We used mouse embryonic stem cells (ESCs) as a model to study the mechanisms that regulate stem cell fate. Using gene expression analysis during a time course of differentiation, we identified 281 candidate regulators of ESC fate. To integrate these candidate regulators into the known ESC transcriptional network, we incorporated promoter occupancy data for OCT4, NANOG and SOX2. We used shRNA knockdown studies followed by a high-content fluorescence imaging assay to test the requirement of our predicted regulators in maintaining self-renewal. We further integrated promoter occupancy data for Polycomb group (PcG) proteins, EED and PHC1 to identify 43 transcriptional networks in which we predict that OCT4 and NANOG co-operate with EED and PHC1 to influence the expression of multiple developmental regulators. Next, we turned our focus to the PcG protein PCL2 which we identified as being bound by both OCT4 and NANOG and down-regulated during differentiation. PcG proteins are conserved epigenetic transcriptional repressors that control numerous developmental gene expression programs. Using multiple biochemical strategies, we demonstrated that PCL2 associates with Polycomb Repressive Complex 2 (PRC2) in mouse ESCs, a complex that exerts its effect on gene expression through H3K27me3. Although PCL2 was not required for global histone methylation, it was required at specific target regions to maintain proper levels of H3K27me3. Knockdown of Pcl2 in ESCs resulted in heightened self-renewal characteristics and defects in differentiation. Integration of global gene expression and promoter occupancy analyses allowed us to identify PCL2 and PRC2 transcriptional targets and draft regulatory networks. We describe the role of PCL2 in both modulating transcription of ESC self-renewal genes in undifferentiated ESCs as well as developmental regulators during early commitment and differentiation.

We used mouse embryonic stem cells (ESCs) as a model to study the mechanisms that regulate stem cell fate. Using gene expression analysis during a time course of differentiation, we identified 281 candidate regulators of ESC fate. To integrate these candidate regulators into the known ESC transcriptional network, we incorporated promoter occupancy data for OCT4, NANOG and SOX2. We used shRNA knockdown studies followed by a high-content fluorescence imaging assay to test the requirement of our predicted regulators in maintaining self-renewal. We further integrated promoter occupancy data for Polycomb group (PcG) proteins, EED and PHC1 to identify 43 transcriptional networks in which we predict that OCT4 and NANOG co-operate with EED and PHC1 to influence the expression of multiple developmental regulators. Next, we turned our focus to the PcG protein PCL2 which we identified as being bound by both OCT4 and NANOG and down-regulated during differentiation. PcG proteins are conserved epigenetic transcriptional repressors that control numerous developmental gene expression programs. Using multiple biochemical strategies, we demonstrated that PCL2 associates with Polycomb Repressive Complex 2 (PRC2) in mouse ESCs, a complex that exerts its effect on gene expression through H3K27me3. Although PCL2 was not required for global histone methylation, it was required at specific target regions to maintain proper levels of H3K27me3. Knockdown of Pcl2 in ESCs resulted in heightened self-renewal characteristics and defects in differentiation. Integration of global gene expression and promoter occupancy analyses allowed us to identify PCL2 and PRC2 transcriptional targets and draft regulatory networks. We describe the role of PCL2 in both modulating transcription of ESC self-renewal genes in undifferentiated ESCs as well as developmental regulators during early commitment and differentiation.

