss of integrity, as assessed by both fluidity and protein binding, revealing a possible cause of mycobacterial persistence. In the second line of inquiry, we developed another general platform for probing membrane material properties that has produced the first reported observations of viscoelasticity in lipid membranes. We utilized recently developed microrheological techniques on freestanding lipid bilayer systems using high speed video particle tracking. The complex shear modulus of the bilayers was extracted at a variety of temperatures that span the liquid-ordered to disordered phase transition of the membranes. At many temperatures measured, the membranes displayed viscoelastic behavior reminiscent of a Maxwell material, namely elastic at high frequencies and viscous at low frequencies. Moreover, the viscoelastic behavior was suppressed at the critical phase transition temperature where the membranes behave as a purely viscous fluid. Surprisingly, the viscoelastic behavior was found in all of several distinct membrane compositions that were examined.
Committee in charge: Dr. Daniel Steck, Chair; Dr. Raghuveer Parthasarathy, Research Advisor; Dr. Darren Johnson; Dr. Heiner Linke; Dr. John Toner
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Wilcock, Paul. "A systems biology approach for investigating oral squamous cell carcinoma (OSCC)." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-systems-biology-approach-for-investigating-oral-squamous-cell-carcinoma-oscc(8ec3728b-1928-450f-b467-76996fa970fb).html.
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A systems biology approach was adopted in order to assess various aspects of the disease oral squamous cell carcinoma. Three main aims were addressed; assess the ability of CoCl2 to mimic the hypoxic response in a eukaryotic cell line, assess the role of PDE4D in oral squamous cell carcinoma (OSCC) and the construction of a normoxic/hypoxic mathematical model to identify therapeutic targets.Cancer cells often acquire a revised metabolism which aids in initiation, survival and progression of the tumour. This is predominantly due to the transcription factor HIF-1 which is activated under hypoxic conditions. Certain compounds such as cobalt chloride (CoCl2) have been used extensively to inhibit the degradation of HIF-1α and simulate hypoxia. CoCl2 is likely to have off-target effects on metabolism; these effects were examined when exposing human telomerase reverse transcriptase (hTERT) cells to 100μM CoCl2. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) based metabolomics were utilised in combination with ELISA assays for HIF-1α and ATP. Central metabolism was accurately mimicked when hTERT cells were subjected to 100μM CoCl2, however; it was apparent that this concentration of CoCl2 does not induce an equal extent of hypoxia as 1% oxygen. A number of off-target effects of CoCl2 were observed in secondary metabolism, specifically in lipids and fatty acids. In conclusion, CoCl2 should be used with caution as a hypoxic mimicker with the caveat that interpretation of results should be restricted to its effects on central metabolism.The transcription factor CREB has the ability to regulate approximately 4000 genes, a number of which are associated with cancer initiation and progression. Cyclic adenosine monophosphate (cAMP) is required to activate CREB and is partially regulated through its degradation via the enzyme phosphodiesterase type 4D (PDE4D). A homozygous deletion of PDE4D has been associated with OSCC; however; the exact consequence of this deletion has not been fully elucidated. PDE4D was knocked down in the OSCC cell line BicR16 and cellular proliferation, migration, resistance to ionising radiation and central metabolism was investigated using MTT, scratch, clonogenic and GC-MS, respectively. The knockdown resulted in an increase in proliferation, migration and radiation resistance suggesting the role of PDE4D as a TSG. Amino acids, cholesterol, fatty acids, carbohydrates and TCA intermediates were found to be altered in concentration.A mathematical model of glycolysis, TCA and glutaminolysis under normoxia and hypoxia was constructed through the amalgamation of two established models from the literature. New reactions, parameters and metabolite concentrations were added and unnecessary entities were deleted. COmplex PAthway SImulator (COPASI) was utilised to construct the model before validating the model using experimental data from the literature and steady state and flux analyses. Sensitivity analysis and a reduction in external glucose and glutamine were mimicked and the alterations in hypoxic and normoxic metabolism analysed. The reactions vCSII, vGS, vPGK and vGII were identified as potential therapeutic targets which may affect metabolism in hypoxia only. However, certain validation methods proved unsuccessful and hence the model requires further work before attempting the analyses again.
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Quo, Chang Feng. "Reverse engineering homeostasis in molecular biological systems." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49144.
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This dissertation is an initial study of how modern engineering control may be applied to reverse engineer homeostasis in metabolic pathways using high-throughput biological data. This attempt to reconcile differences between engineering control and biological homeostasis from an interdisciplinary perspective is motivated not only by the observation that robust behavior in metabolic pathways resembles stabilized dynamics in controlled systems, but also by the challenges forewarned in achieving a true meeting of minds between engineers and biologists. To do this, a comparator model is developed and applied to model the effect of single-gene (SPT) overexpression on C16:0 sphingolipid de novo biosynthesis in vitro, specifically to simulate and predict potential homeostatic pathway interactions between the sphingolipid metabolites. Sphingolipid de novo biosynthesis is highly regulated because its pathway intermediates are highly bioactive. Alterations in sphingolipid synthesis, storage, and metabolism are implicated in human diseases. In addition, when variation in structure is considered, sphingolipids are one of the most diverse and complex families of biomolecules. To complete the modeling paradigm, wild type cells are defi ned as the reference that exhibits the "desired" pathway dynamics that the treated cells approach. Key model results show that the proposed modern engineering control approach using a comparator to reverse engineer homeostasis in metabolic systems is: (a) eff ective in capturing observed pathway dynamics from experimental data, with no signifi cant di fference in precision from existing models, (b) robust to potential errors in estimating state-space parameters as a result of sparse data, (c) generalizable to model other metabolic systems, as demonstrated by testing on a separate independent dataset, and (d) biologically relevant in terms of predicting steady-state feedback as a result of homeostasis that is verifi ed in literature and with additional independent data from drug dosage experiments.
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Smith, Aaron. "Vertex model approaches to epithelial tissues in developmental systems." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:4d19f232-764c-4e27-bca9-d2ede0ec2db9.
