Dissertations / Theses on the topic 'Network pathway'

Current approaches integrating large scale data and information from a variety of sources to reveal molecular basis of cellular events do not adequately benefit from pathway information. Here, we portray a network structure based pathway enrichment system that fuses and exploits model and data: signalling pathways are taken as the biological models while microarray and ChIP-seq data are the sample input data sources among many other alternatives. Our model- and data-driven hybrid system allows to quantitatively assessing the biological activity of a cyclic pathway and simultaneous enrichment of the significant paths leading to the ultimate cellular response. Signal Transduction Score Flow (SiTSFlow) algorithm is the fundamental constituent of proposed network structure based pathway enrichment system. SiTSFlow algorithm converts each pathway into a cascaded graph and then gene scores are mapped onto the protein nodes. Gene scores are transferred to en route of the pathway to form a final activity score describing behaviour of a specific process in the pathway while enriching of the gene node scores. Because of cyclic pathways, the algorithm runs in an iterative manner and it terminates when the node scores converge. The converged final activity score provides a quantitative measure to assess the biological significance of a process under the given experimental conditions. The conversion of cyclic pathways into cascaded graphs is performed by using a linear time multiple source Breadth First Search Algorithm. Furthermore, proposed network structure based pathway enrichment system works in linear time in terms of nodes and edges of given pathways. In order to explore various biological responses of several processes in a global signalling network, the selected small pathways have been unified based on their common gene and process nodes. The merge algorithm for pathways also runs in linear time in terms of nodes and edges of given pathways. In the experiments, SiTSFlow algorithm proved the convergence behaviour of activity scores for several cyclic pathways and for a global signalling network. The biological results obtained by assessing of experimental data by described network structure based pathway enrichment system were in correlation with the expected cellular behaviour under the given experimental conditions.

The advent of various types of high-throughput genomic data has enabled researchers to investigate complex biological systems in a systemic way and started to shed light on the underlying molecular mechanisms in cancers. To analyze huge amounts of genomic data, effective statistical and machine learning tools are clearly needed; more importantly, integrative approaches are especially needed to combine different types of genomic data for a network or pathway view of biological systems. Motivated by such needs, we make efforts in this dissertation to develop integrative framework for pathway analysis. Specifically, we dissect the molecular pathway into two parts: protein-DNA interaction network and protein-protein interaction network. Several novel approaches are proposed to integrate gene expression data with various forms of biological knowledge, such as protein-DNA interaction and protein-protein interaction for reliable molecular network identification. The first part of this dissertation seeks to infer condition-specific transcriptional regulatory network by integrating gene expression data and protein-DNA binding information. Protein-DNA binding information provides initial relationships between transcription factors (TFs) and their target genes, and this information is essential to derive biologically meaningful integrative algorithms. Based on the availability of this information, we discuss the inference task based on two different situations: (a) if protein-DNA binding information of multiple TFs is available: based on the protein-DNA data of multiple TFs, which are derived from sequence analysis between DNA motifs and gene promoter regions, we can construct initial connection matrix and solve the network inference using a constraint least-squares approach named motif-guided network component analysis (mNCA). However, connection matrix usually contains a considerable amount of false positives and false negatives that make inference results questionable. To circumvent this problem, we propose a knowledge based stability analysis (kSA) approach to test the conditional relevance of individual TFs, by checking the discrepancy of multiple estimations of transcription factor activity with respect to different perturbations on the connections. The rationale behind stability analysis is that the consistency of observed gene expression and true network connection shall remain stable after small perturbations are applied to initial connection matrix. With condition-specific TFs prioritized by kSA, we further propose to use multivariate regression to highlight condition-specific target genes. Through simulation studies comparing with several competing methods, we show that the proposed schemes are more sensitive to detect relevant TFs and target genes for network inference purpose. Experimentally, we have applied stability analysis to yeast cell cycle experiment and further to a series of anti-estrogen breast cancer studies. In both experiments not only biologically relevant regulators are highlighted, the condition-specific transcriptional regulatory networks are also constructed, which could provide further insights into the corresponding cellular mechanisms. (b) if only single TF's protein-DNA information is available: this happens when protein-DNA binding relationship of individual TF is measured through experiments. Since original mNCA requires a complete connection matrix to perform estimation, an incomplete knowledge of single TF is not applicable for such approach. Moreover, binding information derived from experiments could still be inconsistent with gene expression levels. To overcome these limitations, we propose a linear extraction scheme called regulatory component analysis (RCA), which can infer underlying regulation relationships, even with partial biological knowledge. Numerical simulations show significant improvement of RCA over other traditional methods to identify target genes, not only in low signal-to-noise-ratio situations and but also when the given biological knowledge is incomplete and inconsistent to data. Furthermore, biological experiments on Escherichia coli regulatory network inferences are performed to fairly compare traditional methods, where the effectiveness and superior performance of RCA are confirmed. The second part of the dissertation moves from protein-DNA interaction network up to protein-protein interaction network, to identify dys-regulated protein sub-networks by integrating gene expression data and protein-protein interaction information. Specifically, we propose a statistically principled method, namely Metropolis random walk on graph (MRWOG), to highlight condition-specific PPI sub-networks in a probabilistic way. The method is based on the Markov chain Monte Carlo (MCMC) theory to generate a series of samples that will eventually converge to some desired equilibrium distribution, and each sample indicates the selection of one particular sub-network during the process of Metropolis random walk. The central idea of MRWOG is built upon that the essentiality of one gene to be included in a sub-network depends on not only its expression but also its topological importance. Contrasted to most existing methods constructing sub-networks in a deterministic way and therefore lacking relevance score for each protein, MRWOG is capable of assessing the importance of each individual protein node in a global way, not only reflecting its individual association with clinical outcome but also indicating its topological role (hub, bridge) to connect other important proteins. Moreover, each protein node is associated with a sampling frequency score, which enables the statistical justification of each individual node and flexible scaling of sub-network results. Based on MRWOG approach, we further propose two strategies: one is bootstrapping used for assessing statistical confidence of detected sub-networks; the other is graphic division to separate a large sub-network to several smaller sub-networks for facilitating interpretations. MRWOG is easy to use with only two parameters need to be adjusted, one is beta value for performing random walk and another is Quantile level for calculating truncated posteriori mean. Through extensive simulations, we show that the proposed scheme is not sensitive to these two parameters in a relatively wide range. We also compare MRWOG with deterministic approaches for identifying sub-network and prioritizing topologically important proteins, in both cases MRWG outperforms existing methods in terms of both precision and recall. By utilizing MRWOG generated node/edge sampling frequency, which is actually posteriori mean of corresponding protein node/interaction edge, we illustrate that condition-specific nodes/interactions can be better prioritized than the schemes based on scores of individual node/interaction. Experimentally, we have applied MRWOG to study yeast knockout experiment for galactose utilization pathways to reveal important components of corresponding biological functions; we also applied MRWSOG to study breast cancer patient prognostics problems, where the sub-network analysis could lead to an understanding of the molecular mechanisms of antiestrogen resistance in breast cancer. Finally, we conclude this dissertation with a summary of the original contributions, and the future work for deepening the theoretical justification of the proposed methods and broadening their potential biological applications such as cancer studies.
Ph. D.

Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association.  FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms.  BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.

The aim of this doctoral research is to advance understanding of how the primate brain learns to process the detailed spatial form of natural visual scenes. Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects and faces. However, it remains a difficult challenge to understand exactly how these neurons develop their response properties through visually guided learning. This thesis approaches this problem through the use of computational modelling. In particular, I first show how the brain may learn to represent the spatial structure of objects and faces through a series of processing stages along the ventral visual pathway. Then I propose how understanding the two complementary unsupervised learning mechanisms of translation invariance may have useful applications in clinical psychology. Next, the potential functional role of top-down (feedback) propagation of visual information in the brain in driving the development of border ownership cells, which are thought to play a role in binding visual features such as boundary edges to their respective objects, is investigated. In particular, the limitations of traditional rate-coded neural networks in modelling these cells are identified. Finally, a general solution to such binding problems with the use of a more biologically realistic spiking neural network is presented. This work is set to make an important contribution towards understanding how the visual system learns to encode the detailed spatial structure of objects and faces within scenes, including representing the binding relations between the visual features that comprise those objects and faces.

Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Voit, Eberhard O.; Committee Member: Borodovsky, Mark; Committee Member: Butera, Robert; Committee Member: Kemp, Melissa; Committee Member: Park, Haesun. Part of the SMARTech Electronic Thesis and Dissertation Collection.

