Name
Abstract | ||
|---|---|---|
Dynamical interactions among sets of genes (and their products) regulate developmental processes and some dynamical diseases, like cancer. Gene regulatory networks (GRNs) are directed networks that define interactions (links) among different genes/proteins involved in such processes. Genetic regulation can be modified during the time course of the process, which may imply changes in the nodes activity that leads the system from a specific state to a different one at a later time (dynamics). How the GRN modifies its topology, to properly drive a developmental process, and how this regulation was acquired across evolution are questions that the evolutionary dynamics of gene networks tackles. In the present work we review important methodology in the field and highlight the combination of these methods with evolutionary algorithms. In recent years, this combination has become a powerful tool to fit models with the increasingly available experimental data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/computation3010099 | COMPUTATION |
Keywords | Field | DocType |
evolutionary dynamics, evolutionary algorithms, gene regulatory networks | Gene,Evolutionary algorithm,Biology,Inference,Computational biology,Evolutionary dynamics,Gene regulatory network,Genetics | Journal |
Volume | Issue | ISSN |
3 | 1 | 2079-3197 |
Citations | PageRank | References |
1 | 0.37 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Aguilar-Hidalgo | 1 | 3 | 1.79 |
| Maria Carmen Lemos | 2 | 3 | 2.53 |
| Antonio Córdoba | 3 | 1 | 1.38 |