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The purpose of this thesis is to develop a vertex model framework that can be used to perform computational experiments related to the dynamics of epithelial tissues in developmental systems. We focus on three example systems: the Drosophila wing imaginal disc, the Drosophila epidermis and the visceral endoderm of the mouse embryo. Within these systems, key questions pertaining to size-control mechanisms and coordination of cell migration remain unanswered and are amenable to computational testing. The vertex model presented here builds upon existing frameworks in three key ways. Firstly, we include novel force terms, representing, for example, the reaction of a cell to being compressed and its shape becoming distorted during a highly dynamic process such as cell migration. Secondly, we incorporate a model of diffusing morphogenetic growth factors within the vertex framework, using an arbitrary Lagrangian-Eulerian formulation of the diffusion equation and solving with the finite-element method (FEM). Finally, we implement the vertex model on the surface of an ellipsoid, in order to simulate cell migration in the mouse embryo. Throughout this thesis, we validate our model by running simple simulations. We demonstrate convergence properties of the FEM scheme and discuss how the time taken to solve the system scales with tissue size. The model is applied to biological systems and its utility demonstrated in several contexts. We show that when growth is dependent on morphogen concentration in the Drosophila wing disc, proliferation occurs preferentially in regions of high concentration. In the Drosophila epidermis, we show that a recently proposed mechanism of compartment size-control, in which a growth-factor is released in limited amounts, is viable. Finally, we examine the phenomenon of rosettes in the mouse embryo, which occur when five or more cells meet at a common vertex. We show, by running simulations both with and without rosettes, that they are crucial facilitators of ordered migration, and are thus critical in the patterning of the early embryo.
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Daruwalla, Anahita. "Understanding Carotenoid and Retinoid Biochemical Diversity using Novel Archaeal and Eukaryotic Model Systems." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1626709424672807.
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Marín, de Mas Igor Bartolomé. "Development and application of novel model-driven and data-driven approaches to study metabolism in the framework of systems medicine." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/296313.
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The general aim of this thesis is to develop and apply new computational tools to overcome existing limitations in the analysis of metabolism. This thesis is focused on developing new computational strategies to overcome the following identified limitations:
i) The existing metabolic flux analysis tools does not account for the existence of metabolic channeling: Here we developed a new computational tool based on non-stationary 13 C-FBA to evaluate different models reflecting different topologies of intracellular metabolism, using the channeling in hepatocytes as case of concep
ii) Metabolic drug-target discovery based on GSMM does not consider the different cell subpopulations existing within the tumor: Here we develope a method that integrate trancriptomic data into a comparative genome-scale metabolic network reconstruction analysis in the context of intra-tumoral heterogeneity . We determined subpopulation-specific drug targets . Additionally we determined a metabolic gene signature associated to tumor progression in pc that was correlated with other types of cancer.
Iii) Current mechanistic and probabilistic computational approaches are not suitable to study the complexity of the crosstalk between metabolic and gene regulatory networks.: Here we developed a novel computational method combining probabilistic and mechanistic approaches to integrate multi-level omic data into a discrete model-based analysis. This method allowed to analyze the mechanism underlying the crosstalk between metabolism and gene regulation, using as case of concept the study of the abnormal adaptation to training in COPD patients.La presente tesis doctoral se centra en el desarrollo de herramientas computacionales que permitan el estudio de los mecanismos moleculares que ocurren dentro de la célula. Mas específicamente estudia el metabolismo celular desde diferentes puntos de vista usando y desarrollando métodos computacionales basados en diversas metodologías. Así pues en un primer capitulo se desarrolla un método basado en el analista de los flujos metabólicos en estado no estacional isotópico utilizando modelos cinéticos para estudiar el fenómeno de la canalización metabólica en hepatocitos. Este fenómeno modifica la topología metabólica alterando el fenotipo. Nuestro método nos permitió discriminar varios modelos con distintas topología prediciendo la existencia de canalización metabólica en la glucólisis. En el segundo capitulo se desarrolló un método para analizar el metabolismo tumoral teniendo en cuenta la heterogeneidad de poblaciones. En concreto estudiamos dos subpoblaciones extraídas de una linea celular de cáncer de próstata. Para ello utilizamos un modelo a gran escala de todo el metabolismo celular humano. El análisis reflejó la existencia de diferencias notables a nivel de vías metabólicas concretas, confiriendo a cada subpoblacion sensibilidades distintas a diferentes fármacos. En esta linea se demostró que mientras las células PC-3M eran sensibles al etomoxir e insensibles al calcitriol, las PC-3S presentaban una sensibilidad opuesta. En el tercero y ultimo capitulo de la tesis desarrollamos un nuevo método computacional que integra aproximaciones probabilísticas y mecanicistas para integrar diferentes tipos de datos en un análisis basado en modelos discretos. Para ello utilizamos como caso de concepto el estudio de la adaptación anómala al entrenamiento de pacientes con EPOC. El análisis reveló diferencias importantes a nivel de metabolismo energético en comparación con el grupo control.
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Johansson, Rikard. "Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge." Doctoral thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141614.