There is a huge demand for analysis and visualization of the biological models. PathwayPioneer is a web-based tool to analyze and visually represent complex biological models. PathwayPioneer generates the initial layout of the model and allows users to customize it. It is developed using .net technologies (C#) and hosted on the Internet Information Service (IIS) server. At back-end it interacts with python-based COBRApy library for biological calculations like Flux Balance Analysis (FBA). We have developed a parallel processing architecture to accommodate processing of large models and enable message-based communication between the .net webserver and python engine. We compared the performance of our online system by loading a website with multiple concurrent dummy users and performed different time intensive operations in parallel. Given two metabolites of interest, millions of pathways can be found between them even in a small metabolic network. Depth First Search or Breadth First search algorithm retrieves all the possible pathways, thereby requiring huge computational time and resources. In Pathway Search using Informed Method, we have implemented, compared, and analyzed three different informed search techniques (Selected Subsystem, Selected Compartment, and Dynamic Search) and traditional exhaustive search technique. We found that the Dynamic approach performs exceedingly well with respect to time and total number of pathways searches. During our implementation we developed a SBML parser which outperforms the commercial libSBML parser in C#.

Molecular networks are commonly used to explore cellular organisation and disease mechanisms. Function is studied using molecular interaction networks, such as protein-protein networks. Although much biological insight has been gained using these models of molecular function, they are hindered by their reliance on available experimental data and an inability to capture the complexity of biological processes. Functional modules can be identified based on molecular network topology, making it essential that the edges accurately depict molecular interactions. However, these networks struggle to depict the temporal nature of interactions, giving the impression that all interactions are constant. This misrepresentation can result in functionally heterogeneous clusters. The notoriously inaccurate nature of experimental protein interaction data, along with variable conformity among network clusters and functional modules further impedes functional module extraction. Representation of genes by single nodes artificially merges the functions of pleiotropic genes, distorting the arrangement of function within molecular networks. This thesis therefore explores a more suitable model for representing function. Pathways are composed of sets of proteins that are known to interact within a particular cellular context, corresponding to a discernible biological function. Their representation of context dependent cellular activity makes them ideal for use as nodes within a new pathway level model. Using combinatorial algorithms a reduced redundancy pathway set was produced to represent global cellular systems. Enrichment analysis provides reliable functional annotations for each pathway node, attributing independent functions to pleiotropic genes. Edges are based on functional semantic similarity, generating a network representation of functional organisation. Both yeast and human biological systems are presented as functionally connected pathway networks. Pathway annotation and experimentation with semantic similarity measures provides insight into the cross-talk between biological processes. Pathway functional modules elucidate the intracellular implementation of processes. Disease modules highlight the effects of functional perturbations and disease mechanisms. The pathway model provides a complementary, high-level functional model that begins to bridge the gap between molecular data and phenotype. The utilisation of pathway data provides a large, well-validated data source, avoiding the inaccuracies inherent with molecular data. Pathway models better represent components of biological complexity such as pleiotropy and linear implementation of functions.

The network-mapping tool integrated with protein database search can be used for filling pathway holes. A metabolic pathway under consideration (pattern) is mapped into a known metabolic pathway (text), to find pathway holes. Enzymes that do not show up in the pattern may be a hole in the pattern pathway or an indication of alternative pattern pathway. We present a data-mining framework for filling holes in the pattern metabolic pathway based on protein function, prosite scan and protein sequence homology. Using this framework we suggest several fillings found with the same EC notation, with group neighbors (enzymes with same EC number in first three positions, different in the fourth position), and instances where the function of an enzyme has been taken up by the left or right neighboring enzyme in the pathway. The percentile scores are better when closely related organisms are mapped as compared to mapping distantly related organisms.

The number and complexity of genome-scale metabolic networks is increasing as new systems are characterized and existing models are extended. Tools for visualization of network topology and dynamics are not keeping pace and are becoming a bottleneck for advancement. Specically, visualization tools are not optimized for human comprehension and often produce layouts where important interactions and inherent organization are not apparent. Researchers seek visualizations in which the network is partitioned into functional modules and compartments, arranged in linear, cyclic, or branching schema as appropriate, and most importantly, can be customized to their needs and shared. Challenges include the wide diversity in the biological standards, layout schemas, and network formats. This work introduces a web-based tool that provides this functionality as an extension to the existing web-based tool called Pathway Pioneer (www.pathwaypioneer.org). Pathway Pioneer is a dynamic web-based system built as a front-end graphical user interface to the ux balance analysis tool COBRA-py. Full click-and-drag layout editing capabilities are added allowing each metabolite and reaction to be translated and rotated as connecting edges are automatically redrawn. Initial automated layouts for new models maximize planarity while clustering reactions based on subsystem module and compartment. The users are given maximum exibility to design specific layouts while details of convention, such as joined in and out of reaction edges, disconnected co-factors, and connected metabolites, are automatically handled. Layouts can be shared among researchers and explored to archival Symphony format, along with pdf and png images. This tool provides the user with a semi automatic layout algorithm along with graphical and interactive tools to fully customize the network layout for optimal comprehension. Export capabilities are compatible with COBRA-py and other visualization tools. It provides a platform for share model development and innovation to the community, sharpening the R&D curve, and improving the turn-around time of model reconstruction at the genome-scale. Pathway Pioneer provides unique capabilities in customization of metabolic networks that complements and overcomes limitations of the growing body of existing tools.

Biological network data is plentiful with practically every experimental methodology giving â network viewsâ into cellular function and behavior. Bioinformatic screens that yield network data include, for example, genome-wide deletion screens, protein-protein interaction assays, RNA interference experiments, and methods to probe metabolic pathways. Efficient and comprehensive computational approaches are required to model these screens and gain insight into the nature of biological networks. This thesis presents three new algorithms to model and mine network datasets. First, we present an algorithm that models genome-wide perturbation screens by deriving relations between phenotypes and subsequently using these relations in a local manner to derive genephenotype relationships. We show how this algorithm outperforms all previously described algorithms for gene-phenotype modeling. We also present theoretical insight into the convergence and accuracy properties of this approach. Second, we define a new data mining problemâ constrained minimal separator miningâ and propose algorithms as well as applications to modeling gene perturbation screens by viewing the perturbed genes as a graph separator. Both of these data mining applications are evaluated on network datasets from S. cerevisiae and C. elegans. Finally, we present an approach to model the relationship between metabolic pathways and operon structure in prokaryotic genomes. In this approach, we present a new pattern classâ biclusters over domains with supplied partial ordersâ and present algorithms for systematically detecting such biclusters. Together, our data mining algorithms provide a comprehensive arsenal of techniques for modeling gene perturbation screens and metabolic pathways.
Ph. D.

High-throughput whole-genome biological assays are highly intricate and difficult to interpret. The molecular interaction networks generated from evaluation of those experiments suggest that cellular functions are carried out by modules of interacting molecules. Reverse-engineering the modular structure of cellular interaction networks has the promise of significantly easing their analysis. We hypothesize that:

1. cellular wiring diagrams can be decomposed into overlapping modules, where each module is a set of coherently-interacting molecules and
2. a cell responds to a stress or a stimulus by appropriately modulating the activities of a subset of these modules.

Motivated by these hypotheses, we develop models and algorithms that can reverse-engineer molecular modules from large-scale functional genomic data. We address two major problems:

1. Given a wiring diagram and genome-wide gene expression data measured after the application of a stress or in a disease state, compute the active network of molecular interactions perturbed by the stress or the disease.
2. Given the active networks for multiple stresses, stimuli, or diseases, compute a set of network legos, which are molecular modules with the property that each active network can be expressed as an appropriate combination of a subset of modules.

To address the first problem, we propose an approach that computes the most-perturbed subgraph of a curated pathway of molecular interactions in a disease state. Our method is based on a novel score for pathway perturbation that incorporates both differential gene expression and the interaction structure of the pathway. We apply our method to a compendium of cancer types. We show that the significance of the most perturbed sub-pathway is frequently larger than that of the entire pathway. We identify an association that suggests that IL-2 infusion may have a similar therapeutic effect in bladder cancer as it does in melanoma. We propose two models to address the second problem. First, we formulate a Boolean model for constructing network legos from a set of active networks. We reduce the problem of computing network legos to that of constructing closed biclusters in a binary matrix. Applying this method to a compendium of 13 stresses on human cells, we automatically detect that about four to six hours after treatment with chemicals cause endoplasmic reticulum stress, fibroblasts shut down the cell cycle far more aggressively than fibroblasts or HeLa cells do in response to other treatments. Our second model represents each active network as an additive combination of network legos. We formulate the problem as one of computing network legos that can be used to recover active networks in an optimal manner. We use existing methods for non-negative matrix approximation to solve this problem. We apply our method to a human cancer dataset including 190 samples from 18 cancers. We identify a network lego that associates integrins and matrix metalloproteinases in ovarian adenoma and other cancers and a network lego including the retinoblastoma pathway associated with multiple leukemias.
Ph. D.

