At the core of gene regulatory networks are transcription factors (TFs) that recognize specific DNA sequences and target distinct gene sets. Characterizing the DNA binding specificity of all TFs is a prerequisite for understanding global gene regulatory logic, which in recent years has resulted in the development of high-throughput methods that probe TF specificity in vitro and are now routinely used to inform or interpret in vivo studies. Despite the broad success of such methods, several challenges remain, two of which are addressed in this thesis. Genomic DNA can harbor different epigenetic marks that have the potential to alter TF binding, the most prominent being CpG methylation. Given the vast number of modified CpGs in the human genome and an increasing body of literature suggesting a link between epigenetic changes and genome instability, or the onset of disease such as cancer, methods that can characterize the sensitivity of TFs to DNA methylation are needed to mechanistically interpret its impact on gene expression. We developed a high-throughput in vitro method (EpiSELEX-seq) that probes TF binding to unmodified and modified DNA sequences in competition, resulting in high-resolution maps of TF binding preferences. We found that methylation sensitivity can vary between TFs of the the same structural family and is dependent on the position of the 5mCpG within the TF binding site. The importance of our in vitro profiling of methylation sensitivity is demonstrated by the preference of human p53 tetramers for 5mCpGs within its binding site core. This previously unknown, stabilizing effect is also detectable in p53 ChIP-seq data when comparing methylated and unmethylated sites genome-wide. A second impediment to predicting TF binding is our limited understanding of i) how cooperative participation of a TF in different complexes can alter their binding preference, and ii) how the detailed shape of DNA aids in creating a substrate for adaptive multi-TF binding. To address these questions in detail, we studied the in vitro binding preferences of three D. melanogaster homeodomain TFs: Homothorax (Hth), Extradenticle(Exd) and one of the eight Hox proteins. In vivo, Hth occurs in two splice forms: with (HthFL) and without (HthHM) the DNA binding domain (DBD). HthHM-Exd itself is a Hox cofactor that has been shown to induce latent sequence specificity upon complex formation with Hox proteins. There are three possible complexes that can be formed, all potentially having specific target genes: HthHM-Exd-Hox, HthFL-Exd-Hox, and HthFL-Exd. We characterized the in vitro binding preferences of each of these by developing new computational approaches to analyze high-throughput SELEX-seq data. We found distinct orientation and spacing preference for HthFL-Exd-Hox, alternative recognition modes that depend on the affinity class a sequence falls into, and a strong preference for a narrow DNA minor grove near Exd's N-terminal DBD. Strikingly, this shape readout is crucial to stabilize the HthHM-Exd-Hox complex in the absence of a Hth DBD and can thus be used to distinguish HthHM from HthFL isoform binding. Mutating the amino acids responsible for the shape readout by Exd and reinserting the engineered protein into the fly genome allowed us to classify in vivo binding sites based on ChIP-seq signal comparison between “shape-mutant” and wild-type Exd. In summary, the research presented here has investigated TF binding preferences beyond sequence context by combining novel high-throughput experimental and computational methods. This interdisciplinary approach has enabled us to study binding preferences of TF complexes with respect to the epigenetic landscape of their cognate binding sites. Our novel mechanistic insights into DNA shape readout have provided a new avenue of exploiting guided protein engineering to probe how specific TFs interact with their co-factors in a cellular context, and how flanking genomic sequence helps determine which multi-TF complexes will form and which binding mode a complex adopts.

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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.

Cells of the ventrolateral dermomyotome delaminate and migrate into the limb buds where they give rise to all muscles of the limbs. The migratory cells proliferate and form myoblasts, which withdraw from the cell cycle to become terminally differentiated myocytes. The regulatory mechanisms that control the later steps of this myogenic program are not well understood. The homeodomain transcription factor Pitx2 is expressed specifically in the muscle lineage from the migration of precursors to adult muscle. Ablation of Pitx2 results in distortion, rather than loss, of limb muscle anlagen, suggesting that its function becomes critical during the colonization of, and/or fiber assembly in, the anlagen. Microarrays were used to identify changes in gene expression in flow-sorted migratory muscle precursors from Wild type and Pitx2 null mice. Changes in gene expression were observed in genes encoding cytoskeletal, adhesion and fusion proteins which play a role in cell motility and myoblast fusion. We observed decreased cellular motility, disrupted cytoskeleton organization and focal adhesion distribution, decreased fusion of mononucleated myoblasts into multinucleated myotubes and decreased proliferation in presence of Ptix2. These studies suggest that Pitx2 plays a critical role in regulating the timing of myoblast filling the limb anlagen which may have detrimental consequences for higher order muscle architecture. / Graduation date: 2013

