Inferring Gene Regulatory Networks from Time Series Microarray Data

The innovations and improvements in high-throughput genomic technologies, such as DNA microarray, make it possible for biologists to simultaneously measure dependencies and regulations among genes on a genome-wide scale and provide us genetic information. An important objective of the functional genomics is to understand the controlling mechanism of the expression of these genes and encode the knowledge into gene regulatory network (GRN). To achieve this, computational and statistical algorithms are especially needed. Inference of GRN is a very challenging task for computational biologists because the degree of freedom of the parameters is redundant. Various computational approaches have been proposed for modeling gene regulatory networks, such as Boolean network, differential equations and Bayesian network. There is no so called golden method which can generally give us the best performance for any data set. The research goal is to improve inference accuracy and reduce computational complexity. One of the problems in reconstructing GRN is how to deal with the high dimensionality and short time course gene expression data. In this work, some existing inference algorithms are compared and the limitations lie in that they either suffer from low inference accuracy or computational complexity. To overcome such difficulties, a new approach based on state space model and Expectation-Maximization (EM) algorithms is proposed to model the dynamic system of gene regulation and infer gene regulatory networks. In our model, GRN is represented by a state space model that incorporates noises and has the ability to capture more various biological aspects, such as hidden or missing variables. An EM algorithm is used to estimate the parameters based on the given state space functions and the gene interaction matrix is derived by decomposing the observation matrix using singular value decomposition, and then it is used to infer GRN. The new model is validated using synthetic data sets before applying it to real biological data sets. The results reveal that the developed model can infer the gene regulatory networks from large scale gene expression data and significantly reduce the computational time complexity without losing much inference accuracy compared to dynamic Bayesian network

Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

An investigation into the contribution of gene remodeling to protein coding gene family evolution across the Metazoa

This thesis explores gene evolution throughout the history of Metazoa. This group of multicellular organisms represents a wide range of diversity, embodied by the number of species, the multitude of morphological and developmental traits, and the complexity within the genetic elements dictating these traits. Although a significant amount of research has been carried out on gene family emergence and expansion in animal genomes, comparatively little research has been published on how these genes are formed. Of specific interest here is the role of complex, reticulated mechanisms of gene evolution in forming new genes, these include processes such as gene fusion and fission - hereafter referred to as gene remodeling. Current methods of gene family prediction are not sensitive to these mechanisms of gene evolution. We apply both network and phylogenetic models to characterise the traits and role of gene remodeling across Metazoa and take a data focused approach to attempt to resolve remaining issues within the animal tree of life (AToL). ​In Chapter 2 we took a novel network approach to quantify the contribution of gene remodeling events to novel protein coding gene family evolution in the animal tree of life. Using graph theory we analysed the partial homology shared between a set of animal proteomes spanning most major clades, and we placed these gene remodeling events onto the species tree. In addition to this, in Chapter 3 we sought to assess the phylogenetic properties of these events and their ability to reconstruct AToL, ultimately our aim was to determine if gene fusions could be deployed to resolve contentious regions within AToL. As a consilient approach is most desirable in phylogeny reconstruction, in Chapter 4 we examine the potential for resolving AToL using a combination of data types, i.e. gene fusions and previously published phylogenomic datasets. Specifically, we examined potential issues in annotation of homology and orthology within previously published animal phylogenomic datasets and focussed on determining what impact inaccurate definitions of orthology have had on resolving difficult or contentious parts of AToL

Machine learning approach to reconstructing signalling pathways and interaction networks in biology

In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway

Evolutionary Approaches to the Study of Small Noncoding Regulatory RNA Pathways: A Dissertation

Short noncoding RNAs play roles in regulating nearly every biological process, in nearly every organism, yet the exact function and importance of these molecules remains a subject of some debate. In order to gain a better understanding of the contexts in which these regulators have evolved, I have undertaken a variety of approaches to study the evolutionary history of the components that make up these pathways, in the form of two main research efforts. In the first chapter, I have used a combination of population genetics and molecular evolution techniques to show that proteins involved in the piRNA pathway are rapidly evolving, and that different components of the pathway seem to be evolving rapidly on different timescales. These rapidly evolving piRNA pathway proteins can be loosely separated into two groups. The first group appears to evolve quickly at the species level, perhaps in response to transposons that invade across species lines, while the second group appears to evolve quickly at the level of individual populations, perhaps in response to transposons that are paternally present yet novel to the maternal genome. In the second chapter of my research, I have used molecular evolution techniques and carefully devised controls to show that the binding sites of well-conserved miRNAs are among the most slowly changing short motifs in the genome, consistent with a conserved function for these short RNAs in regulatory pathways that are ancient and extremely slow to change. I have additionally discovered a major flaw in an existing approach to motif turnover calculations, which may lead to systematic biases in the published literature toward the false inference of increased regulatory complexity over time. I have implemented a revised approach to motif turnover that addresses this flaw