In the quest to understand cell behavior and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are being deployed to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling cell behavior, inferring biological networks, and controlling genetic diseases. Genes interact with other genes in a Gene Regulatory Network (GRN) and can be modeled as a Boolean Network (BN) or equivalently as a Finite State Machine (FSM). As the expression of genes deter- mine cell behavior, important problems include (i) inferring the GRN from observed gene expression data from biological measurements, and (ii) using the inferred GRN to explain how genetic diseases occur and determine the ”best” therapy towards treatment of disease. We report results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the predictor (logical support) of each gene that regulates melanoma, using gene expression data from patients who are suffering from the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular target gene. Our second technique builds upon the first, in which we use a logic synthesis technique; implemented using SAT, to determine gene regulating functions for predictors and gene expression data. This technique determines a BN (or family of BNs) to describe the GRN and is validated on a synthetic network and the p53 network. The first two techniques assume binary valued gene expression data. In the third technique, we utilize continuous (analog) expression data, and present an algorithm to infer and rank predictors using modified Zhegalkin polynomials. We demonstrate our method to rank predictors for genes in the mutated mammalian and melanoma networks. The final technique assumes that the GRN is known, and uses weighted partial Max-SAT (WPMS) towards cancer therapy. In this technique, the GRN is assumed to be known. Cancer is modeled using a stuck-at fault model, and ATPG techniques are used to characterize genes leading to cancer and select drugs to treat cancer. To steer the GRN state towards a desirable healthy state, the optimal selection of drugs is formulated using WPMS. Our techniques can be used to find a set of drugs with the least side-effects, and is demonstrated in the context of growth factor pathways for colon cancer.

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems.

A primary aim of Synthetic Biology is the design and implementation of biological systems that perform engineered functions. However, the assembly of double-stranded DNA molecules is a major barrier to this progress, as it remains time consuming and laborious. Here I present three improved methods for DNA assembly. The first is based on, and makes use of, BioBricks. The second method relies on overlap-extension PCR to assemble non-standard parts. The third method improves upon overlap extension PCR by reducing the number of steps and the time it takes to assemble DNA. Finally, I show how the PCR-based assembly methods presented here can be used, in concert, with in vivo homologous recombination in yeast to assemble as many as 19 individual DNA parts in one step. These methods will also be used to assemble an incoherent feedforward loop, gene regulatory network.

The core focus of this research lies in developing and using intelligent methods to solve biological problems and integrating the knowledge for understanding the complex gene regulatory phenomenon. We have developed an integrative framework and used it to: model molecular interactions from separate case studies on time-series gene expression microarray datasets, molecular sequences and structure data including the functional role of microRNAs; to extract knowledge; and to build reusable models for the central dogma theme. Knowledge was integrated with the use of ontology and it can be reused to facilitate new discoveries as demonstrated on one of our systems – the Brain Gene Ontology (BGO). The central dogma theme states that proteins are produced from the DNA (gene) via an intermediate transcript called RNA. Later these proteins play the role of enzymes to perform the checkpoints as a gene expression control. Also, according to the recently emerged paradigm, sometimes genes do not code for proteins but results in small molecules of microRNAs which in turn controls the gene regulation. The idea is that such a very complicated molecular biology process (central dogma) results in production of a wide variety of data that can be used by computer scientists for modelling and to enable discoveries. We have suggested that this range of data should actually be taken into account for analysis to understand the concept of gene regulation instead of just taking one source of data and applying some standard methods to reveal facts in the system biology. The problem is very complex and, currently, computational algorithms have not been really successful because either existing methods have certain problems or the proven results were obtained for only one domain of the central dogma of molecular biology, so there has always been a lack of knowledge integration. Proper maintenance of diverse sources of data, structures and, in particular, their adaptation to new knowledge is one of the most challenging problems and one of the crucial tasks towards the knowledge integration vision is the efficient encoding of human knowledge in ontologies. More specifically this work has contributed towards the development of novel computational and information science methods and we have promoted the vision of knowledge integration by developing brain gene ontology (BGO) system. With the integrative use of several bioinformatics methods, this research has indeed resulted in modelling of such knowledge that has not been revealed in system biology so far. There are many discoveries made during my study and some of the findings are briefly mentioned as follows: (1) in relation to leukaemia disease we have discovered a new gene “TCF-1” that interacts with the “telomerase” gene. (2) With respect to yeast cell cycle analysis, we hypothesize that exoglucanase gene “exg1” is now implicated to be tied with “MCB cluster regulation” and a “mannosidase” with “histone linked mannoses”. A new quantitative prediction is that the time delay of the interaction between two genes seems to be approximately 30 minutes, or 0.17 cell cycles. Next, Cdc22, Suc22 and Mrc1 genes were discovered that interacts with each other as the potential candidates in controlling the Ribonucleotide reductase (RNR) activity. (3) Upon studying the phenomenon of Long Term Potentiation (LTP) it was found that the transcription factors, responsible for regulation of gene expression, begin to be elevated as soon as 30 min after induction of LTP, and remain elevated up to 2 hours. (4) Human microRNA data investigation resulted in the successful identification of two miRNA families i.e. let-7 and mir-30. (5) When we analysed the CNS cancer data, a set of 10 genes (HMG-I(Y), NBL1, UBPY, Dynein, APC, TARBP2, hPGT, LTC4S, NTRK3, and Gps2) was found to give 85% correct prediction on drug response. (6) Upon studying the AMPA, GABRA and NMDA receptors we hypothesize that phenylalanine (F at position 269) and leucine (L at position 353) in these receptors play the role of a binding centre for their interaction with several other genes/proteins such as c-jun, mGluR3, Jerky, BDNF, FGF-2, IGF-1, GALR1, NOS and S100beta. All the developed methods that we have used to discover above mentioned findings are very generic and can be easily applied on any dataset with some constraints. We believe that this research has established the significant fact that integrative use of various computational intelligence methods is critical to reveal new aspects of the problem and finally knowledge integration is also a must. During this coursework, I have significantly published this research in reputed international journals, presented results in several conferences and also produced book chapters.

