Artificial Gene Regulatory Networks—A Review

On Artificial Gene Regulatory Networks

Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-k...

Effective calibration of artificial gene regulatory networks

Knowing every single component of a given biological system is not enough to understand the complexity of the system but rather it becomes crucial to understand how these components interact with each others. It is not only important the knowledge of genes and proteins, but also knowing their structures and primarily the laws and parameters governing their dynamics, which is often unknown and i...

Key Regulatory Gene Expression in Erythroleukemia Differentiation

The characteristics of cellular and molecular mechanisms associated with cell proliferation and differentiation is important to understand malignancy. In this report we characterise a leukemic model, D5A1, to study the action of differentiation agent, cellular events and gene expression of the selected transcription factors. Cells induced with 4 mM hexamethylene bisacetamide (HMBA) caused signs...

Artificial gene regulatory networks and spatial computation: A case study

This paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchroniz...

A Modular Gene Regulatory Network Model of Artificial Ontogenesis