The effectiveness of psychological therapies has received increasing attention in recent years with a confidant optimism building in the strong research evidence for its efficacy. However, criticism comes from the study of attrition from therapy in routine clinical practice, which studies show can reach from 30 to 60%. Searches for the causes of attrition have uncovered a multitude of correlations but only socio-economic variables emerge as significant predictors of attrition. This present study proposes and tests a theoretical model with clear implications for practice and research. In reviewing three broad literatures on health service use the concept of the Decision Action Pathway Interactive Network (DAPIN) began to emerge. Health decisions are seen as taking place within an emerging decision/action pathway that is subject to a dynamic interaction network. Decisions are made by individuals based on rational calculations, with network interactions providing the mechanism by which the social factors influence the decision/action pathway. Empirical testing of DAPIN consisted of the construction of a patient self-report cost attached to therapy attendance (CATA) measure that could be used to determine whether people of low SES do in fact have higher network costs attached to attending therapy and whether this is related to higher attrition. A small sample of patients attending their first appointment completed CATA and those who unilaterally terminated in the first four sessions compared with those who continued therapy. Weak support was obtained for the DAPIN model. The Demand sub-scale of CAT A proved to be a powerful predictor of unilateral termination from therapy (attrition) at the early stage of therapy attendance and provides a useful short tool for routine clinical practice. The small and idiosyncratic sample used meant that the DAPIN model could not be adequately tested. However, the evidence accumulated suggests that the model is worthy of more extensive testing.

Filamentous growth is a cell differentiation program utilized by Saccharomyces cerevisiae to respond to nutrient limitation in the environment. This process is principally controlled by a mitogen-activated protein kinase (MAPK) pathway but is also impacted by a number of other pathways including Ras2p-cAMP-PKA, Target of Rapamycin, Rim101, and mitochondrial retrograde. Using a high-throughput genetic screening approach in conjunction with directed gene-deletion analysis, I have identified 97 new regulators of the filamentous growth MAPK pathway. These new regulators created new connections to the filamentous growth MAPK pathway as well as extended previously known connections. I have linked several of the pathways governing filamentous growth together as part of an integrated signaling network by showing that these pathways regulate each other’s transcriptional targets. This network indicates an intricate level of communication and coordination among these pathways that has not been previously appreciated. I show that proper coordination of the filamentous growth MAPK pathway is essential for proper morphogenesis and this is a potential reason for the many inputs used to control this response. The filamentous growth MAPK pathway is also regulated by three transmembrane proteins – Msb2p, Sho1p, and Opy2p. Here these three proteins are compared to determine that they have specific functions in regulating filamentous growth. The three proteins exhibit different localization patterns and rates of turnover from the plasma membrane. I show that the Rim101 pathway affects the filamentous growth MAPK pathway independently of the ESCRT pathway which shares components with the Rim101 pathway. Additionally, I have shown that overexpression of the arrestin protein Aly1p results in mislocalized Msb2p and diminished pathway activity.

