Diplomová práce se zabývá studiem genové regulace v Clostridium beijerinckii NRRL B-598, pro následné odvození genové regulační sítě bakterie C. beijerinckii NRRL B-598. V teoretické části této práce je uvedena obecná nomenklatura problematiky genové regulace se zaměřením na nomenklaturu genových regulačních sítí. Následně jsou zde popsané laboratorní metody, sloužící pro získání vhodných dat popisující expresi genů. Tato data jsou základem pro studium genové regulace a návrhy genových regulačních sítí. Práce se zaměřuje především na technologii RNA-Seq a stručný popis laboratorních dat získaných ze zmíněné bakterie C. beijerinckii NRRL B-598. V praktické části se práce zabývá předzpracováním těchto surových laboratorních dat a následným studiem genové regulace se zaměřením na odvození operonů a vytvoření prvních genových regulačních sítí pomocí různých přístupů pro C. beijerinckii NRRL B-598.

La biologie des systèmes complexes est le cadre idéal pour l'interdisciplinarité. Dans cette thèse, les modèles et les théories statistiques répondent aux modèles et aux expérimentations biologiques. Nous nous sommes intéressés au cas particulier de la leucémie lymphoïde chronique à cellules B, qui est une forme de cancer des cellules du sang. Nous avons commencé par modéliser le programme génique tumoral sous-jacent à cette maladie et nous l'avons comparé au programme génique d'individus sains. Pour ce faire, nous avons introduit la notion de réseau en cascade. Nous avons ensuite démontré notre capacité à contrôler ce système complexe, en prédisant mathématiquement les effets d'une expérience d'intervention consistant à inhiber l'expression d'un gène. Cette thèse s'achève sur la perspective d'une modulation orientée, c'est-à-dire le choix d'expériences d'intervention permettant de « reprogrammer » le programme génique tumoral vers un état normal. / System biology is a well-suited context for interdisciplinary. In this thesis, statistical models and theories closely meet biological models and experiments. We focused on a specific complex system model: the chronic B-cell chronic lymphocytic leukemia disease which is a cancer of the blood cells. We started by modeling the genetic program which underlies this disease and we compared it to the healthy one. This conduced us to introduce the concept of cascade networks. We then showed our ability to control this complex system by predicting with our mathematical model the effects of a gene inhibition experiment. This thesis ends with the perspective of oriented modulation, i.e. targeted interventional experiments on genes allowing to “reprogram” the cancerous genetic program toward a healthy normal state.

In this thesis we analyze parameter optimization problems governed by linear ordinary differential equations (ODEs) and develop computationally efficient numerical methods for their solution. In addition, a series of noise-robust finite difference formulas are given for the estimation of the derivatives in the ODEs. The suggested methods have been employed to identify Gene Regulatory Networks (GRNs). GRNs are responsible for the expression of thousands of genes in any given developmental process. Network inference deals with deciphering the complex interplay of genes in order to characterize the cellular state directly from experimental data. Even though a plethora of methods using diverse conceptual ideas has been developed, a reliable network reconstruction remains challenging. This is due to several reasons, including the huge number of possible topologies, high level of noise, and the complexity of gene regulation at different levels. A promising approach is dynamic modeling using differential equations. In this thesis we present such an approach to infer quantitative dynamic models from biological data which addresses inherent weaknesses in the current state-of-the-art methods for data-driven reconstruction of GRNs. The method is computationally cheap such that the size of the network (model complexity) is no longer a main concern with respect to the computational cost but due to data limitations; the challenge is a huge number of possible topologies. Therefore we embed a filtration step into the method to reduce the number of free parameters before simulating dynamical behavior. The latter is used to produce more information about the network’s structure. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise on a 1565-gene E.coli gene regulatory network. We show the computation time over various network sizes and estimate the order of computational complexity. Results on five networks in the benchmark collection DREAM4 Challenge are also presented. Results on five networks in the benchmark collection DREAM4 Challenge are also presented and show our method to outperform the current state of the art methods on synthetic data and allows the reconstruction of bio-physically accurate dynamic models from noisy data. / I detta examensarbete analyserar vi parameteroptimeringsproblem som är beskrivna med ordinära differentialekvationer (ODEer) och utvecklar beräkningstekniskt effektiva numeriska metoder för att beräkna lösningen. Dessutom härleder vi brusrobusta finita-differens approximationer för uppskattning av derivator i ODEn. De föreslagna metoderna har tillämpats för regulatoriska gennätverk (RGN). RGNer är ansvariga för uttrycket av tusentals gener. Nätverksinferens handlar om att identifiera den komplicerad interaktionen mellan gener för att kunna karaktärisera cellernas tillstånd direkt från experimentella data. Tillförlitlig nätverksrekonstruktion är ett utmanande problem, trots att många metoder som använder många olika typer av konceptuella idéer har utvecklats. Detta beror på flera olika saker, inklusive att det finns ett enormt antal topologier, mycket brus, och komplexiteten av genregulering på olika nivåer. Ett lovande angreppssätt är dynamisk modellering från biologiska data som angriper en underliggande svaghet i den för tillfället ledande metoden för data-driven rekonstruktion. Metoden är beräkningstekniskt billig så att storleken på nätverket inte längre är huvudproblemet för beräkningen men ligger fortfarande i databegränsningar. Utmaningen är ett enormt antal av topologier. Därför bygger vi in ett filtreringssteg i metoder för att reducera antalet fria parameterar och simulerar sedan det dynamiska beteendet. Anledningen är att producera mer information om nätverkets struktur. Vi utvärderar metoden på simulerat data, och studierar dess prestanda med avseende på datastorlek och brusnivå genom att tillämpa den på ett regulartoriskt gennätverk med 1565-gen E.coli. Vi illustrerar beräkningstiden över olika nätverksstorlekar och uppskattar beräkningskomplexiteten. Resultat på fem nätverk från DREAM4 är också presenterade och visar att vår metod har bättre prestanda än nuvarande metoder när de tillämpas på syntetiska data och tillåter rekonstruktion av bio-fysikaliskt noggranna dynamiska modeller från data med brus.

Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two case studies. The first examines bright field cell image segmentation and the second examines the information complexity of multicellular patterns. In the first study we demonstrate that our proposed AIT-based algorithm provides an accurate and robust bright field cell segmentation. Cell segmentation is the process of detecting cells in microscopy images, which is usually a challenging task for bright field microscopy due to the low contrast of the images. In the second study, which is the primary contribution of this dissertation, we employ an AIT-based algorithm to quantify the complexity of information content that arises during the development of multicellular organisms. We simulate multicellular organism development by coupling the Gene Regulatory Networks (GRN) within an epithelial field. Our results show that the configuration of GRNs influences the information complexity in the resultant multicellular patterns.

No description available.

No description available.

No description available.

No description available.

The field of functional genomics aims to understand the complex relationship between genotype and phenotype by integrating genome-wide approaches, such as transcriptomics, proteomics, and metabolomics. Large-scale "-omics" research has been made widely possible by the advent of high-throughput techniques, such as next-generation sequencing and mass-spectrometry. The vast data generated from such studies provide a wealth of information on the biological dynamics underlying phenotypes. Though functional genomics approaches are used extensively in human disease research, their use also spans organisms as miniscule as mycoplasmas to as great as sperm whales. In particular, functional genomics is instrumental in agricultural advancements for the improvement of productivity and sustainability in crop and livestock production. Improvement in soybean production is especially imperative, as soybeans are a primary source of oil and protein for human and livestock consumption, respectively. The research presented here employs functional genomics approaches – transcriptomics and metabolomics – to discern the transcriptional regulation and metabolic events underlying two economically important agronomic traits in soybean: seed phytic acid content and Soybean mosaic virus resistance. At normal levels, seed phytic acid content inhibits mineral absorption in humans and livestock, acting as an antinutrient and contributing to phosphorus pollution; however, the development of low phytic acid soybeans has helped mitigate these issues, as their seeds increase nutrient bioavailability and reduce environmental impact. Despite these desirable qualities, low phytic acid soybeans exhibit poor seed performance, which negatively affects germination rates and yield and has prevented their large-scale commercial production. Thus, part of the focus of this research was investigating the effects of mutations conferring the low phytic acid phenotype on seed germination. Comparative studies between low and normal phytic acid soybean seeds were carried out and revealed distinct differences in metabolite profiles and in the transcriptional regulation of biological pathways that may be vital for successful seed germination. The final part of this research concerns Rsv3-mediated extreme resistance, a unique mode of resistance that is effective against the most virulent strains of Soybean mosaic virus. The molecular mechanisms governing this type of resistance are poorly characterized. Therefore, the research presented here attempts to elucidate the regulatory elements responsible for the induction of the Rsv3-mediated extreme resistance response. Utilizing a comparative transcriptomic time series approach on Soybean mosaic virus-inoculated Rsv3 (resistant) and rsv3 (susceptible) soybean lines, this final study provides gene candidates putatively functioning in the regulation of biological pathways demonstrated to be crucial for Rsv3-mediated resistance. / Doctor of Philosophy / Soybeans are a crop of great economic importance, being a primary source of oil and protein for human and livestock consumption, respectively. Increasing demand for soybean calls for improvement in its production. An emerging field that has had tremendous impact on this endeavor is the field of functional genomics. Functional genomics approaches generate large-scale biological data that can aid in discerning how specific processes are regulated and controlled in an organism. The research presented in this work utilizes functional genomics approaches to elucidate the biological mechanisms underlying two economically important traits in soybean: seed phytic acid content and Soybean mosaic virus resistance. Phytic acid is a compound found in soybean seeds that causes nutrient deficiencies and phosphorus pollution. Soybeans with reduced to phytic acid content have been developed to mitigate these problems; they have poor seed germination and emergence. The studies in this work employ functional genomics approaches to compare unique sets of low and normal phytic acid soybeans to help establish the relationship between seed phytic acid content and seed performance. These studies resulted in new and promising hypotheses for future studies on investigating the low phytic acid trait. The final focus of this work used a functional genomics approach to discern the molecular mechanisms underlying a unique mode of resistance to Soybean mosaic virus. The study identified genes in soybean that are potentially critical to resistance against Soybean mosaic virus.

Computational biology currently faces challenges in a big data world with thousands of data samples across multiple disease types including cancer. The challenging problem is how to extract biologically meaningful information from large-scale genomic data. Next-generation Sequencing (NGS) can now produce high quality data at DNA and RNA levels. However, in cells there exist a lot of non-specific (background) signals that affect the detection accuracy of true (foreground) signals. In this dissertation work, under Bayesian framework, we aim to develop and apply approaches to learn the distribution of genomic signals in each type of NGS data for reliable identification of specific foreground signals. We propose a novel Bayesian approach (ChIP-BIT) to reliably detect transcription factor (TF) binding sites (TFBSs) within promoter or enhancer regions by jointly analyzing the sample and input ChIP-seq data for one specific TF. Specifically, a Gaussian mixture model is used to capture both binding and background signals in the sample data; and background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. An Expectation-Maximization algorithm is used to learn the model parameters according to the distributions on binding signal intensity and binding locations. Extensive simulation studies and experimental validation both demonstrate that ChIP-BIT has a significantly improved performance on TFBS detection over conventional methods, particularly on weak binding signal detection. To infer cis-regulatory modules (CRMs) of multiple TFs, we propose to develop a Bayesian integration approach, namely BICORN, to integrate ChIP-seq and RNA-seq data of the same tissue. Each TFBS identified from ChIP-seq data can be either a functional binding event mediating target gene transcription or a non-functional binding. The functional bindings of a set of TFs usually work together as a CRM to regulate the transcription processes of a group of genes. We develop a Gibbs sampling approach to learn the distribution of CRMs (a joint distribution of multiple TFs) based on their functional bindings and target gene expression. The robustness of BICORN has been validated on simulated regulatory network and gene expression data with respect to different noise settings. BICORN is further applied to breast cancer MCF-7 ChIP-seq and RNA-seq data to identify CRMs functional in promoter or enhancer regions. In tumor cells, the normal regulatory mechanism may be interrupted by genome mutations, especially those somatic mutations that uniquely occur in tumor cells. Focused on a specific type of genome mutation, structural variation (SV), we develop a novel pattern-based probabilistic approach, namely PSSV, to identify somatic SVs from whole genome sequencing (WGS) data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with a heterozygous status in the normal sample and a homozygous status in the tumor sample. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer patient WGS data for identifying somatic SVs of key factors associated with breast cancer development. In this dissertation research, we demonstrate the advantage of the proposed distributional learning-based approaches over conventional methods for NGS data analysis. Distributional learning is a very powerful approach to gain biological insights from high quality NGS data. Successful applications of the proposed Bayesian methods to breast cancer NGS data shed light on underlying molecular mechanisms of breast cancer, enabling biologists or clinicians to identify major cancer drivers and develop new therapeutics for cancer treatment. / Ph. D.