Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and...

An important goal in the field of evolutionary computation and robotics is the development of systems that show self-adaptation in dynamically changing environments. However, adaptation to a changing or fluctuating environment is challenging and usually requires a dynamic solution. Here, we present a bio-inspired, agent-based controller driven by an artificial gene regulatory network that enhan...

The paper presents a methodology for using computational neurogenetic modelling (CNGM) to bring new original insights into how genes influence the dynamics of brain neural networks. CNGM is a novel computational approach to brain neural network modelling that integrates dynamic gene networks with artificial neural network model (ANN). Interaction of genes in neurons affects the dynamics of the ...

Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory base...

Logical models have widely been used to gain insights into the biological behavior of gene regulatory networks (GRNs). Most logical models assume a synchronous update of the genes' states in a GRN. However, this may not be appropriate, because each gene may require a different period of time for changing its state. In this article, asynchronous stochastic Boolean networks (ASBNs) are proposed f...

The regulatory program for animal development is "wired" into the cis-regulatory networks of the genome. At the DNA level these networks consist of the clusters of cis-regulatory transcription factor target sites (modules) that direct the spatial and temporal expression of each phase of activity of those genes; and the linkages amongst them. Here "linkage" refers to the relation between genes e...

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling...

FACULTY OF SCIENCES SCHOOL OF COMPUTING SCIENCES Doctor of Philosophy by Konstantinos Bougioukos In biological systems, gene expression takes place when genes generate gene products and gene expression levels correspond to concentration levels of these products. Gene expression levels within a single cell are determined by a network of regulatory interactions among genes mediated by gene produc...

Understanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on ti...

Deployment of the gene-regulatory network (GRN) responsible for skeletogenesis in the embryo of the sea urchin Strongylocentrotus purpuratus is restricted to the large micromere lineage by a double negative regulatory gate. The gate consists of a GRN subcircuit composed of the pmar1 and hesC genes, which encode repressors and are wired in tandem, plus a set of target regulatory genes under hesC...