Genetic Regulatory Networks

Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors and are closer to criticality when chaotic dynamics would be expected, compared to classical RBNs.

BACKGROUND: Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. RESULTS: We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. CONCLUSIONS: This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Escherichia coli and Bacillus subtilis are two of the best-studied prokaryotic model organisms. Previous analyses of their transcriptional regulatory networks have shown that they exhibit high plasticity during evolution and suggested that both converge to scale-free-like structures. Nevertheless, beyond this suggestion, no analyses have been carried out to identify the common systems-level components and principles governing these organisms. Here we show that these two phylogenetically distant organisms follow a set of common novel biologically consistent systems principles revealed by the mathematically and biologically founded natural decomposition approach. The discovered common functional architecture is a diamond-shaped, matryoshka-like, three-layer (coordination, processing, and integration) hierarchy exhibiting feedback, which is shaped by four systems-level components: global transcription factors (global TFs), locally autonomous modules, basal machinery and intermodular genes. The first mathematical criterion to identify global TFs, the κ-value, was reassessed on B. subtilis and confirmed its high predictive power by identifying all the previously reported, plus three potential, master regulators and eight sigma factors. The functionally conserved cores of modules, basal cell machinery, and a set of non-orthologous common physiological global responses were identified via both orthologous genes and non-orthologous conserved functions. This study reveals novel common systems principles maintained between two phylogenetically distant organisms and provides a comparison of their lifestyle adaptations. Our results shed new light on the systems-level principles and the fundamental functions required by bacteria to sustain life.

motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly , i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.

A deeper understanding of the regulation of starch biosynthesis in rice (Oryza sativa) endosperm is crucial in tailoring digestibility without sacrificing grain quality. In this study, significant association peaks on chromosomes 6 and 7 were identified through a genomewide association study (GWAS) of debranched starch structure from grains of a 320 indica rice diversity panel using genotyping data from the high-density rice array. A systems genetics approach that interrelates starch structure data from GWAS to functional pathways from a gene regulatory network identified known genes with high correlation to the proportion of amylose and amylopectin. An SNP in the promoter region of Granule Bound Starch Synthase I was identified along with seven other SNPs to form haplotypes that discriminate samples into different phenotypic ranges of amylose. A GWAS peak on chromosome 7 between LOC_Os07g11020 and LOC_Os07g11520 indexed by a nonsynonymous SNP mutation on exon 5 of a bHLH transcription factor was found to elevate the proportion of amylose at the expense of reduced short-chain amylopectin. Linking starch structure with starch digestibility by determining the kinetics of cooked grain amylolysis of selected haplotypes revealed strong association of starch structure with estimated digestibility kinetics. Combining all results from grain quality genomics, systems genetics, and digestibility phenotyping, we propose target haplotypes for fine-tuning starch structure in rice through marker-assisted breeding that can be used to alter the digestibility of rice grain, thus offering rice consumers a new diet-based intervention to mitigate the impact of nutrition-related noncommunicable diseases.

Bacterial populations transition between growing and non-growing phases, based on nutrient availability and stresses conditions. The hallmark of a growing state is anabolism, including DNA replication and cell division. In contrast, bacteria in a growth-arrested state acquire a resistant physiology and diminished metabolism. However, there is little knowledge on how this transition occurs at the molecular level. Here, we provide new evidence that a multi-element genetic regulatory circuit might work to maintain genetic control among growth-phase transitions in Escherichia coli. This work contributes to the discovering of design principles behind the performance of biological functions, which could be of relevance on the new disciplines of biological engineering and synthetic biology.

... Jung Choi et al [2] studied the variation in co-expression relationships using microarray datasets to introduce a model for finding differential co ... Ayla Ergün et al [4] used an algorithm called “Mode-of-action by Network Identification” MNI, which begins by inferring a global ...

Bacteria express genes in response to environmental signals. Therefore, when scientists study gene expression in vitro, they must avoid the presence of any undesired signal in the medium. To avoid these undesired signals, scientists prepare growth media according well-defined formulas and grown bacteria under strictly controlled conditions. In nature, bacteria must cope with a complex, ever-changing, natural environment, an environment that poses a challenge to any bacterium: How can a cell coordinate multiple signals coming from disparate sources and integrate a response to cope with a complex environment? The answer to this question partially depends on the complex organization of transcriptional regulation and how this organization coordinates and integrates multiple environmental signals. This important issue in current systems and synthetic biology research even has social and ecological implications. Recent studies have shed new light on the principles underpinning the functional architecture of regulatory networks. Applying systems principles to organize the complexity of coordination and integration of multiple environmental cues in bacteria gene expression can reveal this architecture.

Random boolean networks (RBN) have been proposed more than thirty years ago as models of genetic regulatory networks. Recent studies on the perturbation in gene expression levels induced by the knock-out (i.e. silencing) of single genes have shown that simple RBN models give rise to a distribution of the size of the perturbations which is very similar in different model network realizations, and is also very similar to the one actually found in experimental data concerning a unicellular organism (S.cerevisiae). In this paper we present further results, based upon the same set of experiments, concerning the correlation between different perturbations. We compare actual data from S. cerevisiae with the results of simulations concerning RBN models with more than 6000 nodes, and comment on the usefulness and limitations of RBN models.

A decentralised real-time controller for a group of robots is presented, the design of which is inspired by biological genetic regulatory networks. A genetic algorithm (GA) is used to automatically evolve controllers for specific tasks. Results of initial experiments are presented and analysed, which demonstrate that it is possible to successfully evolve the controllers to achieve a simple clustering task. Performance is robust under a variety of parameter choices for the GA and controller.