2.50
Hdl Handle:
http://hdl.handle.net/10033/267853
Title:
Simulated evolution of signal transduction networks.
Authors:
Mobashir, Mohammad; Schraven, Burkhart; Beyer, Tilo
Abstract:
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.
Affiliation:
Institute of Molecular and Clinical Immunology, Otto-von-Guericke University, Magdeburg, Germany.
Citation:
Simulated evolution of signal transduction networks. 2012, 7 (12):e50905 PLoS ONE
Journal:
PloS one
Issue Date:
2012
URI:
http://hdl.handle.net/10033/267853
DOI:
10.1371/journal.pone.0050905
PubMed ID:
23272078
Type:
Article
Language:
en
ISSN:
1932-6203
Appears in Collections:
publications of the department immune control (IMMK)

Full metadata record

DC FieldValue Language
dc.contributor.authorMobashir, Mohammaden_GB
dc.contributor.authorSchraven, Burkharten_GB
dc.contributor.authorBeyer, Tiloen_GB
dc.date.accessioned2013-01-31T12:39:12Z-
dc.date.available2013-01-31T12:39:12Z-
dc.date.issued2012-
dc.identifier.citationSimulated evolution of signal transduction networks. 2012, 7 (12):e50905 PLoS ONEen_GB
dc.identifier.issn1932-6203-
dc.identifier.pmid23272078-
dc.identifier.doi10.1371/journal.pone.0050905-
dc.identifier.urihttp://hdl.handle.net/10033/267853-
dc.description.abstractSignal 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.en_GB
dc.language.isoenen
dc.rightsArchived with thanks to PloS oneen_GB
dc.titleSimulated evolution of signal transduction networks.en
dc.typeArticleen
dc.contributor.departmentInstitute of Molecular and Clinical Immunology, Otto-von-Guericke University, Magdeburg, Germany.en_GB
dc.identifier.journalPloS oneen_GB

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