Non-syndromic orofacial clefting (nsOFC) is among the most common congenital birth defects occurring up to 1 in 800 live births, with genetic and environmental causes. Genome wide association studies (GWAS) have identified several genetic loci that confer risk for nsOFC. However, more than half the heritable risk for nsOFC remains unknown and is considered ‘missing’. Moreover, continued sequencing of nsOFC patient DNA by whole exome sequencing and whole exome sequencing identify hundreds of single nucleotide polymorphism (SNPs). The identification of causal SNPs, however, continues to be a challenge in the OFC community. This is fueled partly by a lack of understanding of: (i) molecular mechanism and, (i) the gene regulatory network (GRN) governing differentiation of the relevant tissue, the embryonic superficial epithelia, also known as the periderm. Research has demonstrated that aberrant differentiation of the periderm, particularly the oral periderm results in pathological adhesions of surfaces within the developing oral cavity resulting in OFC. Further these adhesions can extend to the limbs which is a hallmarks feature in some forms of syndromic OFC (sOFC). In zebrafish, our model system of choice, knock-out of interferon regulatory factor 6 (irf6) ablated periderm marker expression and subsequently induces early embryonic lethality. The ortholog of IRF6 is a major genetic locus of Van der Woude syndrome (VWS) the most common form of sOFC and variants of IRF6 elevate risk for nsOFC. Therefore, we hypothesize that GRN of zebrafish periderm differentiation under the control of irf6 is a tool that can be used to identify novel OFC loci. Supporting this view, we have recently demonstrated that knock-down of an irf6 dependent gene encoding transcription factor Grainy-head like 3 (Grhl3) results in aberrant zebrafish periderm differentiation and GRHL3 was recently discovered as a novel VWS genetic locus. Hence it is likely that orthologs of genes encoding additional members of the periderm GRN harbor mutations in OFC patients. To identify cis–regulatory and transcriptional components in the periderm GRN, we performed: (i) a screen for periderm enhancers through in vivo green fluorescent protein (GFP) reporter assays, and, (ii) irf6 RNA-seq, followed by irf6 ChIP-seq to identify direct targets. From our screen for cis-regulatory elements we have identified a candidate human ZNF750 enhancer that directs GFP reporter expression in the zebrafish periderm. From our screen for irf6 direct targets we have identified several transcription factors including klf17, tfap2a and grhl3, all of which have variants in the human orthologs found in OFC patients. We further resolve the structure of the periderm differentiation GRN in zebrafish by assessing loss of function profiles for klf17, tfap2a and grhl3. Additionally, among the irf6 direct targets is a gene encoding another transcription factor, Zinc finger protein 750 (Znf750). We provide evidence to show that znf750 is expressed weakly in the zebrafish periderm. Further, we sequenced DNA in 500 nsOFC patient samples and identify a novel missense Ser160Pro ZNF750 variant which phenocopies the early embryonic lethality observed in irf6 mutants. Therefore, investigation of the zebrafish periderm GRN structure has facilitated the identification of OFC-associated risk loci.

Gene regulatory networks are composed of interconnected regulatory nodes created by regulatory factors of multiple types. All organisms finely tune gene expression in order to adapt to and survive within their current niche. Obligate parasitic bacteria are under extreme pressure to quickly and appropriately adapt their gene regulatory programs in order to survive within their given host. Borrelia burgdorferi is one such organism and persists in nature by alternating between two hosts; Ixodes spp. ticks and small vertebrate animals. These two hosts represent drastically different environments; requiring a unique gene regulatory program to survive and transmit between them. Microbiologists have long sought to better understand exactly what stimuli pathogens sense and how that information is relayed in to physiologic adaptation. In this work I aimed to examine two parts of this interesting field. First, I sought to better understand the stimuli B. burgdorferi sense in order to adapt to their hosts by testing several hypotheses centered on the general notion that B. burgdorferi senses both internal and external metabolic cues as primary signals for adaptation. I demonstrated that a second messenger system immediately downstream of a critical metabolic pathway is important during vertebrate infection and that a key regulator of virulence is itself regulated by a factor involved in DNA replication. Second, I sought to better define the topology of gene regulatory networks, known and unknown, that are important for the ability of the bacteria to adapt. The work in this section focus on the idea that B. burgdorferi gene regulatory networks are extremely complex and are not currently well defined in the literature. My studies revealed that B. burgdorferi possesses a large number of previously undefined regulatory targets, including extended 5’ and 3’ UTRs of known genes, and encodes several hundred-putative small non-coding RNAs. Furthermore, I demonstrate that two essential regulatory factors share substantial, independent, overlap in their regulons highlighting the still undefined complexity of regulatory networks at play in B. burgdorferi.

