Genetic regulatory network (GRN) is a fundamental research area in systems biology. This thesis studies the stability of a class of GRN models. First, a condition is proposed to ensure the robust stability of uncertain GRNs with SUM regulatory functions. It is assumed that the uncertainties are in the form of a parameter vector that determines the coefficients of the model via given functions. Then, the global asymptotic stability conditions of uncertain GRNs affected by disturbances and time delays are further explored. The conditions are obtained by solving a convex optimization problem by exploring the sum of squares (SOS) of matrix polynomials and by introducing polynomially parameter-dependent Lyapunov-Krasovskii functionals (LKFs). Moreover, based on the uncertain GRNs with guaranteed disturbance attenuation, it is shown that estimates of the sought stable uncertainty sets can be obtained through a recursive strategy based on parameter-dependent Lyapunov functions and the SOS. Second, the stability conditions of GRNs described by piecewise models are considered. Depending on whether the state partitions and mode transitions are known or unknown as priori, the proposed networks are divided into two categories, i.e., switched GRNs and hybrid GRNs. It is shown that, by using common polynomial Lyapunov functions and piecewise polynomial Lyapunov functions, two conditions are established to ensure the global asymptotic stability for switched and hybrid GRNs, respectively. In addition, it is shown that, by using the SOS techniques, stability conditions in the form of LMIs for both models can be obtained. Third, the multi-stability of uncertain GRNs with multivariable regulation functions is investigated. It is shown that, by using the Lyapunov functional method and LMI technology, a criterion is established to ensure the robust asymptotical stability of the uncertain GRNs, and such condition can be extended to deal with the multi-stability problem. Moreover, it is shown that by using the square matrix representation (SMR) and by adopting polynomially parameter-dependent Lyapunov functions, a condition in the form of LMIs for robust stability for all admissible uncertainties can be obtained. Examples with synthetic and real biological models are presented in each section to illustrate the applicability and effectiveness of the theoretical results. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches, a temporal synthetic dataset is developed. Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. Furthermore, the methods are also applied to a biological dataset in yeast Saccharomyces Cerevisiae. This dataset includes 14 cell-cycle regulated genes; their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. Resulting networks are also compared with two previous studies in the literature on the same dataset. / Thesis (Master, Computing) -- Queen's University, 2010-10-18 20:47:36.458

<p>In recent time particular interest has been drawn to the inference of gene regulatory networks from microarray gene expression data. But despite major improvements with data based methods, the network reconstruction from expression data alone still presents a computationally complex (NP-hard) problem. In this work it is incorporated additional information – regulatory motifs from yeast, when inferring a gene regulatory network for mouse genes. It was put forward the hypothesis that regulatory patterns analogous to these motifs are present in the set of mouse genes and can be identified by comparing yeast and mouse genes in terms of sequence similarity or Gene Ontology (The Gene Ontology Consortium 2000) annotations.</p><p>In order to examine this hypothesis, small permutations of genes with high similarity to such yeast gene regulatory motifs were first tested against simple data-driven regulatory networks by means of consistency with the expression data. And secondly, using the best scored interactions provided by these permutations it were then inferred networks for the whole set of mouse genes.</p><p>The results showed that individual permutations of genes with a high similarity to a given yeast motif did not perform better than low scored motifs and that complete networks, which were inferred from regulatory interactions provided by permutations, did also neither show any noticeable improvement over the corresponding data-driven network nor a high consistency with the expression data at all.</p><p>It was therefore found that the hypothesis failed, i.e. neither the use of sequence similarity nor searching for identical functional annotations between mouse and yeast genes allowed to identify sets of genes that showed a high consistency with the expression data or would have allowed for an improved gene regulatory network inference.</p>

Inferring regulatory relationships between genes, including the direction and the nature of influence between them, is the foremost problem in the field of genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. More specifically, a “surprising” situation occurs when the phenotype of a double mutant has a similar, aggravating or alleviating effect compared to the phenotype resulting from the single deletion of either one of the genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures, meaning it is not always possible to infer the relationship between the genes. Here, we explore the possibility of dynamic epistasis analysis. In addition to performing genetic perturbations, we drive a genetic pathway with a dynamic, time-varying upstream signal, where the phenotypic consequence is measured at each time step. We explore the theoretical power of dynamic epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We also explore the identifiability of individual links in the pathway. Through these evaluations, we quantify how helpful the addition of dynamics is. We believe that a dynamic input in addition to epistasis analysis is a powerful tool to discriminate between different networks. Our primary findings show that the use of a dynamic input signal alone, without genetic perturbations, appears to be very weak in comparison with the more traditional genetic approaches based on the deletion of genes. However, the combination of dynamical input with genetic perturbations is far more powerful than the classical epistasis analysis approach. In all cases, we find that even relatively simple input dynamics with gene deletions greatly increases the power of epistasis analysis to discriminate alternative network structures and to confidently identify individual links in a network. Our positive results show the potential value of dynamics in epistasis analysis.

Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

Indiana University-Purdue University Indianapolis (IUPUI) / Complex biological systems are composed of many regulatory components, which can be measured with the advent of genomics technology. Each molecular assay is normally designed to interrogate one aspect of the cell state. However, a comprehensive understanding of the regulatory mechanism requires characterization from multiple levels such as genome, epigenome, and transcriptome. Integration of multi-omics data is urgently needed for understanding the global regulatory mechanism of gene expression. In recent years, single-cell technology offers unprecedented resolution for a deeper characterization of cellular diversity and states. High-quality single-cell suspensions from tissue biopsies are required for single-cell sequencing experiments. Tissue biopsies need to be processed as soon as being collected to avoid gene expression changes and RNA degradation. Although cryopreservation is a feasible solution to preserve freshly isolated samples, its effect on transcriptome profiles still needs to be investigated. Investigation of multi-omics data at the single-cell level can provide new insights into the biological process. In addition to the common method of integrating multi-omics data, it is also capable of simultaneously profiling the transcriptome and epigenome at single-cell resolution, enhancing the power of discovering new gene regulatory interactions. In this dissertation, we integrated bulk RNA-seq with ATAC-seq and several additional assays and revealed the complex mechanisms of ER–E2 interaction with nucleosomes. A comparison analysis was conducted for comparing fresh and frozen multiple myeloma single-cell RNA sequencing data and concluded that cryopreservation is a feasible protocol for preserving cells. We also analyzed the single-cell multiome data for mesenchymal stem cells. With the unified landscape from simultaneously profiling gene expression and chromatin accessibility, we discovered distinct osteogenic differentiation potential of mesenchymal stem cells and different associations with bone disease-related traits. We gained a deeper insight into the underlying gene regulatory mechanisms with this frontier single-cell mutliome sequencing technique.

Stochastic modeling to represent intrinsic and extrinsic noise is an important challenge in molecular systems biology. There are numerous ways to model intrinsic noise. One framework for intrinsic noise in gene regulatory networks was recently proposed within the discrete setting. In contrast, extrinsic perturbations were rarely modeled due to the complex mechanisms that contribute to its emergence. Here a discrete framework to model extrinsic noise is proposed. The interacting species of the model are represented by discrete variables and are perturbed to represent extrinsic noise. In particular, they are subject to a discretized lognormal distribution. Additionally, a delay is imposed on the update with a certain probability. These two perturbations represent global extrinsic noise and pathway-specic extrinsic noise. It leads to large variations in the concentration of proteins, which is consistent with an existing continuous way of modeling extrinsic fluctuations. The framework is applied to three different published discrete models: the cell fate of lambda phage infection of bacteria, the lactose utilization system in E. coli, and a signaling network in melanoma cells. The framework captures factors that signicantly contribute to the random decision between lysis and lysogeny as well as explains the bistable switch in the model of the lac operon. Finally, a feed-forward loop analysis is conducted by measuring and comparing the noise level in the target protein of feed-forward loops. This analysis reveals the ability of certain feed-forward loops to attenuate or amplify fluctuations, dependent upon various levels of noise. In conclusion, this thesis aims to resolve the question of how the extrinsic noise can be modeled and how biological systems are able to maintain functionality in the wake of such large variations. / Master of Science

Zhang, Qing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 129-148). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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.

High-throughput technologies for measuring gene expression made inferring of the genome-wide Gene Regulatory Networks an active field of research. Reverse-engineering of systems of transcriptional regulations became an important challenge in molecular and computational biology. Because such systems model dependencies between genes, they are important in understanding of cell behavior, and can potentially turn observed expression data into the new biological knowledge and practical applications. In this dissertation we introduce a set of algorithms, which infer networks of transcriptional regulations from variety of expression profiles with superior accuracy compared to the state-of-the-art techniques. The proposed methods make use of ensembles of trees, which became popular in many scientific fields, including genetics and bioinformatics. However, originally they were motivated from the perspective of classification, regression, and feature selection theory. In this study we exploit their relative variable importance measure as an indication of the presence or absence of a regulatory interaction between genes. We further analyze their predictions on a set of the universally recognized benchmark expression data sets, and achieve favorable results in compare with the state-of-the-art algorithms.