Genetic and protein interactions are essential to regulate cellular machinery. Their identification has become an important aim of systems biology research. In recent years, a variety of computational network inference algorithms have been employed to reconstruct gene regulatory networks from post-genomic data. However, precisely predicting these regulatory networks remains a challenge. We began our study by assessing the ability of various network inference algorithms to accurately predict gene regulatory interactions using benchmark simulated datasets. It was observed from our analysis that different algorithms have strengths and weaknesses when identifying regulatory networks, with a gene-pair interaction (edge) predicted by one algorithm not always necessarily consistent with the other. An edge not predicted by most inference algorithms may be an important one, and should not be missed. The naïve consensus (intersection) method is perhaps the most conservative approach and can be used to address this concern by extracting the edges consistently predicted across all inference algorithms; however, it lacks credibility as it does not provide a quantifiable measure for edge weights. Existing quantitative consensus approaches, such as the inverse-variance weighted method (IVWM) and the Borda count election method (BCEM), have been previously implemented to derive consensus networks from diverse datasets. However, the former method was biased towards finding local solutions in the whole network, and the latter considered species diversity to build the consensus network. In this thesis we proposed a novel consensus approach, in which we used Fishers Combined Probability Test (FCPT) to combine the statistical significance values assigned to each network edge by a number of different networking algorithms to produce a consensus network. We tested our method by applying it to a variety of in silico benchmark expression datasets of different dimensions and evaluated its performance against individual inference methods, Bayesian models and also existing qualitative and quantitative consensus techniques. We also applied our approach to real experimental data from the yeast (S. cerevisiae) network as this network has been comprehensively elucidated previously. Our results demonstrated that the FCPT-based consensus method outperforms single algorithms in terms of robustness and accuracy. In developing the consensus approach, we also proposed a scoring technique that quantifies biologically meaningful hierarchical modular networks.

Modeling gene regulatory networks has become a problem of great interest in biology and medical research. Most common methods for learning regulatory dependencies rely on observations in the form of gene expression data. In this dissertation, computational models for gene regulation have been developed based on constrained regression by integrating comprehensive gene expression data for M. tuberculosis with genome-scale ChIP-Seq interaction data. The resulting models confirmed predictive power for expression in independent stress conditions and identified mechanisms driving hypoxic adaptation and lipid metabolism in M. tuberculosis. I then used the regulatory network model for M. tuberculosis to identify factors responding to stress conditions and drug treatments, revealing drug synergies and conditions that potentiate drug treatments. These results can guide and optimize design of drug treatments for this pathogen. I took the next step in this direction, by proposing a new probabilistic framework for learning modular structures in gene regulatory networks from gene expression and protein-DNA interaction data, combining the ideas of module networks and stochastic blockmodels. These models also capture combinatorial interactions between regulators. Comparisons with other network modeling methods that rely solely on expression data, showed the essentiality of integrating ChIP-Seq data in identifying direct regulatory links in M. tuberculosis. Moreover, this work demonstrates the theoretical advantages of integrating ChIP-Seq data for the class of widely-used module network models. The systems approach and statistical modeling presented in this dissertation can also be applied to problems in other organisms. A similar approach was taken to model the regulatory network controlling genes with circadian gene expression in Neurospora crassa, through integrating time-course expression data with ChIP-Seq data. The models explained combinatorial regulations leading to different phase differences in circadian rhythms. The Neurospora crassa network model also works as a tool to manipulate the phases of target genes.