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The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model. Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data. In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination. In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques.Användandet av matematiska verktyg har inom biologi och medicin traditionellt sett varit mindre utbredd jämfört med andra ämnen inom naturvetenskapen, såsom fysik och kemi. Ett ökat behov av verktyg som databehandling, bioinformatik, statistik och matematisk modellering har trätt fram tack vare framsteg under de senaste decennierna. Dessa framsteg är delvis ett resultat av utvecklingen av storskaliga datainsamlingstekniker. Inom alla områden av biologi och medicin så har dessa data avslöjat en hög nivå av interkonnektivitet mellan komponenter, verksamma på många kontrollnivåer och med flera återkopplingar både mellan och inom varje nivå av kontroll. Tillgång till storskaliga data är emellertid inte synonymt med en detaljerad mekanistisk förståelse för det underliggande systemet. Snarare uppnås en mekanisk förståelse först när vi bygger en hypotes vars prediktioner vi kan testa experimentellt. Att identifiera intressanta prediktioner som är av kvantitativ natur, kräver generellt sett matematisk modellering. Detta kräver i sin tur att det studerade systemet kan formuleras till en matematisk modell, såsom en serie ordinära differentialekvationer, där olika hypoteser kan uttryckas som precisa matematiska uttryck som påverkar modellens output. Inom vissa delområden av biologin har utnyttjandet av matematiska modeller haft en lång tradition, såsom den modellering gjord inom elektrofysiologi av Hodgkin och Huxley på 1950‑talet. Det är emellertid just på senare år, med ankomsten av fältet systembiologi, som matematisk modellering har blivit ett vanligt inslag. Den något långsamma adapteringen av matematisk modellering inom biologi är bl.a. grundad i historiska skillnader i träning och terminologi, samt brist på medvetenhet om exempel som illustrerar hur modellering kan göra skillnad och faktiskt ofta är ett krav för en korrekt analys av experimentella data. I detta arbete tillhandahåller jag sådana exempel och demonstrerar den matematiska modelleringens och hypotestestningens allmängiltighet och tillämpbarhet i tre olika biologiska system. I Arbete II visar vi hur matematisk modellering är nödvändig för en korrekt tolkning och analys av dominant-negativ-inhiberingsdata vid insulinsignalering i primära humana adipocyter. I Arbete III använder vi modellering för att bestämma transporthastigheter över cellkärnmembranet i jästceller, och vi visar hur denna teknik är överlägsen traditionella kurvpassningsmetoder. Vi demonstrerar också frågan om populationsheterogenitet och behovet av att ta hänsyn till individuella skillnader mellan celler och befolkningen som helhet. I Arbete IV använder vi matematisk modellering för att förkasta tre hypoteser om hur fenomenet facilitering uppstår i pyramidala nervceller hos råttor och möss. Vi visar också hur en överlevande hypotes kan beskriva all data, inklusive oberoende valideringsdata. Slutligen utvecklar vi i Arbete I en metod för modellselektion och modelldiskriminering med hjälp av parametrisk ”bootstrapping” samt kombinationen av olika empiriska fördelningar av traditionella statistiska tester. Vi visar hur det empiriska ”log-likelihood-ratio-testet” är den bästa kombinationen av två tester och hur testet är applicerbart, inte bara för modellselektion, utan också för modelldiskriminering. Sammanfattningsvis är matematisk modellering ett värdefullt verktyg för att analysera data och testa biologiska hypoteser, oavsett underliggande biologiskt system. Vidare utveckling av modelleringsmetoder och tillämpningar är därför viktigt eftersom dessa sannolikt kommer att spela en avgörande roll i framtiden för biologi och medicin, särskilt när det gäller att hantera belastningen från ökande datamängder som blir tillgänglig med nya experimentella tekniker.
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Gauges, Ralph [Verfasser], and Ursula [Akademischer Betreuer] Kummer. "Standards and Tools for Model Exchange and Analysis in Systems Biology / Ralph Gauges ; Betreuer: Ursula Kummer." Heidelberg : Universitätsbibliothek Heidelberg, 2011. http://d-nb.info/1179229126/34.
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Narayan, Mahesh. "Functional characterization of (beta)-lactoglobulin; studies of free radical and reactive oxygen species in model and biological systems /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487943341529796.
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Hengenius, James B. "Quantitative modeling of spatiotemporal systems| Simulation of biological systems and analysis of error metric effects on model fitting." Thesis, Purdue University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687049.
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Understanding the biophysical processes underlying biological and biotechnological processes is a prerequisite for therapeutic treatments and technological innovation. With the exponential growth of computational processing speed, experimental findings in these fields have been complemented by dynamic simulations of developmental signaling and genetic interactions. Models provide means to evaluate "emergent" properties of systems sometimes inaccessible by reductionist approaches, making them test beds for biological inference and technological refinement.
The complexity and interconnectedness of biological processes pose special challenges to modelers; biological models typically possess a large number of unknown parameters relative to their counterparts in other physical sciences. Estimating these parameter values requires iterative testing of parameter values to find values that produce low error between model and data. This is a task whose length grows exponentially with the number of unknown parameters. Many biological systems require spatial representation (i.e., they are not well-mixed systems and change over space and time). Adding spatial dimensions complicates parameter estimation by increasing computational time for each model evaluation. Defining error for model-data comparison is also complicated on spatial domains. Different metrics compare different features of data and simulation, and the desired features are dependent on the underlying research question.
This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose biosensor; the model was used to optimize the sensor's design for improved sensitivity to glucose. The latter is a 3D model of the developmental gap gene system that helps establish the bodyplan of Drosophila melanogaster; I wished to determine if the embryo's geometry alone was capable of accounting for observed spatial distributions of gap gene products and to infer feasible genetic regulatory networks (GRNs) via parameter estimation of the GRN interaction terms. Simulation of the biosensor successfully predicted an optimal electrode density on the biosensor surface, allowing us to fabricate improved biosensors. Simulation of the gap gene system on 1D and 3D embryonic demonstrated that geometric effects were insufficient to produce observed distributions when simulated with previously reported GRNs. Noting the effects of the error definition on the outcome of parameter estimation, I conclude with a characterization of assorted error definitions (objective functions), describe data characteristics to which they are sensitive, and end with a suggested procedure for objective function selection. Choice of objective function is important in parameter estimation of spatiotemporal system models in varied biological and biotechnological disciplines.
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Keane, Harriet. "Network pharmacology of the MPP+ cellular model of Parkinson's disease." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:1e18e521-c1a3-4f1b-9572-9c68e0f16c2f.
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Parkinson's disease (PD) is an incurable neurodegenerative motor disorder caused by the inexorable loss of dopamine neurones from the substantia nigra pars compacta. Cell loss is characterised by the perturbation of multiple physiological processes (including mitochondrial function, autophagy and dopamine homeostasis) and much of this pathophysiology can be reproduced in vitro using the mitochondrial toxin MPP+ (1-methyl-4-phenylpyridinium). It was hypothesised that MPP+ toxicity could be modelled using protein-protein interaction networks (PPIN) in order to better understand the interplay of systems-level processes that result in eventual cell death in MPP+ models and PD. Initially, MPP+ toxicity was characterised in the human, dopamine-producing cell line BE(2)-M17 and it was confirmed that the neurotoxin resulted in time and dose dependent apoptosis. A radio-label pulse-chase assay was developed and demonstrated that MPP+ induced decreased autophagic flux preceded cell death. Autophagic dysfunction was consistent with lysosome deacidification due to cellular ATP depletion. Pertinent PPINs were sampled from publically available data using a seedlist of proteins with validated roles in MPP+ toxicity. These PPINs were subjected to a series of analyses to identify potential therapeutic targets. Two topological methods based on betweenness centrality were used to identify target proteins predicted to be critical for the crosstalk between mitochondrial dysfunction and autophagy in the context of MPP+ toxicity. Combined knockdown of a subset of target proteins potentiated MPP+ toxicity and the combined resulted in cellular rescue. Neither of these effects was observed following single knockdown/overexpression confirming the need for multiple interventions. Cellular rescue occurred via an autophagic mechanism; prominent autophagosomes were formed and it was hypothesised that these structures allowed for the sequestration of damaged proteins. This thesis demonstrates the value of PPINs as a model for Parkinson's disease, from network creation through target identification to phenotypic benefit.