Background. The seronegative spondyloarthritis (SpA) are a group of chronic inflammatory diseases resulting from a complex interplay among genetic background (mainly represented by HLA-B27) and environmental factors, that leads to the activation of autoinflammation and the dysregulation of the immune-system. In many cases, an early diagnosis and an appropriate monitoring of disease activity can be difficult because of the overlap of clinical features. Furthermore, because of the indices of inflammation, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), are in the normal range in at least half of SpA patients with a clear expression of disease activity, a delay in diagnosis and consequently in treatment in these patients has been documented. This imparts a tremendous symptomatic burden and loss of function in these patients during the productive years of life. For all these reasons, much attention is currently devoted to the identification of biochemical and genetic biomarkers to be used in the diagnosis as well as prognostic factors in evaluating the treatment effectiveness. Among the genetic predisposing factors, a well-known role is that of HLA-B27, which contributes however to only 20–30% of the total heritability, whereas the whole major histocompatibility complex (MHC) accounts for about 40–50% of the genetic risk of developing SpA. This suggested that other genes are involved in pathogenetic mechanism. In fact, in addition to HLA-B27, a number of genetic factors in both, MHC and non-MHC locus, have been claimed to play a role in pathogenesis of SpA. In this context, because of TNF-α is primarily involved in the propagation and perpetuation of inflammation in SpA, the study of TNF-α genetic is of great interest. Several polymorphisms (SNPs) in genes involved in TNF-α signalling, as TNFA, TNFSF15, TNFR1 and TRADD genes, have been identified as associated with SpA, even if results are controversial. Of great interest are also variants in MEFV gene, involved in the pathogenesis of the autoinflammatory disorder Familial Mediterranean Fever (FMF). Recent studies have shown that the SpA, and in particular the ankylosing spondylitis (AS), are very common among patients affected by FMF and that these patients can present with AS as a sole manifestation. The present study, conducted in a cohort of 91 SpA patients and 223 controls, coming from a North-East Italian region, was aimed to identify biohumoral (biochemical and haematological) and genetic factors to support the diagnostic and prognostic (response to therapy) work-up of SpA diseases. In particular, in addition to biochemical and haematological indices, we investigated whether SNPs in the promoter region of TNFA, or SNPs in the autoinflammatory TNFRSF1A and MEFV genes, might concur with HLA-B27 in enhancing the risk of developing SpA disease and/or in predicting the response to anti-TNFα drugs. Methods. The study population comprised 91 patients with a diagnosis of SpA (mean age ± standard deviation: 52.1 ± 12.5 years; 57 males, 34 females) and 223 blood donors (mean age ± standard deviation: 46 ± 11 years; 146 males, 77 females) coming from Veneto Region, a North-East Italian region. Among patients, 36 had a diagnosis of AS and 55 patients of psoriatic arthritis (PsA), which were based on New York and CASPAR criteria respectively. The protocol of this study was approved by the Local Institutional Ethic Committee of University-Hospital of Padua, Italy (Prot.n. 3024P/13), and all participants gave written informed consent before entering the study. Demographic and physiological data, medical and familial history data were collected for each participant. Blood samples were collected and complete blood count, CRP, ESR, uric acid, prealbumin, alanina aminotransferase (ALT) and glucose were evaluated. Direct sequencing of MEFV (exons 2,3,5 and 10) and TNFRSF1A (exons 2,3,4 and 6) genes were performed. HLA-B27 and TNFA polymorphisms (-1031T>C;-857C>T;-376G>A;-308G>A;-238G>A) were assayed by Real Time-PCR. HLA-CW6 allele presence was analysed by molecular genetic testing using a commercially available CE-IVD microarray. Statistical analysis was performed using STATA software (version 13.1). Results. An higher number of circulating polymorphonuclear cells and higher CRP levels could be detected in SpA patients with respect to controls, and in PsA higher levels of ALT could be observed with respect not only to controls but also to AS. Anyway these indices were not highly elevated and often comprised within the reference intervals. As expected, HLA-B27 was associated with AS (χ2=120.1; p<0.0001). Although a slightly higher frequency of HLA-CW6 carriers was observed among patients with AS (about 6%) or PsA (about 13%) with respect to controls (about 4%), the difference was not statistically significant. Any single studied TNFA SNP was not associated with SpA diagnosis, nor with AS or PsA considered singly. The haplotypes deriving from the pairwise combinations of the five studied SNPs were also statistically inferred. The most frequent haplotypes in controls were selected as references, and only the haplotype -1031C/-308G was significantly associated with AS (p=0.015) exerting in this disease a protective role (Odds Ratio: 0.43; Confidence Interval 95%: 0.22-0.85). Three SNPs were identified in TNFRSF1A gene and among them, only the R92Q (Minor Allele Frequency- MAF=0.034) and the c.625+10A>G (MAF=0.479) were selected for their potential functional implications. Both SNPs were not associated with the presence of SpA (χ2=1.073 and p=0.300 for R92Q; χ2=4.721 and p=0.094 for c.625+10A>G), but c.625+10A>G was associated with the response to anti-TNF therapy, assessed by BASDAI score lower /equal or higher than 4 at 10 months (p=0.031). Twenty-one SNPs were identified in MEFV gene and among them, 10 with a known potential functional significance. Variant alleles were extremely rare in our population (MAF<0.025) except for R202Q (MAF=0.27). None was associated with SpA diagnosis (p>0.05). Conclusions. In conclusion the results of this study indicate the relevant role of TNF-TNFR pathway genetics in the complex network causing SpA and conditioning response to therapy. TNFA was shown to be a predisposing factor for SpA, but mainly for AS, among Italian patients, while genetics of the autoinflammatory gene MEFV appears of no impact in this setting. The haplotype resulting from TNFA-1031C/-308G, potentially associated with lower TNF-α production, exerts a protective role in AS, while the TNFRSF1A c.625+10A>G polymorphism emerged as a potential predictor of response to anti- TNFα therapy.
Introduzione. Le spondiloartriti sieronegative (SpA) sono un gruppo di malattie infiammatorie croniche risultanti da una complessa interazione tra fattori genetici (tra cui, HLA-B27 è il maggior predisponente) e ambientali. Ed è tale interazione ad indurre l'attivazione di processi autoinfiammatori e la disregolazione del sistema immunitario caratterizzanti la malattia. In molti casi, una diagnosi precoce ed un adeguato monitoraggio dell’ attività di malattia risultano difficili a causa della sovrapposizione delle caratteristiche cliniche tra le diverse forme. Il ritardo nella diagnosi e conseguentemente nel trattamento, è inoltre dovuto al fatto che, gli indici d’infiammazione comunemente utilizzati nella pratica clinica, la velocità di eritrosedimentazione (VES) ed la proteina C-reattiva (PCR), sono nella norma in almeno metà dei pazienti con chiara espressione dell’attività di malattia. Il ritardo nella diagnosi conferisce a questi pazienti un carico sintomatico importante ed una perdita di funzione durante gli anni di vita produttiva. Pertanto, forte attenzione è attualmente rivolta all’identificazione di marcatori biochimici e genetici utili alla diagnosi e di fattori prognostici necessari a valutare l'efficacia del trattamento. Tra i fattori genetici predisponenti, è noto il ruolo di HLA-B27, che contribuisce però solo per il 20-30% all'ereditarietà totale, mentre il complesso maggiore di istocompatibilità (MHC) rappresenta circa il 40-50% del rischio genetico di sviluppare la patologia. Questo dato ha suggerito il probabile coinvolgimento di altri geni nel meccanismo patogenetico. Studi di associazione genetica hanno permesso di identificare un certo numero di altri geni, associati alla patologia, sia nel locus MHC che in altri loci. In questo contesto, di grande interesse è lo studio della genetica di TNF-α, considerato il ruolo di tale citochina nel propagare e perdurare dell'infiammazione. Sebbene numerosi studi abbiano dimostrato l’associazione tra i polimorfismi di geni coinvolti nella via del segnale del TNF-α (es. TNFA, TNFSF15, TNFR1 e TRADD) e la patologia di SpA, i risultati sono discordanti. Di grande interesse sono anche le varianti del MEFV gene, coinvolto nella patogenesi della malattia autoinfiammatoria Febbre Mediterranea Familiare (FMF). Studi recenti hanno, infatti, dimostrato che le SpA, ed in particolare la spondilite anchilosante (AS), sono molto comuni tra i pazienti affetti da FMF e che questi pazienti possono presentarsi con AS come unica manifestazione. Questo studio, condotto su 91 pazienti e 223 controlli, provenienti da una regione italiana del Nord-Est, si propone di identificare fattori bioumorali (biochimici ed ematologici) e genetici al fine di supportare i processi diagnostici e prognostici (risposta alla terapia). In particolare, oltre ai parametri biochimici ed ematologici, è stato valutato se polimorfismi nella regione del promotore del gene TNFA, o dei geni TNFRSF1A e MEFV, possano concorrere con l’allele HLA-B27 all’aumento del rischio di sviluppare la malattia e/o nel predire la risposta agli inibitori del TNF-α. Metodi. La popolazione studiata comprendeva 91 pazienti con diagnosi di SpA (età media ± deviazione standard: 52.1 ± 12.5 anni; 57 maschi, 34 femmine) e 223 donatori di sangue (età media ± deviazione standard: 46 ± 11 anni; 146 maschi, 77 femmine) provenienti dalla Regione Veneto, una regione italiana del Nord-Est. Tra i pazienti, 36 presentavano AS e 55 artrite psoriasica (PsA), con diagnosi formulata sulla base dei criteri rispettivamente di New York e CASPAR. Il protocollo di questo studio è stato approvato dal Comitato Etico Istituzionale locale dell’Università-Azienda Ospedaliera di Padova, Italia (Prot.n. 3024P / 13), e tutti i soggetti arruolati hanno firmato un consenso informato prima di partecipare allo studio. Per ciascun soggetto arruolato, sono stati raccolti i dati demografici e fisiologici, la storia clinica e familiare. Sono stati raccolti poi, campioni di sangue, al fine di valutare l’emocromo e la VES, e di determinare i livelli di PCR, acido urico, prealbumina, alanina aminotransferasi (ALT) e glucosio. L’analisi molecolare dei geni MEFV (esoni 2,3,5 e 10) e TNFRSF1A (esoni 2,3,4 e 6) è avvenuta mediante sequenziamento diretto. La determinazione degli alleli HLA-B27 e dei polimorfismi del gene TNFA (-1031T>C;-857C>T;-376G>A;-308G>A;-238G>A) è stata condotta mediante PCR in Real-Time. La determinazione degli alleli HLA-CW6 è avvenuta mediante un test genetico molecolare CE-IVD, disponibile in commercio, che adotta la tecnologia microarray. L’analisi statistica è stata effettuata utilizzando il software STATA (versione 13.1). Risultati. Un maggior numero di cellule polimorfonucleate circolanti e livelli di PCR più elevati sono stati rilevati nei pazienti affetti da SpA rispetto ai controlli. Inoltre, i pazienti affetti da PsA hanno mostrato livelli più elevati di ALT, non solo rispetto ai controlli, ma anche rispetto a pazienti affetti da AS. In ogni caso tali indici non erano molto elevati e spesso risultavano compresi entro gli intervalli di riferimento. Come atteso, gli alleli HLA-B27 sono risultati associati all’AS (χ2=120.1; p<0.0001). Sebbene una frequenza leggermente maggiore degli alleli HLA-CW6 sia stata osservata tra i pazienti con AS (circa il 6%) o PsA (circa il 13%) rispetto ai controlli (circa 4%), la differenza non è risultata essere statisticamente significativa. Nessuno dei polimorfismi del gene TNFA è risultato singolarmente associato alla diagnosi SpA, né a quella di AS o PsA, se valutate indipendentemente. Sono stati, poi, statisticamente dedotti gli aplotipi derivanti dalle coppie di combinazioni dei cinque polimorfismi studiati. Gli aplotipi più frequenti nei controlli sono stati selezionati come aplotipi di riferimento, e solo l’aplotipo -1031C/-308G è risultato significativamente associato con l’AS (p=0.015) esercitando in questa malattia un ruolo protettivo (odds ratio: 0.43; intervallo di confidenza al 95%: 0.22- 0.85). Tre polimorfismi sono stati identificati nel gene TNFRSF1A e tra questi, solo i polimorfismi R92Q (Frequenza dell’allele minore- MAF = 0.034) e c.625 + 10A> G (MAF = 0.479) sono stati selezionati a causa del potenziale ruolo funzionale. Entrambi i polimorfismi non sono risultati associati con la diagnosi di SpA (χ2 = 1.073 e p = 0.300 per R92Q; χ2 = 4.721 e p = 0.094 per c.625 + 10A> G). Il polimorfismo c.625 + 10A> G è però, risultato essere associato con la risposta alla terapia con anti-TNF, valutato sulla base di un punteggio BASDAI inferiore / uguale o superiore a 4, a 10 mesi dall’inizio della terapia (p = 0.031). Ventuno polimorfismi sono stati identificati nel gene MEFV e tra questi, 10 noti per il potenziale significato funzionale. Tali varianti alleliche sono risultate estremamente rare nella nostra popolazione (MAF <0.025) ad eccezione di R202Q (MAF = 0.27). Nessun polimorfismo è risultato essere associato con la diagnosi SpA (p> 0.05). Conclusioni. In conclusione, i risultati di questo studio suggeriscono il ruolo rilevante della genetica della via del segnale TNF-TNFR nel complesso sistema che induce la patogenesi di SpA e condiziona la risposta alla terapia. Il gene TNFA, nella popolazione oggetto di studio, si è dimostrato un fattore predisponente per lo sviluppo di SpA, ma soprattutto di AS. Al contrario, la genetica del gene MEFV non sembra mostrare alcun impatto in questo gruppo di malattie. L'aplotipo TNFA-1031C/-308G, potenzialmente associato alla produzione di livelli più bassi di TNF-α, sembra esercitare un ruolo protettivo nella patogenesi di AS, mentre è emerso che il polimorfismo c.625 TNFRSF1A + 10A> G costituisce un potenziale fattore predittivo di risposta alla terapia con anti-TNFα.

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

Apoptosis, or programmed cell death, is an essential process in all multi-cellular organisms. It is indispensable to an organismâ s survival, preventing the malicious propagation of DNA damage and pathogenic alterations, through the clean disposal of afflicted cells. The BAD/tBID/BAK pathway is a portion of the apoptosis molecular pathway, albeit an important pathway since it is known to be deregulated and lead to pathological ailments such as cancer. Using chemical kinetics the BAD/tBID/BAK signaling pathway is modeled as a set of (nonlinear) ordinary differential equations. A first-cut numerical analysis reveals a mechanism where BAD sensitizes a switch from tBID activation to BAK activation. The phosphorylation of BAD is shown to inhibit this sensitizing effect. All behaviors are supported by experimental data, thereby validating the model of the BAD/tBID/BAK pathway. Moreover, modeling the phosphorylation of BAD as one of two modes, some conflicting experimental data about BADâ s phosphorylation can be disentangled. Parameter values (in this case the kinetic rate constants) are prone to error or missing altogether. Chemical reaction network theory, however, provides a theoretical approach to complement the initial numerical analysis without having to specify rate constant values. We extend the global asymptotic stability and robustness results in [92] to include any complex-balanced mass-action network. This enables us to study the BAD/tBID/BAK signaling network by breaking it into two sub-networks: one with BAD and tBID, and the other with tBID and BAK. The complex-balanced BAD/tBID sub-network is shown to possess a unique steady state which is globally asymptotically stable. This verifies the simple and dynamically well-behaved exchange of BAD for Bcl-2 proteins which guard against tBID activation. The second sub-network, tBID/BAK, is formulated as a complex-balanced network with a perturbation representing the reaction of tBID catalyzing the activation of BAK. Our theoretical results produce a non-conservative, though state-dependent, condition which can be used to prove global convergence to a neighborhood of the unperturbed steady state. We then illustrate the biological importance of the result for tBID/BAK sub-network with an example design for a drug delivery system.
Ph. D.

