The discovery of microRNAs (miRNAs) has led to a paradigm shift in our basic understanding of gene regulation. Competing endogenous RNAs (ceRNAs) are the recent entrants adding to the complexities of miRNA mediated gene regulation. ceRNAs are RNAs that share miRNA recognition elements (MREs) thereby regulating each other. It is apparent that miRNAs act as rheostats that fine-tune gene expressio...

Accurate inference of causal gene regulatory networks from gene expression data is an open bioinformatics challenge. Gene interactions are dynamical processes and consequently we can expect that the effect of any regulation action occurs after a certain temporal lag. However such lag is unknown a priori and temporal aspects require specific inference algorithms. In this paper we aim to assess t...

Regulatory divergence is likely a major driving force in evolution. Comparative transcriptomics provides a new glimpse into the evolution of gene regulation. Ascomycota fungi are uniquely suited among eukaryotes for studies of regulatory evolution, because of broad phylogenetic scope, many sequenced genomes, and facility of genomic analysis. Here we review the substantial divergence in gene exp...

Biological processes are governed by complex networks ranging from gene regulation to signal transduction. Positive feedback is a key element in such networks. The regulation enables cells to adopt multiple internal expression states in response to a single external input signal. However, past works lacked a dynamical aspect of this system. To address the dynamical property of the positive feed...

The discovery of microRNAs (miRNAs) has added a new player to the regulation of gene expression. With the increasing number of molecular species involved in gene regulatory networks, it is hard to obtain an intuitive understanding of network dynamics. Mathematical modelling can help dissecting the role of miRNAs in gene regulatory networks, and we shall here review the most recent developments ...

Oligonucleotide microarrays are an informative tool to elucidate gene regulatory networks. In order for gene expression levels to be comparable across microarrays, normalization procedures have to be invoked. A large number of methods have been described to correct for systematic biases in microarray experiments. The performance of these methods has been tested only to a limited extend. Here, w...

Group-living animals must adjust the expression of their social behaviour to changes in their social environment and to transitions between life-history stages, and this social plasticity can be seen as an adaptive trait that can be under positive selection when changes in the environment outpace the rate of genetic evolutionary change. Here, we propose a conceptual framework for understanding ...

Two genes can be co-regulated and possibly have the similar function if they are similarly expressed, which provides a theoretical basis for construction of gene regulatory networks using gene expression data. Herein, a new method of gene regulatory network was constructed based on biclusters in this paper. Given a bicluster, this paper analyzes the correlation between genes in the clusters and...

Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method...