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Sundqvist, Nicolas. "Can you trust your model? A showcase study of validation in 13C metabolic flux analysis." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156328.
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Cellular metabolism is one of the most fundamental systems for any living organisms, involving thousands of metabolites and reactions that forms large interconnected metabolic networks. Proper and comprehensive understanding of the metabolism in human cells has been a field of research for a long time. One of the key parameters in understanding the metabolism are the metabolic fluxes, which are the rates of conversion of metabolic intermediates. Currently, one of the main approaches for determining these fluxes is metabolic flux analysis (MFA), in which isotope-labelled compounds are introduced into the system and measured. Mathematical models are then used to calculate a prediction of the systems flux configuration. However, the current paradigm of MFA lack established methods for validating that a model can accurately predict quantities for which there are no experimental data. In this study, a model for the central human metabolism was created and evaluated with regards to the model’s ability to predict a validation dataset. Further, an uncertainty analysis of these predictions were performed with a prediction profile likelihood analysis. This study has conclusively shown that MFA models can be validated against experimental data that the model has never seen before. Additionally, such model predictions were shown to be observable with a well determined prediction uncertainty. These results shows that a systematic validation of MFA models is possible. This in turn allows for a greater trust to be placed in the models, and in any conclusions that are based on such models.
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Wu, Zujian. "A generic approach to behaviour-driven biochemical model construction." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7413.
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Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features.
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Alborgeba, Zainab. "Development and evaluation of a cost-effectiveness analysis model for sepsis diagnosis." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19155.
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Sepsis is a life-threatening organ dysfunction that is caused by a dysregulated host response to infection. Sepsis is a substantial health care and economic burden worldwide and is one of the most common reasons for admission to the hospital and intensive care unit. Early diagnosis and targeted treatment of sepsis are the bases to reduce the mortality and morbidity. Conventional blood culturing is the gold standard method for sepsis diagnostics. However, blood culturing is a time consuming method, requiring at least 48 to 72 hours to get the first results with very low sensitivity and specificity. The aim of this study was to determine and assess the direct sepsis-related costs for PCR-based diagnostic strategies (SeptiFast and POC/LAB). A mathematical model was constructed to compare PCR-based diagnostic strategies with the conventional blood culturing. Three case scenarios were investigated based on data from the United Kingdom, Spain and the Czech Republic. It was found that, POC/LAB was the most cost effective strategy in all countries if it could reduce the hospitalization length of stay with at least 3 days in the normal hospital ward and 1 day in the intensive care unit. Reducing the hospitalization length of stay had the greatest impact on the economic outcomes. While, reducing the costs of the diagnostic strategies did not show a remarkable effect on the economic results. In conclusion, the findings suggest that PCR-rapid diagnostic methods could be cost-effective for the diagnosis of patients with sepsis if they could reduce the hospitalization length of stay.
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Parsons, Sven David Charles. "Natural animal model systems to study tuberculosis." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4505.
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Thesis (PhD (Molecular Biology and Human Genetics))--University of Stellenbosch, 2010.ENGLISH ABSTRACT: The growing global epidemic of human tuberculosis (TB) results in 8 million new cases of this disease and 2 million deaths annually. Control thereof will require greater insight into the biology of the causative organism, Mycobacterium tuberculosis, and into the pathogenesis of the disease. This will benefit the design of new vaccines and diagnostic assays which may reduce the degree of both disease transmission and progression. Animal models have played a vital role in the understanding of the aetiology, pathogenesis, and treatment of TB. Much of such insight has been obtained from experimental infection models, and the development of new vaccines, for example, is dependant on these. Nonetheless, studies utilising naturally occurring TB in animals, such as those which have investigated the use of interferon-gamma release assays (IGRA) for its diagnosis, have contributed substantially to the body of knowledge in this field. However, there are few such examples, and this study sought to identify and investigate naturally occuring animal TB in South Africa as an opportunity to gain further insight into this disease. During the course of this study, the dassie bacillus, a distinctly less virulent variant of M. tuberculosis, was isolated from a rock hyrax from the Western Cape Province of South Africa. This has provided new insight into the widespread occurrence of this organism in rock hyrax populations, and has given impetus to further exploring the nature of the difference in virulence between these pathogens. Also investigated was M. tuberculosis infection in dogs in contact with human TB patients. In so doing, the first reported case of canine TB in South Africa was described, v a novel canine IGRA was developed, and a high level of M. tuberculosis infection in these animals was identified. This supports human data reflecting high levels of transmission of this pathogen during the course of human disease. Additionally, the fact that infected companion animals may progress to disease and potentially act as a source of human infection was highlighted. However, an attempt to adapt a flow cytometric assay to study cell-mediated immune responses during canine TB revealed the limitations of such studies in species in which the immune system remains poorly characterised. The use of IGRAs to diagnose TB was further explored by adapting a human assay, the QuantiFERON-TB Gold (In-Tube Method), for use in non-human primates. These studies have shown that such an adaption allows for the sensitive detection of TB in baboons (Papio ursinus) and rhesus macaques (Macaca mulatta) and may be suitable for adaption for use in other species. However, they have also evidenced the limitation of this assay to specifically detect infection by M. tuberculosis. Finally, to contextualise the occurrence of the mycobacterial infections described above, and other similar examples, these have been reviewed as an opinion piece. Together, these investigations confirm that animal models will continue to make important contributions to the study of TB. More specifically, they highlight the opportunities that naturally occuring animal TB provides for the discovery of novel insights into this disease.
AFRIKAANSE OPSOMMING: Wêreldwye tuberkulose (TB) epidemie veroorsaak agt miljoen nuwe gevalle en twee miljoen sterftes jaarliks. Ingryping by die beheer hiervan vereis begrip van die biologie van die mikroörganisme Mycobacterium tuberculosis, die oorsaak van TB, asook van die patogenese van die siekte self. Hierdie kennis kan lei tot ontwerp van nuwe entstowwe en diagnostiese toetse wat gevolglik beide die oordrag- en vordering van die siekte mag bekamp. Dieremodelle speel lankal 'n rol in ons begrip van die etiologie-, patogenese- en behandeling van TB. Insig is grotendeels verkry vanaf eksperimentele infeksiemodelle, en ontwikkeling van entstowwe, onder andere, is afhanklik van soortgelyke modelle. Desnieteenstaande, studies wat natuurlike TB voorkoms in diere ondersoek, byvoorbeeld dié wat op die ontwikkeling van interferon-gamma vrystellingstoetse (IGVT) fokus, het merkwaardige bydrae gemaak tot kennis en begrip in hierdie studieveld. Daar is slegs enkele soortgelyke voorbeelde. Om hierdie rede is die huidige studie uitgevoer waarbinne natuulike diere-TB geïdentifiseer en ondersoek is in Suid-Afrika om verdere kennis en insig te win aangaande TB. Die "dassie bacillus", bekend om beduidend minder virulent te wees as M. tuberculosis, is tydens hierdie studie geïsoleer vanuit 'n klipdassie (Procavia capensis) in die Wes-Kaapse provinsie, Suid-Afrika. Insig in die wydverspreide voorkoms van hierdie organisme in klipdassie bevolkings is gevolglik verkry en verskaf momentum om die aard van verskil in virulensie tussen dié patogene te bestudeer. vii Voorts is M. tuberculosis infeksie bestudeer in honde wat in kontak is met menslike TB pasiënte en word die eerste geval van honde TB dus in Suid-Afrika beskryf. In hierdie groep diere, is 'n hoë vlak van M. tuberculosis infeksie geïdentifiseer deur gebruik te maak van 'n nuut ontwikkelde IGVT vir die diagnose van honde TB. Gevolglik ondersteun dié studie bevindinge van menslike studies wat toon dat besondere hoë vlakke van M. tuberculosis oordrag voorkom gedurende die verloop van die siekte. Verder toon die studie dat geïnfekteerde troeteldiere 'n bron van menslike infeksie kan wees. 'n Poging om 'n vloeisitometriese toets te ontwikkel om die aard van selgefundeerde immuunreaksies te bestudeer in honde met TB toon die beperkings van dergelike studies in spesies waarin die immuunsisteem gebrekkig gekarakteriseer is. Die gebruik van IGVT'e in die diagnose van TB is verder ondersoek deur 'n menslike toets (QuantiFERON-TB Gold, In-Tube Method) aan te pas vir die gebruik van nie-menslike primaat gevalle. Hierdie studies toon gevolglik dat so 'n aanpassing toepaslik is vir hoogs sensitiewe deteksie van TB in chacma bobbejane (Papio ursinus) en rhesus ape (Macaca mulatta), en mag ook aangepas word vir gebruik in ander spesies. Tog word die beperkings van hierdie toets om infeksie wat spesifiek deur M. tuberculosis veroorsaak uitgelig. Ter afsluiting word hierdie studie in konteks geplaas deur 'n oorsig te gee van bogenoemde- en soortgelyke gevalle van dierlike infeksie deur mikobakterieë in Suid-Afrika. Hierdie studies bevestig dat dieremodelle steeds belangrike toevoegings maak tydens die bestudering van TB en lig veral die moontlikhede uit dat bestudering van natuulike TB in diere kan lei tot die ontdekking van nuwe insigte ten opsigte van die siekte self.
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Jha, Sumit Kumar. "Model Validation and Discovery for Complex Stochastic Systems." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/10.
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In this thesis, we study two fundamental problems that arise in the modeling of stochastic systems: (i) Validation of stochastic models against behavioral specifications such as temporal logics, and (ii) Discovery of kinetic parameters of stochastic biochemical models from behavioral specifications.
We present a new Bayesian algorithm for Statistical Model Checking of stochastic systems based on a sequential version of Jeffreys’ Bayes Factor test. We argue that the Bayesian approach is more suited for application do- mains like systems biology modeling, where distributions on nuisance parameters and priors may be known. We prove that our Bayesian Statistical Model Checking algorithm terminates for a large subclass of prior probabilities. We also characterize the Type I/II errors associated with our algorithm. We experimentally demonstrate that this algorithm is suitable for the analysis of complex biochemical models like those written in the BioNetGen language. We then argue that i.i.d. sampling based Statistical Model Checking algorithms are not an effective way to study rare behaviors of stochastic models and present another Bayesian Statistical Model Checking algorithm that can incorporate non-i.i.d. sampling strategies.
We also present algorithms for synthesis of chemical kinetic parameters of stochastic biochemical models from high level behavioral specifications. We consider the setting where a modeler knows facts that must hold on the stochastic model but is not confident about some of the kinetic parameters in her model. We suggest algorithms for discovering these kinetic parameters from facts stated in appropriate formal probabilistic specification languages. Our algorithms are based on our theoretical results characterizing the probability of a specification being true on a stochastic biochemical model. We have applied this algorithm to discover kinetic parameters for biochemical models with as many as six unknown parameters.
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Mitchell, Simon. "A computational model of human iron metabolism." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-computational-model-of-human-iron-metabolism(c3afe167-4a40-42aa-8fd8-a65e47dfe7eb).html.