Recent breakthroughs in high-throughput biotechnology have promoted the integration of multi-platform data to investigate signal transduction pathways within a cell. In order to model complicated dynamics and heterogeneity of biological pathways, sophisticated computational models are needed to address unique properties of both the biological hypothesis and the data. In this dissertation work, we have proposed and developed methods using Markov Chain Monte Carlo (MCMC) techniques to solve complex modeling problems in human cancer research by integrating multi-platform data. We focus on two research topics: 1) identification of transcriptional regulatory networks and 2) uncovering of aberrant intracellular signal transduction pathways. We propose a robust method, called GibbsOS, to identify condition specific gene regulatory patterns between transcription factors and their target genes. A Gibbs sampler is employed to sample target genes from the marginal function of outlier sum of regression t statistic. Numerical simulation has demonstrated significant performance improvement of GibbsOS over existing methods against noise and false positive connections in binding data. We have applied GibbsOS to breast cancer cell line datasets and identified condition specific regulatory rewiring in human breast cancer. We also propose a novel method, namely Gibbs sampler to Infer Signal Transduction (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. By converting predefined potential functions into a Gibbs distribution, GIST estimates edge directions by learning the distribution of linear signaling pathway structures. Through the sampling process, the algorithm is able to infer signal transduction directions which are jointly determined by both gene expression and network topology. We demonstrate the advantage of the proposed algorithms on simulation data with respect to different settings of noise level in gene expression and false-positive connections in protein-protein interaction (PPI) network. Another major contribution of the dissertation work is that we have improved traditional perspective towards understanding aberrant signal transductions by further investigating structural linkage of signaling pathways. We develop a method called Structural Organization to Uncover pathway Landscape (SOUL), which emphasizes on modularized pathways structures from reconstructed pathway landscape. GIST and SOUL provide a very unique angle to computationally model alternative pathways and pathway crosstalk. The proposed new methods can bring insight to drug discovery research by targeting nodal proteins that oversee multiple signaling pathways, rather than treating individual pathways separately. A complete pathway identification protocol, namely Infer Modularization of PAthway CrossTalk (IMPACT), is developed to bridge downstream regulatory networks with upstream signaling cascades. We have applied IMPACT to breast cancer treated patient datasets to investigate how estrogen receptor (ER) signaling pathways are related to drug resistance. The identified pathway proteins from patient datasets are well supported by breast cancer cell line models. We hypothesize from computational results that HSP90AA1 protein is an important nodal protein that oversees multiple signaling pathways to drive drug resistance. Cell viability analysis has supported our hypothesis by showing a significant decrease in viability of endocrine resistant cells compared with non-resistant cells when 17-AAG (a drug that inhibits HSP90AA1) is applied. We believe that this dissertation work not only offers novel computational tools towards understanding complicated biological problems, but more importantly, it provides a valuable paradigm where systems biology connects data with hypotheses using computational modeling. Initial success of using microarray datasets to study endocrine resistance in breast cancer has shed light on translating results from high throughput datasets to biological discoveries in complicated human disease studies. As the next generation biotechnology becomes more cost-effective, the power of the proposed methods to untangle complicated aberrant signaling rewiring and pathway crosstalk will be finally unleashed.
Ph. D.

Research into the synthesis of sequential, poly(n-butyl acrylate), PnBA/polystyrene, PS, IPN microgel (μ-gel) dispersions in organic media was performed. Poly(styreneco- divinylbenzene), PSIDVB (0, 1, 5 & 10 weight % DVB), particles were synthesised by emulsion copolymerisation and these microgels were characterised by dynamic mechanical thermal analysis (DMT A), gel permeation chromatography and diffuse reflectance Fourier transform infrared (DRIFT) spectroscopy.

The ultimate goal of the modern wireless communication industry is the full integration of digital, analog, and radio frequency (RF) functions. The most successful solution for such demands has been complementary metal oxide semiconductor (CMOS) technology, thanks to its cost-effective material and great versatility. Power amplifier (PA), the biggest bottleneck to integrate in a single-chip transceiver in wireless communications, significantly influences overall system performance. Recent advanced wireless communication systems demand a power amplifier that can simultaneously support different communication standards. A fully integrated single-chip tunable CMOS power amplifier is the best solution in terms of the cost and level of integration with other functional blocks of an RF transceiver. This work, for the first time, proposes a fully integrated band-switchable RF power amplifier by using a novel approach towards switching the matching networks. In this approach, which is called Bit Optimized Reconfigurable Network (BORN), two matching networks which can be controlled by digital bits will provide three operating frequency bands for the power amplifier. In order to implementing the proposed BORN PA, a robust high-power RF switch is presented by using resistive body floating technique and 6-terminal triple-well NMOS. The proposed BORN PA delivers measured saturated output power (Psat) of 21.25/22.25/ 23.0dBm at 960MHz/1317MHz/1750MHz, respectively. Moreover, the proposed BORN PA provides respective 3-dB bandwidth of 400MHz/425MHz/550MHz, output 1-dB compression point (P1dB) of 19.5dBm/20.0dBm/21.0dBm, and power-added efficiency (PAE) of 9/11/13% at three targeted frequency bands, respectively. The promising results show that the proposed BORN PA can be a practical solution for RF multiband applications in terms of the cost and level of integration with other functional blocks of an RF transceiver.

Colorectal cancer (CRC) is one of the most frequent malignancies affecting western societies. Currently, the gold standard of CRC treatment is cetuximab, a monoclonal antibody, alone or in combination with chemotherapy. Not all patients positively respond to cetuximab: the analysis of KRAS mutational status at tumor site, a highly invasive analysis, is the only universally accepted genetic predictor for patient s response. However, some KRAS wild type patients, potential good responders, don t benefit from this therapy. To overcome these obstacles, research is focusing on the identification of new biomarkers detectable in circulating blood or other body fluids that can be used for diagnosis as well as for predicting the response to certain therapies. miRNAs, small RNA molecules involved in all aspects of cellular metabolism through regulation of gene expression, have been identified as new biomarkers for many diseases and cancers, including CRC. On the other hand, the scientific research is investigating on new molecules providing high specificity for the key players of the main cellular pathway affected in cancer. The main pathway involved in CRC is MAPK/ERK signaling pathway, which members are good targets for designing new specific inhibitors that could help to overcome the problems related to non-responsive patients to EGFR-targeted therapy. This thesis is focused on the relationship between the response to certain drugs and miRNA transcriptome changes in CRC human cellular models, based on KRAS mutational status. We profiled the expression of 667 miRNAs in 2 human CRC cell lines (Caco-2, KRAS wild type, and HCT-116, KRAS mutated), and 745 miRNAs in 3 CRC cell lines (Caco-2, HCT-116 and SW-620, another KRAS mutated cell line) after treatment with cetuximab and three specific inhibitors of MAPK pathway, respectively. Our aim was the identification of typical miRNA transcription profiles associated to cetuximab response, as well as the investigation on the global involvement of miRNAs within MAPK/ERK pathway. In the first analysis we identified substantially unique subsets of differentially expressed miRNAs in the sensitive cell line compared to the resistant one. Global network functional analysis on their targets suggested a role of these miRNAs in cancer related processes and identified hubs involved in EGFR internalization. In the second analysis we identified six differentially expressed miRNAs, that we have demonstrated to be involved in cell proliferation, migration, apoptosis, and to globally affect the regulation circuits centered on MAPK/ERK signaling. We evaluated the expression of the main candidate miRNAs identified in both studies in biopsies from CRC patients, previously categorized for their KRAS status: two miRNAs from the first study (miR-146b-3p and miR-486-5p) and four from the second (miR-92a-1*, miR-135b*, miR-372, miR-720) resulted highly expressed in biopsies from CRC patients than in normal controls. Moreover, the last four miRNAs are also overexpressed in CRC patients with mutated KRAS than in wild-type genotypes. The identification of miRNAs, which expression is linked to the efficacy of therapy, should help to predict the patients response to treatment and possibly lead to a better understanding of the molecular mechanisms of drug response. Our results contribute to deepen current knowledge on some features MAPK/ERK pathway, pinpointing new oncomiRs in CRC and allowing their translation into clinical practice and CRC therapy. Data shown in this thesis were published in 2010 and 2012 (Ragusa M, Majorana A, Statello L, et al. Specific alterations of microRNA transcriptome and global network structure in colorectal carcinoma after cetuximab treatment. Mol Cancer Ther. 2010 Dec; 9:3396-409; Ragusa M, Statello L, Maugeri M, et al. Specific alterations of the microRNA transcriptome and global network structure in colorectal cancer after treatment with MAPK/ERK inhibitors. J Mol Med (Berl). 2012 Jun 4).