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The construction of Gene Regulatory Networks (GRNs) is one of the most important issues in systems biology. To infer a large-scale GRN with a nonlinear mathematical model, researchers need to encounter the time-consuming problem due to the large number of network parameters involved. In recent years, the cloud computing technique has been widely used to solve large-scale problems. Among others, Hadoop is currently the most well-known and reliable cloud computing framework, which allows users to analyze large amount of data in a distributed environment (i.e., MapReduce). It also supports data backup and data recovery mechanisms. This study proposes an Island-based GAPSO algorithm under the Hadoop cloud computing environment to infer large-scale GRNs. GAPSO exploited the position and velocity functions of PSO, and integrated the operations of Genetic Algorithm. This approach is often used to derive the optimal solution in nonlinear mathematical models. Several sets of experiments have been conducted, in which the number of network nodes varied from 50 to 125. The experiments were executed in the Hadoop distributed environment with 10, 20, and 26 computers, respectively. In the experiments of inferring the network with 125 gene nodes on the largest Hadoop cluster (i.e. 26 computers), the proposed framework performed up to 9.7 times faster than the stand-alone computer. It means that our work can successfully reduce 90% of the computation time in a single experimental run.

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This dissertation attempts to answer some of the vital questions involved in the genetic regulatory networks: inference, optimization and robustness of the mathe- matical models. Network inference constitutes one of the central goals of genomic signal processing. When inferring rule-based Boolean models of genetic regulations, the same values of predictor genes can correspond to di®erent values of the target gene because of inconsistencies in the data set. To resolve this issue, a consistency-based inference method is developed to model a probabilistic genetic regulatory network, which consists of a family of Boolean networks, each governed by a set of regulatory functions. The existence of alternative function outputs can be interpreted as the result of random switches between the constituent networks. This model focuses on the global behavior of genetic networks and re°ects the biological determinism and stochasticity. When inferring a network from microarray data, it is often the case that the sample size is not su±ciently large to infer the network fully, such that it is neces- sary to perform model selection through an optimization procedure. To this end, the network connectivity and the physical realization of the regulatory rules should be taken into consideration. Two algorithms are developed for the purpose. One algo- rithm ¯nds the minimal realization of the network constrained by the connectivity, and the other algorithm is mathematically proven to provide the minimally connected network constrained by the minimal realization. Genetic regulatory networks are subject to modeling uncertainties and perturba- tions, which brings the issue of robustness. From the perspective of network stability, robustness is desirable; however, from the perspective of intervention to exert in- °uence on network behavior, it is undesirable. A theory is developed to study the impact of function perturbations in Boolean networks: It ¯nds the exact number of a®ected state transitions and attractors, and predicts the new state transitions and robust/fragile attractors given a speci¯c perturbation. Based on the theory, one algorithm is proposed to structurally alter the network to achieve a more favorable steady-state distribution, and the other is designed to identify function perturbations that have caused changes in the network behavior, respectively.

Cells behave as complex systems with regulatory processes that make use of many elements such as switches based on thresholds, memory, feedback, error-checking, and other components commonly encountered in electrical engineering. It is therefore not surprising that these complex systems are amenable to study by engineering methods. A great deal of effort has been spent on observing how cells store, modify, and use information. Still, an understanding of how one uses this knowledge to exert control over cells within a living organism is unavailable. Our prime objective is "Personalized Cancer Therapy" which is based on characterizing the treatment for every individual cancer patient. Knowing how one can systematically alter the behavior of an abnormal cancerous cell will lead towards personalized cancer therapy. Towards this objective, it is required to construct a model for the regulation of the cell and utilize this model to devise effective treatment strategies. The proposed treatments will have to be validated experimentally, but selecting good treatment candidates is a monumental task by itself. It is also a process where an analytic approach to systems biology can provide significant breakthrough. In this dissertation, theoretical frameworks towards effective treatment strategies in the context of probabilistic Boolean networks, a class of gene regulatory networks, are addressed. These proposed analytical tools provide insight into the design of effective therapeutic interventions.

Gene regulatory networks model the interactions within the cell and thus it is essential to understand their structure and to develop some control mechanisms that could effectively deal with them. This dissertation tackles these two aspects. To handle the first problem, a new constraint-based modeling algorithm is proposed that can both increase the quality of the output and decrease the computational requirements for learning the structure of gene regulatory networks by integrating multiple biological data types and applying a special method for dense nodes in the network. Constraint-based structure learning algorithms generally perform well on sparse graphs and it is true that sparsity is not uncommon. However, some domains like gene regulatory networks are characterized by the possibility of having some dense regions in the underlying graph and the proposed algorithm is capable of dealing with this issue. The algorithm is based on a well-known structure learning algorithm called the PC algorithm, and extends it in multiple aspects. Once a network exists, we could address the second problem, namely control of the gene regulatory network for various applications where the curse of dimensionality is the main issue. It is possible that hundreds of genes may regulate one biological activity in an organism and this implies a huge state space even in the case of Boolean models. The thesis proposes effective methods to find control policies for large-scale networks. The modeling and control algorithms proposed in this dissertation have been evaluated on both synthetic and real data sets. The test results demonstrate the efficiency and effectiveness of the proposed approaches.