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Iron is essential for virtually all organisms, yet it can be highly toxic if not properly regulated. Only the Lyme disease pathogen Borrelia burgdorferi has evolved to not require iron (Aguirre et al., 2013).Recent findings have characterised elements of the iron metabolism network, but understanding of systemic iron regulation remains poor. To improve understanding and provide a tool for in silico experimentation, a computational model of human iron metabolism has been constructed. COPASI was utilised to construct a model that included detailed modelling of iron metabolism in liver and intestinal cells. Inter-cellular interactions and dietary iron absorption were included to create a systemic computational model. Parameterisation was performed using a wide variety of literature data. Validation of the model was performed using published experimental and clinical findings, and the model was found to recreate quantitatively and accurately many results. Analysis of sensitivities in the model showed that, despite enterocytes being the only route of iron uptake, almost all control over the system is provided by reactions in the liver. Metabolic control analysis identified key regulatory factors and potential therapeutic targets. A virtual haemochromatosis patient was created and compared to a simulation of a healthy human. The redistribution of control in haemochromatosis was analysed in order to improve our understanding of the condition and identify promising therapeutic targets. Cellular prion protein (PrP) is an enigmatic protein, implicated in disease when misfolded, but its physiological role remains a mystery. PrP was recently found to have ferric-reductase capacity. Potential sites of ferric reduction were simulated and the findings compared to PrP knockout mice experiments. I propose that the physiological role of PrP is in the chemical reduction of endocytosed ferric iron to its ferrous form following transferrin receptor-mediated uptake.
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Patel, Manish. "A multiscale discrete model integration strategy for Systems Biology implemented in a grid-enabled software platform : an example application from cancer systems modelling." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1445871/.
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Model integration - the process by which different modelling efforts can be brought together to simulate the target system - is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale. A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence describe focus in a way that can be integrated with behavioural descriptions of other models. In the second step this behaviour is decomposed into an agent-based system by translating the models into local interaction rules. These rules must be enriched and the agent model simulated, therefore a Grid-like Java infrastructure was developed and tested on an 18-node Beowulf cluster. The two-tier approach was tested on this software by taking 4 different models, each exhibiting complexities and submodels, that were successfully integrated and simulated together. The results showed all of the main challenges could be overcome given the correct conditions for rule enrichment, in this case implemented as a genetic algorithm that operated on rule components. This research represents a key breakthrough for cancer systems research. The two-tier approach could provide the tools necessary to understand tumour behavioural complexity and hence provide a means to combat the disease.
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Smith, Robert William. "Mathematical modelling of photoperiodic external coincidence mechanisms in the model plant, Arabidopsis thaliana." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/11734.
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As plants are sessile organisms, processes controlling plant growth and development must react to fluctuations in the external environment to aid plant survival. However, as the climate of the Earth changes and becomes more extreme, plants become less able to develop to their optimal capacity and this can have an adverse effect on crop yield and biofuel feedstock production. Thus, it is becoming increasingly important to understand the molecular mechanisms used by plants to respond to external stimuli. One important system that plants utilise in their response to environmental fluctuations is the circadian clock. The circadian clock is a time-measuring device that buffers the timing of plant growth and development against fluctuations in the local environment, such as temperature, light quality and light intensity. Importantly, the circadian clock is also able to measure day-length (photoperiod). Thus, plant development and growth is co-ordinated with photoperiod that is closely linked to seasonal changes. A key example of this is the time taken for a plant to flower. Flowering of Arabidopsis thaliana occurs specifically in long-days (LDs) of spring/summer months. Thus, the circadian clock is a key regulator promoting flowering in LD conditions. In conjunction with experimental studies, mathematical modelling has proven to be a successful method of elucidating the mechanisms that underlie complex biological systems. One example of this 'systems biology' approach is in uncovering the components that make up the Arabidopsis circadian clock mechanism. Previous research in our group has also led to the development of a model describing photoperiodic flowering that is tentatively linked to the circadian clock mechanism. In this thesis I shall develop on these models to highlight five key results: 1. using rhythmic PHYTOCHROME INTERACTING FACTOR 4 (PIF4) and PIF5 mRNA as an example, I shall show that multiple circadian regulators are required to describe rhythmic transcription of target genes across multiple photoperiods; 2. the stabilisation of CONSTANS (CO) protein by the blue light-signalling component FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1) is required to for flowering in LDs and has a relatively larger impact on photoperiodic flowering than FKF1-dependent degradation of CYCLING DOF FACTOR 1 (CDF1), an inhibitor of flowering; 3. multiple components of the circadian clock play specific post-translational roles in photoperiodic flowering to promote the acceleration of flowering specifically in LDs; 4. temperature regulation of photoperiodic flowering can be explained through an interaction between CO and PIF proteins, limiting the effects of temperature to a specific time-window in a 24-hour day; 5. red light- and temperature-control of the circadian clock can be explained by altering the post-translational regulation of circadian clock components.
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Sumner, T. "Sensitivity analysis in systems biology modelling and its application to a multi-scale model of blood glucose homeostasis." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19896/.
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Biological systems typically consist of large numbers of interacting components and involve processes at a variety of spatial, temporal and biological scales. Systems biology aims to understand such systems by integrating information from all functional levels into a single cohesive model. Mathematical and computational modelling is a key part of the systems biology approach and can be used to produce composite models which describe systems across multiple scales. One of the major diculties in constructing models of biological systems is the lack of precise parameter values which are often associated with a high degree of uncertainty. This uncertainty in parameter values can be incorporated into the modelling process using sensitivity analysis, the systematic investigation of the relationship between uncertain model inputs and the resulting variation in the model outputs. This thesis discusses the use of global sensitivity analysis in systems biology modelling and addresses two main problem areas: the application of sensitivity analysis to time dependent model outputs and the analysis of multi-scale models. An approach to the analysis of time dependent model outputs which makes use of principal component analysis to extract the key modes of variation from the data, is presented. The analysis of multi-scale models is addressed using group-based sensitivity analysis which enables the identication of the most important sub-processes in the model. Together these methods provide a new methodology for sensitivity analysis in multi-scale systems biology modelling. The methodology is applied to a composite model of blood glucose homeostasis that combines models of processes at the sub-cellular, cellular and organ level to describe the physiological system. The results of the analysis suggest three main points about the system: the mobilisation of calcium by glucagon plays a minor role in the regulation of glycogen metabolism; auto-regulation of hepatic glucose production by glucose is important in regulating blood glucose levels; time-delays between changes in blood glucose levels, the release of insulin by the pancreas and the eect of the hormone on hepatic glucose production are important in the possible onset of ultradian glucose oscillations. These results suggest possible directions for further study into the regulation of blood glucose.