<p>In this thesis several random Boolean networks are simulated. Both completely computer generated network and models for biological networks are simulated. Several different tools are used to gain knowledge about the robustness. These tools are Derrida plots, noise analysis and mean probability for canalizing rules. Some simulations on how entropy works as an indicator on if a network is robust are also included. The noise analysis works by measuring the hamming distance between the state of the network when noise is applied and when no noise is applied. For many of the simulated networks two types of rules are applied: nested canalizing and flat distributed rules. The computer generated networks consists of two types of networks: scale-free and ER-networks. One of the conclusions in this report is that nested canalizing rules are often more robust than flat distributed rules. Another conclusion is that the mean probability for canalizing rules has, for flat distributed rules, a very dominating effect on if the network is robust or not. Yet another conclusion is that the probability distribution for indegrees, for flat distributed rules, has a strong effect on if a network is robust due to the connection between the probability distribution for indegrees and the mean probability for canalizing rules.</p>

All high-level living organisms are made of small cell units, containing DNA, RNA, genes, proteins etc. Genes are important components of the cells and it is necessary to understand the inter-gene relations, in order to comprehend, predict and ultimately intervene in the cells’ dynamics. Genetic regulatory networks (GRN) represent the gene interactions that dictate the cell behavior. Translational genomics aims to mathematically model GRNs and one of the main goals is to alter the networks’ behavior away from undesirable phenotypes such as cancer. The mathematical framework that has been often used for modeling GRNs is the probabilistic Boolean network (PBN), which is a collection of constituent Boolean networks with perturbation, BNp. This dissertation uses BNps, to model gene regulatory networks with an intent of designing stationary control policies (CP) for the networks to shift their dynamics toward more desirable states. Markov Chains (MC) are used to represent the PBNs and stochastic control has been employed to find stationary control policies to affect steady-state distribution of the MC. However, as the number of genes increases, it becomes computationally burdensome, or even infeasible, to derive optimal or greedy intervention policies. This dissertation considers the problem of modeling and intervening in large GRNs. To overcome the computational challenges associated with large networks, two approaches are proposed: first, a reduction mapping that deletes genes from the network; and second, a greedy control policy that can be directly designed on large networks. Simulation results show that these methods achieve the goal of controlling large networks by shifting the steady-state distribution of the networks toward more desirable states. Furthermore, a new inference method is used to derive a large 17-gene Boolean network from microarray experiments on gastrointestinal cancer samples. The new algorithm has similarities to a previously developed well-known inference method, which uses seed genes to grow subnetworks, out of a large network; however, it has major differences with that algorithm. Most importantly, the objective of the new algorithm is to infer a network from a seed gene with an intention to derive the Gene Activity Profile toward more desirable phenotypes. The newly introduced reduction mappings approach is used to delete genes from the 17-gene GRN and when the network is small enough, an intervention policy is designed for the reduced network and induced back to the original network. In another experiment, the greedy control policy approach is used to directly design an intervention policy on the large 17-gene network to beneficially change the long-run behavior of the network. Finally, a novel algorithm is developed for selecting only non-isomorphic BNs, while generating synthetic networks, using a method that generates synthetic BNs, with a prescribed set of attractors. The goal of the new method described in this dissertation is to discard isomorphic networks.