In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodologies focused on extracting information at the pathway rather than the single gene level. First, we developed Gene Set Variation Analysis (GSVA), a method that facilitates the organization and condensation of gene expression profiles into gene sets. GSVA enables pathway-centric downstream analyses of microarray and RNA-seq gene expression data. The method estimates sample-wise pathway variation over a population and allows for the integration of heterogeneous biological data sources with pathway-level expression measurements. To illustrate the features of GSVA, we applied it to several use-cases employing different data types and addressing biological questions. GSVA is made available as an R package within the Bioconductor project. Secondly, we developed a pathway-centric genome-based strategy to reposition drugs in type 2 diabetes (T2D). This strategy consists of two steps, first a regulatory network is constructed that is used to identify disease driving modules and then these modules are searched for compounds that might target them. Our strategy is motivated by the observation that disease genes tend to group together in the same neighborhood forming disease modules and that multiple genes might have to be targeted simultaneously to attain an effect on the pathophenotype. To find potential compounds, we used compound exposed genomics data deposited in public databases. We collected about 20,000 samples that have been exposed to about 1,800 compounds. Gene expression can be seen as an intermediate phenotype reflecting underlying dysregulatory pathways in a disease. Hence, genes contained in the disease modules that elicit similar transcriptional responses upon compound exposure are assumed to have a potential therapeutic effect. We applied the strategy to gene expression data of human islets from diabetic and healthy individuals and identified four potential compounds, methimazole, pantoprazole, bitter orange extract and torcetrapib that might have a positive effect on insulin secretion. This is the first time a regulatory network of human islets has been used to reposition compounds for T2D. In conclusion, this thesis contributes with two pathway-centric approaches to important bioinformatic problems, such as the assessment of biological function and in silico drug repositioning. These contributions demonstrate the central role of pathway-based analyses in interpreting high-throughput genomics data.
En l'última dècada, la biologia molecular ha evolucionat des d'una perspectiva reduccionista cap a una perspectiva a nivell de sistemes que intenta desxifrar les complexes interaccions entre els components cel•lulars. Amb l'aparició de les tecnologies d'alt rendiment actualment és possible interrogar genomes sencers amb una resolució sense precedents. La dimensió i la naturalesa desestructurada d'aquestes dades ha posat de manifest la necessitat de desenvolupar noves eines i metodologies per a convertir aquestes dades en coneixement biològic. Per contribuir a aquest repte hem explotat l'abundància de dades genòmiques procedents d'instruments d'alt rendiment i disponibles públicament, i hem desenvolupat mètodes bioinformàtics focalitzats en l'extracció d'informació a nivell de via molecular en comptes de fer-ho al nivell individual de cada gen. En primer lloc, hem desenvolupat GSVA (Gene Set Variation Analysis), un mètode que facilita l'organització i la condensació de perfils d'expressió dels gens en conjunts. GSVA possibilita anàlisis posteriors en termes de vies moleculars amb dades d'expressió gènica provinents de microarrays i RNA-seq. Aquest mètode estima la variació de les vies moleculars a través d'una població de mostres i permet la integració de fonts heterogènies de dades biològiques amb mesures d'expressió a nivell de via molecular. Per il•lustrar les característiques de GSVA, l'hem aplicat a diversos casos usant diferents tipus de dades i adreçant qüestions biològiques. GSVA està disponible com a paquet de programari lliure per R dins el projecte Bioconductor. En segon lloc, hem desenvolupat una estratègia centrada en vies moleculars basada en el genoma per reposicionar fàrmacs per la diabetis tipus 2 (T2D). Aquesta estratègia consisteix en dues fases: primer es construeix una xarxa reguladora que s'utilitza per identificar mòduls de regulació gènica que condueixen a la malaltia; després, a partir d'aquests mòduls es busquen compostos que els podrien afectar. La nostra estratègia ve motivada per l'observació que els gens que provoquen una malaltia tendeixen a agrupar-se, formant mòduls patogènics, i pel fet que podria caldre una actuació simultània sobre múltiples gens per assolir un efecte en el fenotipus de la malaltia. Per trobar compostos potencials, hem usat dades genòmiques exposades a compostos dipositades en bases de dades públiques. Hem recollit unes 20.000 mostres que han estat exposades a uns 1.800 compostos. L'expressió gènica es pot interpretar com un fenotip intermedi que reflecteix les vies moleculars desregulades subjacents a una malaltia. Per tant, considerem que els gens d'un mòdul patològic que responen, a nivell transcripcional, d'una manera similar a l'exposició del medicament tenen potencialment un efecte terapèutic. Hem aplicat aquesta estratègia a dades d'expressió gènica en illots pancreàtics humans corresponents a individus sans i diabètics, i hem identificat quatre compostos potencials (methimazole, pantoprazole, extracte de taronja amarga i torcetrapib) que podrien tenir un efecte positiu sobre la secreció de la insulina. Aquest és el primer cop que una xarxa reguladora d'illots pancreàtics humans s'ha utilitzat per reposicionar compostos per a T2D. En conclusió, aquesta tesi aporta dos enfocaments diferents en termes de vies moleculars a problemes bioinformàtics importants, com ho son el contrast de la funció biològica i el reposicionament de fàrmacs "in silico". Aquestes contribucions demostren el paper central de les anàlisis basades en vies moleculars a l'hora d'interpretar dades genòmiques procedents d'instruments d'alt rendiment.

Quiescence is a non-proliferative cellular state that is critical to tissue repair and regeneration. Although often described as the G0 phase, quiescence is not a single homogeneous state. As cells remain quiescent for longer durations, they move progressively deeper and display a reduced sensitivity to growth signals. Deep quiescent cells, unlike senescent cells, can still re-enter the cell cycle under physiological conditions. Mechanisms controlling quiescence depth are poorly understood, representing a currently underappreciated layer of complexity in growth control. Here, we show that the activation threshold of a Retinoblastoma (Rb)-E2F network switch controls quiescence depth. Particularly, deeper quiescent cells feature a higher E2F-switching threshold and exhibit a delayed traverse through the restriction point (R-point). We further show that different components of the Rb-E2F network can be experimentally perturbed, following computer model predictions, to coarse-or fine-tune the E2F-switching threshold and drive cells into varying quiescence depths.

Dynamical structure functions were developed as a partial structure representation of linear time-invariant systems to be used in the reconstruction of biological networks. Dynamical structure functions contain more information about structure than a system's transfer function, while requiring less a priori information for reconstruction than the complete computational structure associated with the state space realization. Early sufficient conditions for network reconstruction with dynamical structure functions severely restricted the possible applications of the reconstruction process to networks where each input independently controls a measured state. The first contribution of this thesis is to extend the previously established sufficient conditions to incorporate both necessary and sufficient conditions for reconstruction. These new conditions allow for the reconstruction of a larger number of networks, even networks where independent control of measured states is not possible. The second contribution of this thesis is to extend the robust reconstruction algorithm to all reconstructible networks. This extension is important because it allows for the reconstruction of networks from real data, where noise is present in the measurements of the system. The third contribution of this thesis is a Matlab toolbox that implements the robust reconstruction algorithm discussed above. The Matlab toolbox takes in input-output data from simulations or real-life perturbation experiments and returns the proposed Boolean structure of the network. The final contribution of this thesis is to increase the applicability of dynamical structure functions to more than just biological networks by applying our reconstruction method to wireless communication networks. The reconstruction of wireless networks produces a dynamic interference map that can be used to improve network performance or interpret changes of link rates in terms of changes in network structure, enabling novel anomaly detection and security schemes.