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Karkutla, Raja K. "Agent Based and Stochastic Simulations for Non-homogeneous Systems." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1271708137.
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Ferrara, Joseph. "A Study of Nonlinear Dynamics in Mathematical Biology." UNF Digital Commons, 2013. http://digitalcommons.unf.edu/etd/448.
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We first discuss some fundamental results such as equilibria, linearization, and stability of nonlinear dynamical systems arising in mathematical modeling. Next we study the dynamics in planar systems such as limit cycles, the Poincaré-Bendixson theorem, and some of its useful consequences. We then study the interaction between two and three different cell populations, and perform stability and bifurcation analysis on the systems. We also analyze the impact of immunotherapy on the tumor cell population numerically.
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Gao, Qian. "A systems biology approach to multi-scale modelling and analysis of planar cell polarity in Drosophila melanogaster wing." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7478.
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Systems biology aims to describe and understand biology at a global scale where biological systems function as a result of complex mechanisms that happen at several scales. Modelling and simulation are computational tools that are invaluable for description, understanding and prediction these mechanisms in a quantitative and integrative way. Thus multi-scale methods that couple the design, simulation and analysis of models spanning several spatial and temporal scales is becoming a new emerging focus of systems biology. This thesis uses an exemplar – Planar cell polarity (PCP) signalling – to illustrate a generic approach to model biological systems at different spatial scales, using the new concept of Hierarchically Coloured Petri Nets (HCPN). PCP signalling refers to the coordinated polarisation of cells within the plane of various epithelial tissues to generate sub-cellular asymmetry along an axis orthogonal to their apical-basal axes. This polarisation is required for many developmental events in both vertebrates and non-vertebrates. Defects in PCP in vertebrates are responsible for developmental abnormalities in multiple tissues including the neural tube, the kidney and the inner ear. In Drosophila wing, PCP is seen in the parallel orientation of hairs that protrude from each of the approximately 30,000 epithelial cells to robustly point toward the wing tip. This work applies HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) in Drosophila wing. HCPN facilitate the construction of mathematically tractable, compact and parameterised large-scale models. Different levels of abstraction that can be used in order to simplify such a complex system are first illustrated. The PCP system is first represented at an abstract level without modelling details of the cell. Each cell is then sub-divided into seven virtual compartments with adjacent cells being coupled via the formation of intercellular complexes. A more detailed model is later developed, describing the intra- and inter-cellular signalling mechanisms involved in PCP signalling. The initial model is for a wild-type organism, and then a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP, are constructed. Among them, the largest model consists of 800 cells which when unfolded yields 164,000 places (each of which is described by an ordinary differential equation). This thesis illustrates the power and validity of the approach by showing how the models can be easily adapted to describe well-documented genetic mutations in the Drosophila wing using the proposed approach including clustering and model checking over time series of primary and secondary data, which can be employed to analyse and check such multi-scale models similar to the case of PCP. The HCPN models support the interpretation of biological observations reported in literature and are able to make sensible predictions. As HCPN model multi-scale systems in a compact, parameterised and scalable way, this modelling approach can be applied to other large-scale or multi-scale systems.
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Bakhtiyari, Elnaz. "Analysis of differentially expressed genes (DEGs) in neuronal cells from the cerebral cortex of Alzheimer’s disease mouse model." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19218.
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Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder with large implications for society and individuals. AD is a multi-factor disorder, with these factors having a direct or indirect correlation with each other. Despite many studies with different aspects on molecular and cellular pathways, there is still no specific treatment for AD. Identification of potential pathogenic factors can be done by transcriptomic studies of differentially expressed genes (DEGs), but the outcomes have been contradictory. Using both bioinformatics and meta-analysis methods can be useful for removing such inconsistencies. A useful and common approach for a better understanding of neurodegenerative disease is to assess its molecular causes, by comparing the gene expression levels in healthy and disease tissues. Next-generation RNA-sequencing is a valuable method for analyzing both coding and non-coding regions of RNA, and it has made it possible to identify differentially expressed genes in large-scale data. The aim of the current study was to get a better understanding of the transcriptional changes in AD models, and identify differentially expressed genes between healthy and AD individuals from the adult mouse brain model as well as detecting AD pathways. In this study, the transcriptomes of purified neuron, astrocyte and microglia cells from mouse brains were analyzed using publicly available RNA-seq datasets. The DEGs were identified for all three mentioned cell types using DESeq2 and EdgeR packages. All statistical analyses were performed by R software and the DEGs detected by DESeq2 and edgeR, respectively, were compared using Venn diagrams. Additionally, analyzing the AD pathway was performed using GOrilla tool for visualizing the enriched gene ontology (GO) terms in the list of ranked genes. From this project, it was found that there were very few significantly DEGs between AD and healthy samples in neuron cells, while there were more DEGs in astrocyte and microglia cells. In conclusion, comparing DESeq2 and egeR packages using Venn diagrams showed a slight advantage of DESeq2 in detection accuracy, since it was able to identify more DEGs than edgeR. Moreover, analyzing AD pathway using GOrilla tool indicated that identified enriched GO terms by each cell type differed from each other. For astrocytes, more enriched GO terms were identified than for microglia cells, while no significant enriched GO terms were detected for neuron cells.
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Foguet, Coll Carles. "Development of model-driven approaches for metabolic flux analysis and anticancer drug discovery." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668644.
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Metabolism is a hallmark of life and underlies most biological processes in both health and disease. For instance, dysregulation of liver metabolism underlies multifactorial disorders such as diabetes or obesity. Similarly, cancer progression involves a reprogramming of metabolism to support unchecked proliferation, metastatic spread and other facets of the cancer phenotype. Hence, the study of metabolism is of great biomedical interest.
The metabolic phenotype emerges from the complex interactions of metabolites, enzymes, and the signaling cascades regulating their expression and thus must be studied following a holistic approach. With this aim, Systems Biology formulates the interactions between the molecular components of metabolism as a set of mathematical expressions, termed metabolic models, and uses them as a framework to integrate multiple layers of data (e.g., transcriptomics, proteomics and metabolomics) and simulate the emergent metabolic phenotype. The Systems Biology toolbox for the analysis of metabolism consists of several complementary model-based approaches, each with its strengths and limitations. For instance, constraint-based modeling can predict flux distributions at a genome-scale, whereas kinetic modeling and 13C metabolic flux analysis (13C MFA) can more accurately model central carbon metabolism.