In this thesis several random Boolean networks are simulated. Both completely computer generated network and models for biological networks are simulated. Several different tools are used to gain knowledge about the robustness. These tools are Derrida plots, noise analysis and mean probability for canalizing rules. Some simulations on how entropy works as an indicator on if a network is robust are also included. The noise analysis works by measuring the hamming distance between the state of the network when noise is applied and when no noise is applied. For many of the simulated networks two types of rules are applied: nested canalizing and flat distributed rules. The computer generated networks consists of two types of networks: scale-free and ER-networks. One of the conclusions in this report is that nested canalizing rules are often more robust than flat distributed rules. Another conclusion is that the mean probability for canalizing rules has, for flat distributed rules, a very dominating effect on if the network is robust or not. Yet another conclusion is that the probability distribution for indegrees, for flat distributed rules, has a strong effect on if a network is robust due to the connection between the probability distribution for indegrees and the mean probability for canalizing rules.

The past decades have seen rapid development of numerous high-throughput technologies to observe biomolecular phenomena. High-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze heterogeneous biological data, but also model the complex experimental observation procedures. We first present an algorithm for learning dynamic cell cycle transcriptional regulatory networks from gene expression and transcription factor binding data. We learn regulatory networks using dynamic Bayesian network inference algorithms that combine evidence from gene expression data through the likelihood and evidence from binding data through an informative structure prior. We next demonstrate how analysis of cell cycle measurements like gene expression data are obstructed by sychrony loss in synchronized cell populations. Due to synchrony loss, population-level cell cycle measurements are convolutions of the true measurements that would have been observed when monitoring individual cells. We introduce a fully parametric, probabilistic model, CLOCCS, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using CLOCCS, we formulate a constrained convex optimization deconvolution algorithm that recovers single cell estimates from observed population-level measurements. Our algorithm offers a solution for monitoring individual cells rather than a population of cells that lose synchrony over time. Using our deconvolution algorithm, we provide a global high resolution view of cell cycle gene expression in budding yeast, right from an initial cell progressing through its cell cycle, to across the newly created mother and daughter cell. Proteins, and not gene expression, are responsible for all cellular functions, and we need to understand how proteins and protein complexes operate. We introduce PROCTOR, a statistical approach capable of learning the hidden interaction topology of protein complexes from direct protein-protein interaction data and indirect co-complexed protein interaction data. We provide a global view of the budding yeast interactome depicting how proteins interact with each other via their interfaces to form macromolecular complexes. We conclude by demonstrating how our algorithms, utilizing information from heterogeneous biological data, can provide a dynamic view of regulatory control in the budding yeast cell cycle. / Dissertation

<p>The successful completion of many critical biological processes depends on the proper execution of complex spatial and temporal gene expression programs. With the advent of high-throughput microarray technology, it is now possible to measure the dynamics of these expression programs on a genome-wide level. In this thesis we present work focused on utilizing this technology, in combination with novel computational techniques, to examine the role of transcriptional regulatory mechanisms in controlling the complex gene expression programs underlying two fundamental biological processes---the cell cycle and the development and differentiation of an organ.</p><p>We generate a dataset describing the genomic expression program which occurs during the cell division cycle of <italic>Saccharomyces cerevisiae</italic>. By concurrently measuring the dynamics in both wild-type and mutant cells that do not express either S-phase or mitotic cyclins we quantify the relative contributions of cyclin-CDK complexes and transcriptional regulatory networks in the regulation the cell cell expression program. We show that CDKs are not the sole regulators of periodic transcription as contrary to previously accepted models; and we hypothesize an oscillating transcriptional regulatory network which could work independent of, or in tandem with, the CDK oscillator to control the cell cell expression program.</p><p>To understand the acquisition of cellular identity, we generate a nearly complete gene expression map of the <italic>Arabidopsis Thaliana</italic> root at the resolution of individual cell-types and developmental stages. An analysis of this data reveals a representative set of dominant expression patterns which are used to begin defining the spatiotemporal transcriptional programs that control development within the root.</p><p>Additionally, we develop computational tools that improve the interpretability and power of these data. We present CLOCCS, a model for the dynamics of population synchrony loss in time-series experiments. We demonstrate the utility of CLOCCS in integrating disparate datasets and present a CLOCCS based deconvolution of the cell-cycle expression data. A deconvolution method is also developed for the <italic>Arabidopsis</italic> dataset, increasing its resolution to cell-type/section subregion specificity. Finally, a method for identifying biological processes occurring on multiple timescales is presented and applied to both datasets.</p><p>It is through the combination of these new genome-wide expression studies and computational tools that we begin to elucidate the transcriptional regulatory mechanisms controlling fundamental biological processes.</p> / Dissertation