Dans ce travail nous avons élaboré une nouvelle méthode de l'analyse de réseau à définir des membres possibles des voies moléculaires qui sont important pour ce phénotype en utilisant la « hit-liste » des expériences « omics » qui travaille dans le réseau intégré (le réseau comprend des interactions protéine-protéine, de transcription, l’acide ribonucléique micro-l’acide ribonucléique messager et celles métaboliques). La méthode tire des sous-réseaux qui sont construit des voies de quatre types les plus courtes (qui ne se composent des interactions protéine-protéine, ayant au minimum une interaction de transcription, ayant au minimum une interaction l’acide ribonucléique micro-l’acide ribonucléique messager, ayant au minimum une interaction métabolique) entre des hit –gènes et des soi-disant « exécuteurs terminaux » - les composants biologiques qui participent à la réalisation du phénotype finale (s’ils sont connus) ou entre les hit-gènes (si « des exécuteurs terminaux » sont inconnus). La méthode calcule la valeur de la centralité de chaque point culminant et de chaque voie dans le sous-réseau comme la quantité des voies les plus courtes trouvées sur la route précédente et passant à travers le point culminant et la voie. L'importance statistique des valeurs de la centralité est estimée en comparaison avec des valeurs de la centralité dans les sous-réseaux construit des voies les plus courtes pour les hit-listes choisi occasionnellement. Il est supposé que les points culminant et les voies avec les valeurs de la centralité statistiquement signifiantes peuvent être examinés comme les membres possibles des voies moléculaires menant à ce phénotype. S’il y a des valeurs expérimentales et la P-valeur pour un grand nombre des points culminant dans le réseau, la méthode fait possible de calculer les valeurs expérimentales pour les voies (comme le moyen des valeurs expérimentales des points culminant sur la route) et les P-valeurs expérimentales (en utilisant la méthode de Fischer et des transpositions multiples).A l'aide de la méthode masterPATH on a analysé les données de la perte de fonction criblage de l’acide ribonucléique micro et l'analyse de transcription de la différenciation terminal musculaire et les données de la perte de fonction criblage du procès de la réparation de l'ADN. On peut trouver le code initial de la méthode si l’on suit le lien https://github.com/daggoo/masterPATH
In this work we developed a new exploratory network analysis method, that works on an integrated network (the network consists of protein-protein, transcriptional, miRNA-mRNA, metabolic interactions) and aims at uncovering potential members of molecular pathways important for a given phenotype using hit list dataset from “omics” experiments. The method extracts subnetwork built from the shortest paths of 4 different types (with only protein-protein interactions, with at least one transcription interaction, with at least one miRNA-mRNA interaction, with at least one metabolic interaction) between hit genes and so called “final implementers” – biological components that are involved in molecular events responsible for final phenotypical realization (if known) or between hit genes (if “final implementers” are not known). The method calculates centrality score for each node and each path in the subnetwork as a number of the shortest paths found in the previous step that pass through the node and the path. Then, the statistical significance of each centrality score is assessed by comparing it with centrality scores in subnetworks built from the shortest paths for randomly sampled hit lists. It is hypothesized that the nodes and the paths with statistically significant centrality score can be considered as putative members of molecular pathways leading to the studied phenotype. In case experimental scores and p-values are available for a large number of nodes in the network, the method can also calculate paths’ experiment-based scores (as an average of the experimental scores of the nodes in the path) and experiment-based p-values (by aggregating p-values of the nodes in the path using Fisher’s combined probability test and permutation approach). The method is illustrated by analyzing the results of miRNA loss-of-function screening and transcriptomic profiling of terminal muscle differentiation and of ‘druggable’ loss-of-function screening of the DNA repair process. The Java source code is available on GitHub page https://github.com/daggoo/masterPATH

The basis of developmental biology lies in the idea of when and how cells decide to divide or to differentiate. Previous studies have established several signaling pathways that determine cell fate decisions, including Notch, Wingless, Hedgehog, Bone morphogenetic protein, and Fibroblast growth factor. Signaling converges on transcriptional factors that regulate gene expression. In mouse embryonic stem cells, I explored how pluripotency and differentiation are regulated through opposing actions of beta-catenin-mediated canonical Wnt signaling, and the mechanisms underlying Sonic hedgehog signaling in generating progenitor cells in the ventral neural tube.

With a narrowly defined unemployment rate of 26.5% in South Africa, this paper contributes to the salient task of exploring a job creation programme in a high growth sector of the global coffee production network, namely the production of espresso based beverages by baristas for sale in restaurants, cafes, and hotels. Situated in the qualitative paradigm with an inductive research agenda, this research utilises the case study method to explore Ground UP, a skills training programme of a local not-for-profit, which provides barista skills training that unemployed people can use to become economically active in context of the specialty coffee industry. By applying the concepts of upgrading in the context of a global production network and a descriptive focus on both the Ground UP programme, as well as the characteristics and dynamics specialty coffee industry in South Africa, this research examines the potential for this industry to offer a pathway out of poverty. Applying a theoretical lens to this descriptive case study, the theme of governance features strongly, and the analysis reveals that Ground UP, as an agent of palliative development, can help beneficiaries to access a pathway out of poverty. It is also argued that the extent to which they will be able to capture the gains in the specialty coffee industry in the longer term will be impacted on external factors and other key players in the industry as well as their positioning in a global production network.

Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.

Ricerca sperimentale sulla valutazione della performance nei servizi pubblici. Il lavoro è articolato in due parti: nella prima, dopo una ricognizione internazionale della letteratura e delle principali esperienze della misurazione, gestione e valutazione della performance, viene costruito e argomentato un framework sperimentale per la valutazione della performance dei network pubblici. La seconda sezione presenta uno studio di caso sperimentale per validare il framework. Lo studio di caso ha in oggetto il policy network per la gestione del PTDAR dei pazienti con lesione midollare. Viene considerato come network il territorio regionale e unità di analisi è collocata a livello micro in un unità dipartimentale: Unità Spinale Unipolare. Il framework risulta consistente e promettente per la valutazione dei policy network per le cure integrate.
Experimental case study design for the performance evaluation of health care public services. The thesis is broken down into two main parts: the first part deals with the performance framework construction wherein the international theoretical literature and experiences realized are retrieved and discussed. The second part deliveries an experimental case study design to validate the framework proposed. The case studies focuses on the integrated care pathway for persons whit spinal cord injury/dysfunction. The policy network sets out the Regional policy making and the unity of analysis is a Spinal Unit specialized centre. The framework proved to be consistent and adapted for evaluating policy network for integrated care.

In this thesis we developed two approaches to study positive selection and genetic adaptation in the human genome. Both approaches are based on applications of network theory. In the first approach, we studied how the signals of selection are distributed among the genes of a metabolic pathway. We use a network representation of the Asparagine N-Glycosylation pathway, and determine if given positions are more likely to be involved in selection events. We determined a different distribution of signals between the upstream part of this pathway, which has a linear structure and is involved in a conserved process, and the downstream part of the pathway, which has a complex network structure and is involved in adaptation to the environment. In the second approach, we applied a network representation of the set of genotypes observed in a population (Genotype Network) to next-generation sequencing data. The main result is a genome-wide picture of how the populations of the 1000 Genomes dataset have explored the genotype space. We found that the genotype networks of coding regions tend to be more connected and more expanded in the space than non coding regions, and that simulated sweeps have similar patterns compared to simulated neutral regions.
En esta tesis hemos desarrollado dos métodos para estudiar los patrones de selección positiva y adaptación genética en el genoma humano. Ambos métodos se basan en aplicaciones de teoría de redes. En la primera aplicación hemos investigado cómo las señales de selección están distribuidas a lo largo de una ruta metabólica. Hemos utilizado una representación de la ruta de N-Glicosilación, para estudiar si determinadas posiciones tienen más probabilidades de estar implicadas en eventos de selección positiva. Hemos comparado la distribución de las señales de selección entre la primera parte de la ruta metabólica, que tiene una estructura muy lineal y está involucrada en un proceso conservado, y la segunda parte de la ruta, que tiene una estructura de redes compleja y está involucrada en adaptación al ambiente. En la segunda aplicación hemos aplicado el concepto de redes de genotipos (Genotype Networks) a datos de secuencia de nueva generación. El resultado es un análisis completo de cómo las poblaciones de 1000 Genomas han explorado el espacio de genotipo. Las redes de genotipos de regiones codificantes suelen estar más conectadas y más expandidas que las regiones no-codificantes. Además, por medio de simulaciones hemos observado los patrones esperados para eventos de selección positiva.

In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway.

Metabolic pathway comparison between different species is important to discover the differences in a metabolic function developed during the evolutionary process. This kind of analysis may allow the detection of important information useful also in drug engineering and medical science. In this thesis we propose a method for metabolic pathways comparison based on their representation as sets and multisets of chemical reactions. The information is taken from the KEGG database because it has a standardised representation of each pathway in the different organisms. The pathway comparison technique is then used in the context of metabolic networks comparison in order to solve the problems due to the size of the compared networks. The proposed methods have been implemented in Java as part of a tool for metabolic networks comparison.

Genes can be associated in numerous ways, e.g. by co-expression in micro-arrays, co-regulation in operons and regulons or co-localization on the genome. Association of genes often indicates that they contribute to a common biological function, such as a pathway. The aim of this thesis is to predict metabolic pathways from associated enzyme-coding genes. The prediction approach developed in this work consists of two steps: First, the reactions are obtained that are carried out by the enzymes coded by the genes. Second, the gaps between these seed reactions are filled with intermediate compounds and reactions. In order to select these intermediates, metabolic data is needed. This work made use of metabolic data collected from the two major metabolic databases, KEGG and MetaCyc. The metabolic data is represented as a network (or graph) consisting of reaction nodes and compound nodes. Interme- diate compounds and reactions are then predicted by connecting the seed reactions obtained from the query genes in this metabolic network using a graph algorithm.

In large metabolic networks, there are numerous ways to connect the seed reactions. The main problem of the graph-based prediction approach is to differentiate biochemically valid connections from others. Metabolic networks contain hub compounds, which are involved in a large number of reactions, such as ATP, NADPH, H2O or CO2. When a graph algorithm traverses the metabolic network via these hub compounds, the resulting metabolic pathway is often biochemically invalid.

In the first step of the thesis, an already existing approach to predict pathways from two seeds was improved. In the previous approach, the metabolic network was weighted to penalize hub compounds and an extensive evaluation was performed, which showed that the weighted network yielded higher prediction accuracies than either a raw or filtered network (where hub compounds are removed). In the improved approach, hub compounds are avoided using reaction-specific side/main compound an- notations from KEGG RPAIR. As an evaluation showed, this approach in combination with weights increases prediction accuracy with respect to the weighted, filtered and raw network.

