Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Modeling stochasticity and variability in gene regulatory networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.     

Epitranscriptomic technologies and analyses

RNA can interact with RNA-binding proteins(RBPs), mRNA, or other non-coding RNAs(ncRNAs) to form complex regulatory networks. High-throughput CLIP-seq, degradome-seq, and RNA-RNA interactome sequencing methods represent powerful approaches to identify biologically relevant ncRNA-target and protein-ncRNA interactions. However, assigning ncRNAs to their regulatory target genes or interacting RNA-binding proteins(RBPs) remains technically challenging. Chemical modifications to mRNA also play important roles in regulating gene expression. Investigation of the functional roles of these modifications relies highly on the detection methods used. RNA structure is also critical at nearly every step of the RNA life cycle. In this review, we summarize recent advances and limitations in CLIP technologies and discuss the computational challenges of and bioinformatics tools used for decoding the functions and regulatory networks of ncRNAs. We also summarize methods used to detect RNA modifications and to probe RNA structure.     

Impact of selection on genes involved in regulatory network: a modelling study

Complex phenotypes are often controlled by many interacting genes. One question emerging from such organization is how selection, acting at the phenotypic level, shapes the evolution of genes involved in regulatory networks controlling the phenotypes. We studied this issue through a matrix model of such networks. In a population submitted to selection, we simulated the evolution of a quantitative trait controlled by a set of loci that regulate each other through positive or negative interactions. Investigating several levels of selection intensity on the trait, we studied the evolution of regulation intensity between the genes and the evolution of the genetic diversity of those genes as an indirect measure of the strength of selection acting on them. We show that an increasing intensity of selection on the phenotype leads to an increased level of regulation between the loci. Moreover, we found that the genes responding more strongly to selection within the network were those evolving towards stronger regulatory action on the other genes and/or those that are the less regulated by the other genes. This observation is strongest for an intermediate level of selection. This may explain why several experimental studies have shown evidence of selection on regulatory genes inside gene networks.     

Paralogous antirepressors acting on the master regulator for biofilm formation in Bacillus subtilis

Matrix production during biofilm formation by Bacillus subtilis is governed by a gene control circuit at the heart of which are three dedicated regulatory proteins, the antirepressor SinI, the repressor SinR and the downstream regulator SlrR. Matrix production is triggered by the synthesis of SinI, which binds to and inactivates SinR, thereby derepressing genes for matrix production as well as the gene for SlrR. Recently, two additional regulators of matrix genes were identified: SlrA, which was reported to be an activator of SlrR, and YwcC, a repressor of SlrA synthesis (Kobayashi, 2008). We present evidence indicating that SlrA, which is a paralogue of SinI, is like SinI, an antirepressor that binds to, and inactivates, SinR. We also show that SlrA does not activate SlrR for expression of matrix genes. Instead, SlrR binds to, and inhibits the activity of, SlrA. Thus, the YwcC-SlrA-SinR-SlrR pathway is a negative feedback loop in which SlrA indirectly stimulates the synthesis of SlrR, and SlrR, in turn, inhibits the activity of SlrA. Finally, we report that under standard laboratory conditions SlrA makes only a small contribution to the expression of genes for matrix production. We propose that in response to an unknown signal recognized by the YwcC repressor, SlrA transiently boosts matrix production.     

Biologically meaningful update rules increase the critical connectivity of generalized Kauffman networks

We generalize random Boolean networks by softening the hard binary discretization into multiple discrete states. These multistate networks are generic models of gene regulatory networks, where each gene is known to assume a finite number of functionally different expression levels. We analytically determine the critical connectivity that separates the biologically unfavorable frozen and chaotic regimes. This connectivity is inversely proportional to a parameter which measures the heterogeneity of the update rules. Interestingly, the latter does not necessarily increase with the mean number of discrete states per node. Still, allowing for multiple states decreases the critical connectivity as compared to random Boolean networks, and thus leads to biologically unrealistic situations.Therefore, we study two approaches to increase the critical connectivity. First, we demonstrate that each network can be kept in its frozen regime by sufficiently biasing the update rules. Second, we restrict the randomly chosen update rules to a subclass of biologically more meaningful functions. These functions are characterized based on a thermodynamic model of gene regulation. We analytically show that their usage indeed increases the critical connectivity. From a general point of view, our thermodynamic considerations link discrete and continuous models of gene regulatory networks.