Since the development of high-throughput technologies, we can capture large quantities of gene¡¦s expression data from DNA microarray data, so there are some technologies have been proposed to model gene regulatory networks. Gene regulatory networks is mainly used to express the relationship between the genes, but only can express a simple relationship, and can¡¦t clearly show how the operation between genes regulatory. In the simulation method of gene regulation, the mathematical methods are more often used. In the mathematical methods, S-system is the most widely used in non-linear differential equations. When the use of mathematical simulation of gene regulatory networks, there are mainly two aspects¡G(1) deciding on the model structure and (2) estimating the involved parameter values. However, when using S-system simulated the gene regulatory networks, we can only know the gene profiles, and there is no way to know the regulatory relationships between genes, but in order to understand the relationship between genes, we must clearly understand how genes work. Therefore, we propose to encode parameter values to infer the regulatory parameter values between genes. We propose the method of encoding parameter values, and using six artificial genetic datasets, and assuming 100% parameter values are known, 90% known, 70% known, 50% known, 30% known, 10% known. The experimental results show, besides it can infer a high proportion of non-regulation, positive regulation and negative regulation, also can infer more precise parameter values, and also has a clear understanding of the regulatory relationship between genes.

Understanding the mechanisms of gene regulation in the field of systems biology is a very important issue. With the development of bio-information technology, we can capture large quantities of gene¡¦s expression data from DNA microarray data. In order to discover the relationship of gene regulation, the simulation of gene regulatory networks have been proposed. Among these simulations methods, the S-system model is the most widely used in non-linear differential equations. It can simulate the dynamic behavior of gene regulatory networks and gene expression, but can¡¦t explain the structure and orientation of gene regulatory networks. Therefore, we propose a Boolean knowledge-based approach to assist the S-system modeling of gene regulatory networks. In this study, we derive the positive and negative regulatory relationships between genes from the regulation of S-system parameters, and use the structure of Boolean networks as our knowledge base. According to the results of the experiment, we can verify our assumptions for the regulation of the S-system parameters, and also has a better understanding of the regulatory relationship between genes.

Cells switch phenotypes or behaviors to adapt to various environmental stimuli. Often there are multiple alternative phenotypes, hence a cell chooses one phenotype among them, a process which we term a ``decision switch'. At the cellular level, decision switches are governed by gene regulation, hence they are intrinsically stochastic. Here we investigate two aspects of decision switches: how copy number of genetic components facilitates multiple phenotypes and how temporal dynamics of gene regulation with stochastic fluctuations affect switching a cell fate. First, we demonstrate that gene expression can be sensitive to changes in the copy number of genes and promoters, and alternative phenotypes may arise due to bistability within gene regulatory networks. Our analysis in phage-lambda-infected E. coli cells exhibit drastic change in gene expression by changing the copy number of viral genes, suggesting phages can determine their fates collectively via sharing gene products. Second, we examine decision switches mediated by temporal dynamics of gene regulation. We consider a case when temporal gene expression triggers a corresponding cell fate, and apply it to the lysis-lysogeny decision switch by phage lambda. Our analysis recapitulates the systematic bias between lysis and lysogeny by the viral gene copy number. We also present a quantitative measure of cell fate predictability based on temporal gene expression. Analyses using our framework suggest that the future fate of a cell can be highly correlated with temporal gene expression, and predicted if the current gene expression is known.