Information processing in the transcriptional regulatory network of yeast: Functional robustness (original) (raw)
Scale-free Paradigm in Yeast Genetic Regulatory Network Inferred from Microarray Data
2006
Abstract A major challenge of computational biology is the inference of genetic regulatory networks and the identification of their topology from DNA microarray data. Recent results show that scale-free networks play an important role in this context. These networks are characterized by a very small number of highly connected and relevant nodes, and by numerous poorly connected ones. In this paper, we experimentally assess the predictive power of the scale-free paradigm in a supervised learning framework.
Transcriptional Network Structure Assessment Via the Data Processing Inequality
International Journal of Biophysics, 2012
Whole genome transcriptional regulation involves an enormous number of physicochemical processes responsible for phenotypic variability and organismal function. The actual mechanisms of regulation are only partially understood. In this sense, an extremely important conundrum is related with the probabilistic inference of gene regulatory networks. A plethora of different methods and algorithms exists. Many of these algorithms are inspired in statistical mechanics and rely on information theoretical grounds. However, an important shortcoming of most of these methods, when it comes to deconvolute the actual, functional structure of gene regulatory networks lies in the presence of indirect interactions. We present a proposal to discover and assess for such indirect interactions within the framework of information theory by means of the data processing inequality. We also present some actual examples of the applicability of the method in several instances in the field of functional genomics.
Nature Genetics, 2008
A major goal of biology is the construction of networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. A network reconstructed by integrating genotypic, TFBS and PPI data is shown to be the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. The network is also shown to elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. Predictions are prospectively validated to provide direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
On the Contributions of Topological Features to Transcriptional Regulatory Network Robustness
2012
Background: Because biological networks exhibit a high-degree of robustness, a systemic understanding of their architecture and function requires an appraisal of the network design principles that confer robustness. In this project, we conduct a computational study of the contribution of three degree-based topological properties (transcription factor-target ratio, degree distribution, cross-talk suppression) and their combinations on the robustness of transcriptional regulatory networks. We seek to quantify the relative degree of robustness conferred by each property (and combination) and also to determine the extent to which these properties alone can explain the robustness observed in transcriptional networks. Results: To study individual properties and their combinations, we generated synthetic, random networks that retained one or more of the three properties with values derived from either the yeast or E. coli gene regulatory networks. Robustness of these networks were estimated through simulation. Our results indicate that the combination of the three properties we considered explains the majority of the structural robustness observed in the real transcriptional networks. Surprisingly, scale-free degree distribution is, overall, a minor contributor to robustness. Instead, most robustness is gained through topological features that limit the complexity of the overall network and increase the transcription factor subnetwork sparsity. Conclusions: Our work demonstrates that (i) different types of robustness are implemented by different topological aspects of the network and (ii) size and sparsity of the transcription factor subnetwork play an important role for robustness induction. Our results are conserved across yeast and E Coli, which suggests that the design principles examined are present within an array of living systems.
Inferring Gene Regulatory Networks from a Population of Yeast Segregants
Scientific Reports, 2019
Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. In addition, we id...
The topology of the transcription regulatory network in the yeast, Saccharomyces cerevisiae
Physica A: Statistical Mechanics and its Applications, 2003
2 Motivation: A central goal of postgenomic biology is the elucidation of the regulatory relationships among all cellular constituents that together comprise the 'genetic network' of a cell or microorganism. Experimental manipulation of gene activity coupled with the assessment of perturbed transcriptome (i. e., global mRNA expression) patterns represents one approach toward this goal, and may provide a backbone into which other measurements can be later integrated.
Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology
PLOS Computational Biology, 2007
The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems. Citation: Ciliberti S, Martin OC, Wagner A (2007) Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Comput Biol 3(2): e15.
Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling
Methods in Molecular Biology, 2018
Modelling gene regulatory networks not only requires a thorough understanding of the biological system depicted but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarise the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Amongst other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.
Statistical indicators of collective behavior and functional clusters in gene networks of yeast
The European Physical Journal B, 2006
We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles [2]. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.