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.

With the abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior knowledge (PK), along with gene expression, greatly improves prediction accuracy, the accuracy remains low. PK in GRN inference can be categorized into noisy and curated. Several algorithms were proposed to incorporate noisy PK, but none address curated PK. Another challenge is that much of the PK is not stored in databases or not in a unified structured format to be accessible by inference algorithms. Moreover, no GRN inference method exists that supports post-prediction PK. This thesis addresses those limitations with three solutions: PEAK algorithm for integrating both curated and noisy PK, Online-PEAK for post-prediction interactive feedback, and DeTangle for visualization and navigation of GRNs. PEAK integrates both curated as well as noisy PK in GRN inference. We introduce a novel method for GRN inference, CurInf, to effectively integrate curated PK, and we use the previous method, Modified Elastic Net, for noisy PK, and we call it NoisInf. Using 100% curated PK, CurInf improves the AUPR accuracy score over NoisInf by 27.3% in synthetic data, 86.5% in E. coli data, and 31.1% in S. cerevisiae data. Moreover, we developed an online algorithm, online-PEAK, that enables the biologist to interact with the inference algorithm, PEAK, through a visual interface to add their domain experience about the structure of the GRN as a feedback to the system. We experimentally verified the ability of online-PEAK to achieve incremental accuracy when PK is added by the user, including true and false PK. Even when the noise in PK is 10 times more than true PK, online-PEAK performs better than inference without any PK. Finally, we present DeTangle, a Web server for interactive GRN prediction and visualization. DeTangle provides a seamless analysis of GRN starting from uploading gene expression, GRN inference, post-prediction feedback using online-PEAK, and visualization and navigation of the predicted GRN. More accurate prediction of GRN can facilitate studying complex molecular interactions, understanding diseases, and aiding drug design. / Ph. D.

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.

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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.