Practical indistinguishability in a gene regulatory network inferenceproblem, a case study

The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.

The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.

To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.

Kolmogorov complexity of epithelial pattern formation: The role of regulatory network configuration

In order to understand and infer a principle underlying biological phenomena, it is necessary to handle massive data of expression patterns, kinetics, and metabolism, so that plausible regulative mechanisms are revealed. The in silico sampling and screening method described in this paper automatically infers possible regulatory network structures using several gene expression profiles. In an experimental evaluation of the feasibility of using this method, each of the possible topologies of three-unit networks were tested exhaustively. After a genetic algorithm was used to identify the parameter set for topology, the plausible topologies were selected by using mutant gene expression data. The experimental results demonstrate that the method caix derive a set of possible network structures that includes the correct one.

Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory networks helps us to understand the complex cellular process. In this paper, we describe a statistical approach for constructing transcriptional regulatory networks using data of gene expression, promoter sequence, and transcription factor binding sites. Our simulation studies show that the overall and false positive error rates in the estimated transcriptional regulatory networks are expected to be small if the systematic noise in the constructed feature matrix is small. Our analysis based on 658 microarray experiments on yeast gene expression programs and 46 transcription factors suggests that the method is capable of identifying significant transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.

Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level, and toward intelligent, efficient, and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein abundance, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is an exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in a public database, along with the confidence held in each edge (if available), can be fed into CABIN as one "evidence network," using the Cytoscape SIF file format. Using CABIN, one may increase the confidence in individual edges in a network inferred by an algorithm in SEBINI, as well as extend such a network by combining it with species-specific or generic information, e.g., known protein-protein interactions or target genes identified for known transcription factors. Thus, the combined SEBINI-CABIN toolkit aids in the more accurate reconstruction of biological networks, with less effort, in less time.A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/SEBINI/RootServlet . Source code and PostgreSQL database schema are available under open source license. ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. CABIN can be downloaded from http://www.sysbio.org/dataresources/cabin.stm . mudita.singhal@pnl.gov.

Structure and evolution of transcriptional regulatory networks

Motivation: When analysing gene expression time series data, an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Although some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data.Results: Here, we present a method that allows us to infer regulatory network structures that may vary between time points, using a set of hidden states that describe the network structure at a given time point. To model the distribution of the hidden states, we have applied the Hierarchical Dirichlet Process Hidden Markov Model, a non-parametric extension of the traditional Hidden Markov Model, which does not require us to fix the number of hidden states in advance. We apply our method to existing microarray expression data as well as demonstrating is efficacy on simulated test data.Contact: thomas.thorne@imperial.ac.uk

Many algorithms have been proposed to learn transcription regulatory networks from gene expression data. Bayesian networks have obtained promising results, in particular, the module network method. The genes in a module share a regulation program (regression tree), consisting of a set of parents and conditional probability distributions. Hence, the method significantly decreases the search space of models and consequently avoids overfitting. The regulation program of a module is normally learned by a deterministic search algorithm, which performs a series of greedy operations to maximize the Bayesian score. The major shortcoming of the deterministic search algorithm is that its result may only represent one of several possible regulation programs. In order to account for the model uncertainty, we propose a regression tree-based Gibbs sampling algorithm for learning regulation programs in module networks. The novelty of this work is that a set of tree operations is defined for generating new regression trees from a given tree and we show that the set of tree operations is sufficient to generate a well mixing Gibbs sampler even in large data sets. The effectiveness of our algorithm is demonstrated by the experiments in synthetic data and real biological data.

Changes in gene expression underlie phenotypic plasticity, variation within species, and phenotypic divergence between species. These expression differences arise from modulation of regulatory networks. To understand the source of expression differences, networks of interactions among genes and gene products that orchestrate gene expression must be considered. Here I review the basic structure of eukaryotic regulatory networks and discuss selected case studies that provide insight into how these networks are altered to create expression differences within and between species.

Functional characterization of the PP2C family reveals NtPP2C171 as a negative regulator of drought tolerance in tobacco

Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.