Phylogenetics in the Genomic Era

Molecular phylogenetics was born in the middle of the 20th century, when the advent of protein and DNA sequencing offered a novel way to study the evolutionary relationships between living organisms. The first 50 years of the discipline can be seen as a long quest for resolving power. The goal – reconstructing the tree of life – seemed to be unreachable, the methods were heavily debated, and the data limiting. Maybe for these reasons, even the relevance of the whole approach was repeatedly questioned, as part of the so-called molecules versus morphology debate. Controversies often crystalized around long-standing conundrums, such as the origin of land plants, the diversification of placental mammals, or the prokaryote/eukaryote divide. Some of these questions were resolved as gene and species samples increased in size. Over the years, molecular phylogenetics has gradually evolved from a brilliant, revolutionary idea to a mature research field centred on the problem of reliably building trees. This logical progression was abruptly interrupted in the late 2000s. High-throughput sequencing arose and the field suddenly moved into something entirely different. Access to genome-scale data profoundly reshaped the methodological challenges, while opening an amazing range of new application perspectives. Phylogenetics left the realm of systematics to occupy a central place in one of the most exciting research fields of this century – genomics. This is what this book is about: how we do trees, and what we do with trees, in the current phylogenomic era. One obvious, practical consequence of the transition to genome-scale data is that the most widely used tree-building methods, which are based on probabilistic models of sequence evolution, require intensive algorithmic optimization to be applicable to current datasets. This problem is considered in Part 1 of the book, which includes a general introduction to Markov models (Chapter 1.1) and a detailed description of how to optimally design and implement Maximum Likelihood (Chapter 1.2) and Bayesian (Chapter 1.4) phylogenetic inference methods. The importance of the computational aspects of modern phylogenomics is such that efficient software development is a major activity of numerous research groups in the field. We acknowledge this and have included seven "How to" chapters presenting recent updates of major phylogenomic tools – RAxML (Chapter 1.3), PhyloBayes (Chapter 1.5), MACSE (Chapter 2.3), Bgee (Chapter 4.3), RevBayes (Chapter 5.2), Beagle (Chapter 5.4), and BPP (Chapter 5.6). Genome-scale data sets are so large that statistical power, which had been the main limiting factor of phylogenetic inference during previous decades, is no longer a major issue. Massive data sets instead tend to amplify the signal they deliver – be it biological or artefactual – so that bias and inconsistency, instead of sampling variance, are the main problems with phylogenetic inference in the genomic era. Part 2 covers the issues of data quality and model adequacy in phylogenomics. Chapter 2.1 provides an overview of current practice and makes recommendations on how to avoid the more common biases. Two chapters review the challenges and limitations of two key steps of phylogenomic analysis pipelines, sequence alignment (Chapter 2.2) and orthology prediction (Chapter 2.4), which largely determine the reliability of downstream inferences. The performance of tree building methods is also the subject of Chapter 2.5, in which a new approach is introduced to assess the quality of gene trees based on their ability to correctly predict ancestral gene order. Analyses of multiple genes typically recover multiple, distinct trees. Maybe the biggest conceptual advance induced by the phylogenetic to phylogenomic transition is the suggestion that one should not simply aim to reconstruct “the” species tree, but rather to be prepared to make sense of forests of gene trees. Chapter 3.1 reviews the numerous reasons why gene trees can differ from each other and from the species tree, and what the implications are for phylogenetic inference. Chapter 3.2 focuses on gene trees/species trees reconciliation methods that account for gene duplication/loss and horizontal gene transfer among lineages. Incomplete lineage sorting is another major source of phylogenetic incongruence among loci, which recently gained attention and is covered by Chapter 3.3. Chapter 3.4 concludes this part by taking a user’s perspective and examining the pros and cons of concatenation versus separate analysis of gene sequence alignments. Modern genomics is comparative and phylogenetic methods are key to a wide range of questions and analyses relevant to the study of molecular evolution. This is covered by Part 4. We argue that genome annotation, either structural or functional, can only be properly achieved in a phylogenetic context. Chapters 4.1 and 4.2 review the power of these approaches and their connections with the study of gene function. Molecular substitution rates play a key role in our understanding of the prevalence of nearly neutral versus adaptive molecular evolution, and the influence of species traits on genome dynamics (Chapter 4.4). The analysis of substitution rates, and particularly the detection of positive selection, requires sophisticated methods and models of coding sequence evolution (Chapter 4.5). Phylogenomics also offers a unique opportunity to explore evolutionary convergence at a molecular level, thus addressing the long-standing question of predictability versus contingency in evolution (Chapter 4.6). The development of phylogenomics, as reviewed in Parts 1 through 4, has resulted in a powerful conceptual and methodological corpus, which is often reused for addressing problems of interest to biologists from other fields. Part 5 illustrates this application potential via three selected examples. Chapter 5.1 addresses the link between phylogenomics and palaeontology; i.e., how to optimally combine molecular and fossil data for estimating divergence times. Chapter 5.3 emphasizes the importance of the phylogenomic approach in virology and its potential to trace the origin and spread of infectious diseases in space and time. Finally, Chapter 5.5 recalls why phylogenomic methods and the multi-species coalescent model are key in addressing the problem of species delimitation – one of the major goals of taxonomy. It is hard to predict where phylogenomics as a discipline will stand in even 10 years. Maybe a novel technological revolution will bring it to yet another level? We strongly believe, however, that tree thinking will remain pivotal in the treatment and interpretation of the deluge of genomic data to come. Perhaps a prefiguration of the future of our field is provided by the daily monitoring of the current Covid-19 outbreak via the phylogenetic analysis of coronavirus genomic data in quasi real time – a topic of major societal importance, contemporary to the publication of this book, in which phylogenomics is instrumental in helping to fight disease

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward