Gene Regulatory Network Reconstruction Using Dynamic Bayesian Networks

High-content technologies such as DNA microarrays can provide a system-scale overview of how genes interact with each other in a network context. Various mathematical methods and computational approaches have been proposed to reconstruct GRNs, including Boolean networks, information theory, differential equations and Bayesian networks. GRN reconstruction faces huge intrinsic challenges on both experimental and theoretical fronts, because the inputs and outputs of the molecular processes are unclear and the underlying principles are unknown or too complex. In this work, we focused on improving the accuracy and speed of GRN reconstruction with Dynamic Bayesian based method. A commonly used structure-learning algorithm is based on REVEAL (Reverse Engineering Algorithm). However, this method has some limitations when it is used for reconstructing GRNs. For instance, the two-stage temporal Bayes network (2TBN) cannot be well recovered by application of REVEAL; it has low accuracy and speed for high dimensionality networks that has above a hundred nodes; and it even cannot accomplish the task of reconstructing a network with 400 nodes. We implemented an algorithm for DBN structure learning with Friedman\u27s score function to replace REVEAL, and tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou and Conzen\u27s algorithm. The new score metric improved the precision and recall of GRN reconstruction. Networks of gene interactions were reconstructed using a Dynamic Bayesian Network (DBN) approach and were analyzed to identify the mechanism of chemical-induced reversible neurotoxicity through reconstruction of gene regulatory networks in earthworms with tools curating relevant genes from non-model organism\u27s pathway to model organism pathway

Analysing directed network data

The topology of undirected biological networks, such as protein-protein interaction networks, or genetic interaction networks, has been extensively explored in search of new biological knowledge. Graphlets, small connected non-isomorphic induced sub-graphs of an undirected network, have been particularly useful in computational network biology. Having in mind that a significant portion of biological networks, such as metabolic networks or transcriptional regulatory networks, are directed by nature, we define all up to four node directed graphlets and orbits and implement the directed graphlet and graphlet orbits counting algorithm. We generalise all existing graphlet based measures to the directed case, defining: relative directed graphlet frequency distance, directed graphlet degree distribution similarity, directed graphlet degree vector similarity, and directed graphlet correlation distance. We apply new topological measures to metabolic networks and show that the topology of directed biological networks is correlated with biological function. Finally, we look for topology–function relationships in metabolic networks that are conserved across different species.Open Acces

Learning condition-specific networks

Condition-specific cellular networks are networks of genes and proteins that describe functional interactions among genes occurring under different environmental conditions. These networks provide a systems-level view of how the parts-list (genes and proteins) interact within the cell as it functions under changing environmental conditions and can provide insight into mechanisms of stress response, cellular differentiation and disease susceptibility. The principle challenge, however, is that cellular networks remain unknown for most conditions and must be inferred from activity levels of genes (mRNA levels) under different conditions. This dissertation aims to develop computational approaches for inferring, analyzing and validating cellular networks of genes from expression data. This dissertation first describes an unsupervised machine learning framework for inferring cellular networks using expression data from a single condition. Here cellular networks are represented as undirected probabilistic graphical models and are learned using a novel, data-driven algorithm. Then several approaches are described that can learn networks using data from multiple conditions. These approaches apply to cases where the condition may or may not be known and, therefore, must be inferred as part of the learning problem. For the latter, the condition variable is allowed to influence expression of genes at different levels of granularity: condition variable per gene to a single condition variable for all genes. Results on simulated data suggest that the algorithm performance depends greatly on the size and number of connected components of the union network of all conditions. These algorithms are also applied to microarray data from two yeast populations, quiescent and non-quiescent, isolated from glucose starved cultures. Our results suggest that by sharing information across multiple conditions, better networks can be learned for both conditions, with many more biologically meaningful dependencies, than if networks were learned for these conditions independently. In particular, processes that were shared among both cell populations were involved in response to glucose starvation, whereas the processes specific to individual populations captured characteristics unique to each population. These algorithms were also applied for learning networks across multiple species: yeast (S. cerevisiae) and fly (D. melanogaster). Preliminary analysis suggests that sharing patterns across species is much more complex than across different populations of the same species and basic metabolic processes are shared across the two species. Finally, this dissertation focuses on validation of cellular networks. This validation framework describes scores for measuring how well network learning algorithms capture higher-order dependencies. This framework also introduces a measure for evaluating the entire inferred network structure based on the extent to which similarly functioning genes are close together on the network

A Study Of Computational Problems In Computational Biology And Social Networks: Cancer Informatics And Cascade Modelling

It is undoubtedly that everything in this world is related and nothing independently exists. Entities interact together to form groups, resulting in many complex networks. Examples involve functional regulation models of proteins in biology, communities of people within social network. Since complex networks are ubiquitous in daily life, network learning had been gaining momentum in a variety of discipline like computer science, economics and biology. This call for new technique in exploring the structure as well as the interactions of network since it provides insight in understanding how the network works and deepening our knowledge of the subject in hand. For example, uncovering proteins modules helps us understand what causes lead to certain disease and how protein co-regulate each others. Therefore, my dissertation takes on problems in computational biology and social network: cancer informatics and cascade model-ling. In cancer informatics, identifying specific genes that cause cancer (driver genes) is crucial in cancer research. The more drivers identified, the more options to treat the cancer with a drug to act on that gene. However, identifying driver gene is not easy. Cancer cells are undergoing rapid mutation and are compromised in regards to the body\u27s normally DNA repair mechanisms. I employed Markov chain, Bayesian network and graphical model to identify cancer drivers. I utilize heterogeneous sources of information to discover cancer drivers and unlocking the mechanism behind cancer. Above all, I encode various pieces of biological information to form a multi-graph and trigger various Markov chains in it and rank the genes in the aftermath. We also leverage probabilistic mixed graphical model to learn the complex and uncertain relationships among various bio-medical data. On the other hand, diffusion of information over the network had drawn up great interest in research community. For example, epidemiologists observe that a person becomes ill but they can neither determine who infected the patient nor the infection rate of each individual. Therefore, it is critical to decipher the mechanism underlying the process since it validates efforts for preventing from virus infections. We come up with a new modeling to model cascade data in three different scenario

