Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We exam...

The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN). For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these pro...

We investigate the ability of artificial Genetic Regulatory Networks (GRNs) to evolve differentiation. The proposed GRN model supports non-linear interaction between regulating factors, thereby facilitating the realization of complex regulatory logics. As a proof of concept we evolve GRNs of this kind to follow different pathways, producing two kinds of periodic dynamics in response to minimal ...

The gene regulatory network (GRN) that underlies the development of the embryonic skeleton in sea urchins is an important model for understanding the architecture and evolution of developmental GRNs. The initial deployment of the network is thought to be regulated by a derepression mechanism, which is mediated by the products of the pmar1 and hesC genes. Here, we show that the activation of the...

background: it is now well-demonstrated that histone demethylases play an important role in developmental controls, cell-fate decisions, and a variety of diseases such as cancer. lysine-specific demethylase 5d (kdm5d) is a male-specific histone demethylase that specifically demethylates di- and tri-methyl h3k4 at the start site of active genes. in this light, the aim of this study was to invest...

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize ...

Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized in cellular subsystems, critical for coherent functioning cells. Here we address questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that may be predicted symmetries (GRN) described by concept symmetry fib...

As with other retinal cell types, retinal ganglion cells (RGCs) arise from multipotent retinal progenitor cells (RPCs), and their formation is regulated by a hierarchical gene-regulatory network (GRN). Within this GRN, three transcription factors--atonal homolog 7 (Atoh7), POU domain, class 4, transcription factor 2 (Pou4f2), and insulin gene enhancer protein 1 (Isl1)--occupy key node positions...

The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference o...

A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of t...