Early specification of endomesodermal territories in the sea urchin embryo depends on a moving torus of regulatory gene expression. We show how this dynamic patterning function is encoded in a gene regulatory network (GRN) subcircuit that includes the otx, wnt8, and blimp1 genes, the cis-regulatory control systems of which have all been experimentally defined. A cis-regulatory reconstruction ex...

MOTIVATION Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal,...

A gene regulatory network (GRN) controls the process by which the endomesoderm of the sea urchin embryo is specified. In this GRN, the program of gene expression unique to the skeletogenic micromere lineage is set in train by activation of the pmar1 gene. Through a double repression system, this gene is responsible for localization of expression of downstream regulatory and signaling genes to c...

The endomesoderm gene regulatory network (GRN) of C. elegans is a rich resource for studying the properties of cell-fate-specification pathways. This GRN contains both cell-autonomous and cell non-autonomous mechanisms, includes network motifs found in other GRNs, and ties maternal factors to terminal differentiation genes through a regulatory cascade. In most cases, upstream regulators and the...

Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper presents an innovative Collateral-Fuzzy Gene Regulatory Network Reconstruction (CF-GeNe) framework for Gene Regulatory Network (GRN) inference. The approach uses the Collateral Missing Value Estimation (CMVE) algorithm as it...

Though a plethora of techniques have been used to build gene regulatory networks (GRN) from time-series gene expression data, stabilities of such techniques have not been studied. This paper investigates the stability of GRN built using dynamic Bayesian networks (DBN) by synthetically generating gene expression time-series. Assuming scale-free topologies, sample datasets are drawn from DBN to e...

Reverse engineering gene regulatory networks (GRNs), also known as network inference, refers to the process of reconstructing GRNs from gene expression data. Biologists model a GRN as a directed graph in which nodes represent genes and links show regulatory relationships between the genes. By predicting the links to infer a GRN, biologists can gain a better understanding of regulatory circuits ...

Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives information about at which different environmental conditions genes of particular interest get over expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between genes. Interaction can be positive or negative. For inference of GRN, time series data ...

This paper proposes a new method to evaluate the complexity of a Gene Regulatory Network (GRN). It is based on the generation of pictures. In addition to being visually interesting, the pictures shows the capacity of the GRN to produce smooth and/or sudden transitions, fractal-like complexity and regularities. We also have studied the influence of the size of the GRN on the complexity of pictur...

This paper proposes a new method to evaluate the complexity of a Gene Regulatory Network (GRN). It is based on the generation of pictures. In addition to being visually interesting, the pictures shows the capacity of the GRN to produce smooth and/or sudden transitions, fractal-like complexity and regularities. We also have studied the influence of the size of the GRN on the complexity of pictur...