Simulated Evolution of Signal Transduction Networks
Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually tra...
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Public Library of Science
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521023/ |
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pubmed-3521023 |
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pubmed-35210232012-12-27 Simulated Evolution of Signal Transduction Networks Mobashir, Mohammad Schraven, Burkhart Beyer, Tilo Research Article Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network. Public Library of Science 2012-12-12 /pmc/articles/PMC3521023/ /pubmed/23272078 http://dx.doi.org/10.1371/journal.pone.0050905 Text en © 2012 Mobashir et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Mobashir, Mohammad Schraven, Burkhart Beyer, Tilo |
| spellingShingle |
Mobashir, Mohammad Schraven, Burkhart Beyer, Tilo Simulated Evolution of Signal Transduction Networks |
| author_facet |
Mobashir, Mohammad Schraven, Burkhart Beyer, Tilo |
| author_sort |
Mobashir, Mohammad |
| title |
Simulated Evolution of Signal Transduction Networks |
| title_short |
Simulated Evolution of Signal Transduction Networks |
| title_full |
Simulated Evolution of Signal Transduction Networks |
| title_fullStr |
Simulated Evolution of Signal Transduction Networks |
| title_full_unstemmed |
Simulated Evolution of Signal Transduction Networks |
| title_sort |
simulated evolution of signal transduction networks |
| description |
Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network. |
| publisher |
Public Library of Science |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521023/ |
| _version_ |
1611940040380776448 |