As part of this Ph.D. thesis, we have expanded this toolbox through the development of new model-based approaches for computing both detailed metabolic maps of central carbon metabolism and genome-scale flux maps. With this aim, we developed HepatoDyn, a highly detailed kinetic model of hepatocyte metabolism capable of dynamic 13C MFA and used it to characterize the negative effects of fructose in hepatic metabolic function. Similarly, we also developed Iso2Flux, a novel steady-state 13C MFA software, and parsimonious 13C MFA, a new 13C MFA algorithm that can integrate transcriptomics to trace flux through large metabolic networks. Even more, we developed r2MTA a constraint-based modeling algorithm to robustly identify the optimal interventions to induce a transition towards a therapeutically desirable metabolic state. Finally, we also developed a workflow for integrating transcriptomics, metabolomics, gene dependencies, and 13C MFA to predict genome-scale flux maps.
Furthermore, we apply the systems biology toolbox, using both newly developed and existing tools, to the genome-scale analysis of the molecular drivers underlying cancer stem cells and metastasis in prostate and colorectal cancer, respectively. We identify putative therapeutic interventions against both phenotypes paving the way for a new generation of anticancer drugs.
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Jiang, Xiaoshan. "A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/42523.
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In each individual cell, there are many signaling pathways that may interact or cross talk with each other. Especially, some can sense the same signal and go through different pathways but eventually converge at some points. Therefore repetitive signal stimulations may result in intricate cell responses, among which the priming effect has been extensively studied in monocytes and macrophages as it plays an unambiguously crucial role in immunological protection against pathogen infection. Priming basically describes the phenomena that host cells can launch a dramatically enhanced response to the second higher dose of stimulus if cells have been previously treated with a lower dose of identical stimulus. It was reported to be associated with many human immune diseases (such as rheumatoid arthritis and hepatitis) that are attracting more and more researches on the priming effect. It is undoubtable that many genes are involved in this complicated biological process. Microarray is one of the standard techniques that are applied to do the transcriptome profiling of cells under repetitive stimuli and reveal gene regulatory networks. Therefore a well-established pipeline to analyze microarray data is of special help to investigate the underlying mechanism of priming effect. In this research, we aimed to design a strategy that can be used to interpret microarray data and to propose gene candidates that potentially participate in priming effect. To confirm our analysis results, we used a detailed mathematical model to further demonstrate the mechanism of a specific case of priming effect in a computational perspective.Master of Science
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Buckalew, Richard L. "Mathematical Models in Cell Cycle Biology and Pulmonary Immunity." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1395242276.
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Luise, Fabiana. "A systems biology approach to model the development of the mouse second branchial arch in mouse embryonic stem cells." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/a-systems-biology-approach-to-model-the-development-of-the-mouse-second-branchial-arch-in-mouse-embryonic-stem-cells(95f1e66d-4260-413c-bc9a-2eb106df4c84).html.
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The generation of the face and neck is unique to vertebrate embryonic development, and requires a complex orchestration of dynamic molecular and tissue interactions. Elucidation of the molecular control of craniofacial development is important for the understanding of morphogenesis, evolution and disease. Branchial arches (BAs) are transient structures of early craniofacial development giving rise to different components of the oropharyngeal apparatus. BA development requires the contribution of endoderm, mesoderm, ectoderm and cranial neural crest cells (CNCCs). CNCCs form transiently during embryogenesis and are characterised by extensive multipotency and migratory abilities. CNCCs migration represents a key step in the evolution of the vertebrate head. This research aimed to uncover the molecular mechanisms regulating the mouse second branchial arch (IIBA), which contributes to the formation of the middle ear and neck. IIBA development depends on the expression of the transcription factor Hoxa2. Abnormal development of the IIBA is responsible for many craniofacial congenital malformations, therefore, dissecting the IIBA molecular control is important for gaining insights into development and disease mechanisms. Inference of regulatory networks from omic data has provided a valuable tool to study biological mechanisms. A network model from mouse IIBA transcriptomic data was generated by using an overlapping module algorithm. This model identified CBL, CTNNB1, EP300, EGFR and CDH1 (E-CADHERIN) as putative regulatory proteins associated with IIBA development and suggested the presence of an epithelial to mesenchymal transition (EMT) event. EMT is a crucial process occurring during embryonic development and adult tissue homeostasis, which is characterised by loss of cell-cell adhesion and increased cellular motility. A prerequisite for EMT is the loss of E-CADHERIN. By means of a network comparison approach, the study uncovered a statistically significant overlap between the IIBA network model and one generated from transcriptomic data of wild type (wtD3) versus E-cadherin knock-out (Ecad-/-) mouse embryonic stem cells (mESCs) (Ecad-/- vs wtD3), with candidate proteins shared between the two models. The function of the common putative regulator EP300 and its related histone epigenetic signature H3K27ac was interrogated in both wtD3 and Ecad-/- mESCs by chromatin immunoprecipitation and massive parallel sequencing (ChIP-seq) analysis. The results showed that EP300 and H3K27ac binding signatures were enriched in Ecad-/- mESCs, suggestive of a poised epigenetic EMT phenotype. EP300 and H3K27ac were increased at network candidate genomic loci. Conversely, ChIP-seq analysis on human induced pluripotent stem cells (hiPSCs) treated with an E-CADHERIN inhibiting peptide revealed absence of overlap with the network, suggesting they could map to a different stage of IIBA development. The function of the candidate molecules identified through the IIBA network model was tested by chemical inhibition assays in mESCs induced to differentiate with retinoic acid (RA) treatment and subsequent analysis of Hoxa2 transcript expression. CDH1 and CTNNB1 repress Hoxa2 transcript expression, whereas CBL and EGFR are shown to be required for Hoxa2 activation. This research is the the first demonstration of a network biology approach to the study of IIBA development in silico and confirmation of candidate protein function using an in vitro model of IIBA development.