Strategies for increasing the applicability of biological network inference

The manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and stress resistant crop development. The construction of interaction maps promises to enhance our ability to engineer cellular behavior. Within the last 15 years, many methods have been developed to infer the structure of the gene regulatory interaction map from gene abundance snapshots provided by high-throughput experimental data. However, relatively little research has focused on using gene regulatory network models for the prediction and manipulation of cellular behavior. This dissertation examines and applies strategies to utilize the predictive power of gene network models to guide experimentation and engineering efforts. First, we developed methods to improve gene network models by integrating interaction evidence sources, in order to utilize the full predictive power of the models. Next, we explored the power of networks models to guide experimental efforts through inference and analysis of a regulatory network in the pathogenic fungus Cryptococcus neoformans. Finally, we develop a novel, network-guided algorithm to select genetic interventions for engineering transcriptional state. We apply this method to select intervention strains for improving biofuel production in a mixed glucose-xylose environment. The contributions in this dissertation provide the first thorough examination, systematic application, and quantitative evaluation of the utilization of network models for guiding cellular engineering

Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

Bayesian network learning and applications in Bioinformatics

Abstract A Bayesian network (BN) is a compact graphic representation of the probabilistic re- lationships among a set of random variables. The advantages of the BN formalism include its rigorous mathematical basis, the characteristics of locality both in knowl- edge representation and during inference, and the innate way to deal with uncertainty. Over the past decades, BNs have gained increasing interests in many areas, including bioinformatics which studies the mathematical and computing approaches to under- stand biological processes. In this thesis, I develop new methods for BN structure learning with applications to bi- ological network reconstruction and assessment. The first application is to reconstruct the genetic regulatory network (GRN), where each gene is modeled as a node and an edge indicates a regulatory relationship between two genes. In this task, we are given time-series microarray gene expression measurements for tens of thousands of genes, which can be modeled as true gene expressions mixed with noise in data generation, variability of the underlying biological systems etc. We develop a novel BN structure learning algorithm for reconstructing GRNs. The second application is to develop a BN method for protein-protein interaction (PPI) assessment. PPIs are the foundation of most biological mechanisms, and the knowl- edge on PPI provides one of the most valuable resources from which annotations of genes and proteins can be discovered. Experimentally, recently-developed high- throughput technologies have been carried out to reveal protein interactions in many organisms. However, high-throughput interaction data often contain a large number of iv spurious interactions. In this thesis, I develop a novel in silico model for PPI assess- ment. Our model is based on a BN that integrates heterogeneous data sources from different organisms. The main contributions are: 1. A new concept to depict the dynamic dependence relationships among random variables, which widely exist in biological processes, such as the relationships among genes and genes' products in regulatory networks and signaling pathways. This con- cept leads to a novel algorithm for dynamic Bayesian network learning. We apply it to time-series microarray gene expression data, and discover some missing links in a well-known regulatory pathway. Those new causal relationships between genes have been found supportive evidences in literature. 2. Discovery and theoretical proof of an asymptotic property of K2 algorithm ( a well-known efficient BN structure learning approach). This property has been used to identify Markov blankets (MB) in a Bayesian network, and further recover the BN structure. This hybrid algorithm is evaluated on a benchmark regulatory pathway, and obtains better results than some state-of-art Bayesian learning approaches. 3. A Bayesian network based integrative method which incorporates heterogeneous data sources from different organisms to predict protein-protein interactions (PPI) in a target organism. The framework is employed in human PPI prediction and in as- sessment of high-throughput PPI data. Furthermore, our experiments reveal some interesting biological results. 4. We introduce the learning of a TAN (Tree Augmented Naïve Bayes) based net- work, which has the computational simplicity and robustness to high-throughput PPI assessment. The empirical results show that our method outperforms naïve Bayes and a manual constructed Bayesian Network, additionally demonstrate sufficient informa- tion from model organisms can achieve high accuracy in PPI prediction

Combining heterogeneous sources of data for the reverse-engineering of gene regulatory networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gene Regulatory Networks (GRNs) represent how genes interact in various cellular processes by describing how the expression level, or activity, of genes can affect the expression of the other genes. Reverse-engineering GRN models can help biologists understand and gain insight into genetic conditions and diseases. Recently, the increasingly widespread use of DNA microarrays, a high-throughput technology that allows the expression of thousands of genes to be measured simultaneously in biological experiments, has led to many datasets of gene expression measurements becoming publicly available and a subsequent explosion of research in the reverse-engineering of GRN models. However, microarray technology has a number of limitations as a data source for the modelling of GRNs, due to concerns over its reliability and the reproducibility of experimental results. The underlying theme of the research presented in this thesis is the incorporation of multiple sources and different types of data into techniques for reverse-engineering or learning GRNs from data. By drawing on many data sources, the resulting network models should be more robust, accurate and reliable than models that have been learnt using a single data source. This is achieved by focusing on two main strands of research. First, the thesis presents some of the earliest work in the incorporation of prior knowledge that has been generated from a large body of scientific papers, for Bayesian network based GRN models. Second, novel methods for the use of multiple microarray datasets to produce Bayesian network based GRN models are introduced. Empirical evaluations are used to show that the incorporation of literature-based prior knowledge and combining multiple microarray datasets can provide an improvement, when compared to the use of a single microarray dataset, for the reverse-engineering of Bayesian network based GRN models