A fundamental challenge in biology is to understand the complex gene regulatory networks which control tissue development in the mammalian embryo, and maintain homoeostasis in the adult. The cell fate decisions underlying these processes are ultimately made at the level of individual cells. Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conventional high-throughput experiments. The aim of this PhD was to investigate the possibility of using this new high resolution data to reconstruct mechanistic computational models of gene regulatory networks. In this thesis I introduce the idea of viewing single-cell gene expression profiles as states of an asynchronous Boolean network, and frame model inference as the problem of reconstructing a Boolean network from its state space. I then give a scalable algorithm to solve this synthesis problem. In order to achieve scalability, this algorithm works in a modular way, treating different aspects of a graph data structure separately before encoding the search for logical rules as Boolean satisfiability problems to be dispatched to a SAT solver. Together with experimental collaborators, I applied this method to understanding the process of early blood development in the embryo, which is poorly understood due to the small number of cells present at this stage. The emergence of blood from Flk1+ mesoderm was studied by single cell expression analysis of 3934 cells at four sequential developmental time points. A mechanistic model recapitulating blood development was reconstructed from this data set, which was consistent with known biology and the bifurcation of blood and endothelium. Several model predictions were validated experimentally, demonstrating that HoxB4 and Sox17 directly regulate the haematopoietic factor Erg, and that Sox7 blocks primitive erythroid development. A general-purpose graphical tool was then developed based on this algorithm, which can be used by biological researchers as new single-cell data sets become available. This tool can deploy computations to the cloud in order to scale up larger high-throughput data sets. The results in this thesis demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the gene regulatory networks that underpin organogenesis. Rapid technological advances in our ability to perform single-cell profiling suggest that my tool will be applicable to other organ systems and may inform the development of improved cellular programming strategies.

The relationship between a pathogen and its host is a complex series of events that occurs at the molecular level and is controlled by transcriptional and protein interactions. To facilitate the understanding of these mechanisms in Aspergillus flavus and Zea mays, three approaches were taken: 1) the development of a predicted interactome for Z. mays (PiZeaM), 2) the development of co-expression networks for Z. mays and A. flavus from RNA-seq data, and 3) the development of causal inference networks depicting interactions between the host and the pathogen. PiZeaM is the genome-wide roadmap of protein-protein interactions that occur within Z. mays. PiZeaM helps create a novel map of the interactions in Z. mays in response to biotic and abiotic stresses. To further support the predicted interactions, an analysis of microarray-based gene expression was used to produce a gene co-expression network. PiZeaM was able to capture conserved resistance pathways involved involved in the response to pathogens, abiotic stress and development. Gene Co-expression networks were developed by the simultaneous use of correlations to develop networks for differentially expressed genes, resistance marker genes, pathogenicity genes, and genes involved is secondary metabolism in Z. mays and A. flavus. From these networks, correlation and anti-correlation of host and pathogen gene expression was detected, revealing genes that potentially interact at different stages of pathogenesis. Finally, causal gene regulatory relationships were inferred using partial correlation analysis of Z. mays infected with A. flavus over a 3 day period. The gene regulatory network (GRN) sheds light on the specifics of the mechanisms of pathogenesis and resistance that govern the Z. mays-A. flavus interaction. The direct product of this research is the understanding of key transcription factors and signaling genes involved in resistance. This body of research highlights how PPIs and GRNs can be utilized to identify biomarkers and gene functions in both Z. mays and A. flavus.