In the second step of the thesis, path finding between two seeds was extended to pathway prediction given multiple seeds. Several multiple-seed pathay prediction approaches were evaluated, namely three Steiner tree solving heuristics and a random-walk based algorithm called kWalks. The evaluation showed that a combination of kWalks with a Steiner tree heuristic applied to a weighted graph yielded the highest prediction accuracy.

Finally, the best perfoming algorithm was applied to a microarray data set, which measured gene expression in S. cerevisiae cells growing on 21 different compounds as sole nitrogen source. For 20 nitrogen sources, gene groups were obtained that were significantly over-expressed or suppressed with respect to urea as reference nitrogen source. For each of these 40 gene groups, a metabolic pathway was predicted that represents the part of metabolism up- or down-regulated in the presence of the investigated nitrogen source.

The graph-based prediction of pathways is not restricted to metabolic networks. It may be applied to any biological network and to any data set yielding groups of associated genes, enzymes or compounds. Thus, multiple-end pathway prediction can serve to interpret various high-throughput data sets.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

Recent large-scale genome-wide studies for many human disorders have identified associations with numerous genetic variants. The biological interpretation of these variants presents a major challenge. In particular, the identification of biological pathways underlying the association could provide crucial insights into the disease aetiologies. In this thesis, I used functional genomics approaches to increase our understanding of neuropsychiatric and neurodevelopmental disorders. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them.

Il Diabete, modello paradigmatico delle malattie croniche, sta assumendo negli ultimi anni le proporzioni di una pandemia, che non ha intenzione di arrestarsi, ma del quale, con l’aumento dei fattori di rischio, aumentano prevalenza e incidenza. Secondo stime autorevoli il numero delle persone con diabete nel 2035 aumenterà fino a raggiungere i 382 milioni di casi. Una patologia complessa che richiede lo sforzo di una vasta gamma di professionisti, per ridurre in futuro in maniera significativa i costi legati a questa patologia e nel contempo mantenere e addirittura migliorare gli standard di cura. Una soluzione è rappresentata dall'impiego delle ICT, Information and Communication Technologies. La continua innovazione tecnologica dei medical device per diabetici lascia ben sperare, dietro la spinta di capitali sempre più ingenti che iniziano a muoversi in questo mercato del futuro. Sempre più device tecnologicamente avanzati, all’avanguardia e performanti, sono a disposizione del paziente diabetico, che può migliorare tutti processi della cura, contenendo le spese. Di fondamentale importanza sono le BAN reti di sensori e wearable device, i cui dati diventano parte di un sistema di gestione delle cure più ampio. A questo proposito METABO è un progetto ICT europeo dedicato allo studio ed al supporto di gestione metabolica del diabete. Si concentra sul miglioramento della gestione della malattia, fornendo a pazienti e medici una piattaforma software tecnologicamente avanzata semplice e intuitiva, per aiutarli a gestire tutte le informazioni relative al trattamento del diabete. Innovativo il Clinical Pathway, che a partire da un modello Standard con procedimenti semplici e l’utilizzo di feedback del paziente, viene progressivamente personalizzato con le progressive modificazioni dello stato patologico, psicologico e non solo. La possibilità di e-prescribing per farmaci e device, e-learning per educare il paziente, tenerlo sotto stretto monitoraggio anche alla guida della propria auto, la rendono uno strumento utile e accattivante.

The aim of this study was to explore how musicking (a term denoting any music related activity, see Small 1998, p. 9) could be used locally to support people with dementia and their caregivers in a sustainable manner. The data for the study came primarily from a group known as “Song Birds”, a community-based volunteer music group working with people with dementia and their caregivers in the south of England. Participant observation was combined with interviews and an extensive ethnographic study of the music and care world surrounding the group. The data was explored using a grounded theory approach investigating three time phases, “preparation for the events”, “during the events” and “in-between and after the events”. The main findings related to the lay crafting of the events and the emergence of pathways between “music and care nodes” in a local, social network. The preparatory physical and social crafting of Song Birds events created a transitional time and place that guided the participants from everyday life into their collective musicking. This crafting was essential to the success of the musicking and produced inclusive activities that considered the different capabilities of all participants. As a result of these carefully crafted events, dementia identities were temporarily displaced and relationships were transformed. The musical repertoire was an important resource in this crafting and evolved according to the participants’ changing situations. The positive musical benefits and affordances (see DeNora 2000) from such weekly events could be transferred into participants’ everyday lives through multiple music and care groups and the pathways that connected those groups which constituted a “music and care world”. Such musically fostered networks helped generate a virtuous cycle that maintained the music group as a sustainable activity. As dementia care was a long-term activity, such sustainability was important to the on-going community support for people affected by dementia. Community musicking thus allowed people affected by dementia, their relatives and friends to remain together.

Les systèmes acellulaires sont en train de devenir une puissante plateforme de biofabrication. L'optimisation de systèmes acellulaires est importante pour obtenir un rendement maximal. L'optimisation expérimentale, en laboratoire humide, est longue et coûteuse. Différents types de modélisations permettant d'optimiser la voie d'intérêt, en un temps plus court et à moindre coût, sont apparus au cours des dernières décennies. Dans cette étude, nous avons testé deux approches : systémique à travers la mise en œuvre de réseaux de neurones, et analytique à travers l™utilisation d™équations différentielles. Dans une première étape, un modèle à réseau de neurones artificiels a été construit pour prédire le flux de métabolites à travers la voie. Dans une seconde étape, une nouvelle méthodologie, appelée GC-ANN, a été développée pour sélectionner des équilibres enzymatiques optimaux, et rentables, pour des valeurs de flux plus élevées. Cette approche a permis une amélioration inattendue du flux, jusqu'à 63%, validée in vitro. Dans une troisième étape, un modèle cinétique a été construit, et l™estimation des paramètres cinétiques pour les enzymes sélectionnées a été réalisée, afin de reproduire les conditions expérimentales. Enfin, liée à l'un des produits chimiques les plus exigeants en termes de production, la voie de synthèse du malate a été modélisée avec succès dans un système acellulaires. Même si de nombreuses études ont été réalisées, la biofabrication a grande échelle n'est pas encore possible pour le malate. La combinaison du système acellulaire et de la modélisation pourrait aider à réaliser la bioproduction du malate. De manière plus générale, cette thèse explore différentes approches de modélisations mathématiques, et leurs limites, pour l'optimisation de voies métaboliques
Cell-free systems (CFS) are emerging as a powerful platform for biomanufacturing. The optimisation of the cell-free system is important to achieve maximum yield. The experimental optimisation is time-consuming and expensive. Different kinds of modelling emerged in the last decades, helping to optimise the pathway of interest in a shorter time at a low cost. In this study, we tested two approaches: systemic through the implementation of neural networks, and analytical through the use of differential equations. In the first step, an artificial neural network model was built to predict the flux through the pathway, and in the second step, a new methodology termed GC-ANN was developed to select optimum and cost-efficient enzyme balances for higher flux. This approach showed unexpected betterment of flux estimation, up to 63%. In the third step, a kinetic model was built and estimation of kinetic parameters for selected enzymes was achieved to replicate experimental conditions. Finally, linked to one of the most demanding chemicals, malate synthesis pathway was successfully modelled in the cell-free system. Even though many studies have been performed, biomanufacturing has not yet been possible for malate. The combination of the cell-free system and modelling could help achieve the biomanufacturing of malate. Overall, this thesis explores different mathematical modelling approaches, and their limits, for optimising metabolic pathways

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006.
Includes bibliographical references (p. 153-165).
Cells communicate with other cells, and process cues from their environment, via signaling pathways, in which extracellular cues trigger a cascade of information flow, causing signaling molecules to become chemically, physically or locationally modified, gain new functional capabilities, and affect subsequent molecules in the cascade, culminating in a phenotypic cellular response. Mapping the influence connections among biomolecules in a signaling cascade aids in understanding of the underlying biological process and in development of therapeutics for diseases involving aberrant pathways, such as cancer and autoimmune disease. In this thesis, we present an approach for automatically reverse-engineering the structure of a signaling pathway, from high-throughput data. We apply Bayesian network structure inference to signaling protein measurements performed in thousands of single cells, using a machine called a flow cytorneter. Our de novo reconstruction of a T-cell signaling map was highly accurate, closely reproducing the known pathway structure, and accurately predicted novel pathway connections. The flow cytometry measurements include specific perturbations of signaling molecules, aiding in a causal interpretation of the Bayesian network graph structure.
(cont.) However, this machine can measure only -4-12 molecules per cell, too few for effective coverage of a signaling pathway. To address this problem, we employ a number of biologically motivated assumptions to extend our technique to scale up from the number of molecules measured to larger models, using measurements of overlapping variable subsets. We demonstrate this approach by scaling up to a model of 11 variables, using 15 overlapping 4-variable measurements.
by Karen Sachs.
Ph.D.