Limits...
The stochastic evolutionary game for a population of biological networks under natural selection.

Chen BS, Ho SJ - Evol. Bioinform. Online (2014)

Bottom Line: In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution.In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution.Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics.

View Article: PubMed Central - PubMed

Affiliation: Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.

ABSTRACT
In this study, a population of evolutionary biological networks is described by a stochastic dynamic system with intrinsic random parameter fluctuations due to genetic variations and external disturbances caused by environmental changes in the evolutionary process. Since information on environmental changes is unavailable and their occurrence is unpredictable, they can be considered as a game player with the potential to destroy phenotypic stability. The biological network needs to develop an evolutionary strategy to improve phenotypic stability as much as possible, so it can be considered as another game player in the evolutionary process, ie, a stochastic Nash game of minimizing the maximum network evolution level caused by the worst environmental disturbances. Based on the nonlinear stochastic evolutionary game strategy, we find that some genetic variations can be used in natural selection to construct negative feedback loops, efficiently improving network robustness. This provides larger genetic robustness as a buffer against neutral genetic variations, as well as larger environmental robustness to resist environmental disturbances and maintain a network phenotypic traits in the evolutionary process. In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution. However, if the harbored neutral genetic variations are accumulated to a sufficiently large degree, and environmental disturbances are strong enough that the network robustness can no longer confer enough genetic robustness and environmental robustness, then the phenotype robustness might break down. In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution. Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics.

No MeSH data available.


Related in: MedlinePlus

A real branched metabolic pathway of the generic inhibition and activation model where X1,…, X4 are metabolites.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3928070&req=5

f4-ebo-10-2014-017: A real branched metabolic pathway of the generic inhibition and activation model where X1,…, X4 are metabolites.

Mentions: Consider the generic inhibition and activation model in evolution in Figure 4, which is a known metabolic pathway and has been widely used for imitating characteristics of a real metabolic pathway.30,31 In this metabolic pathway, the metabolite X3 converted from the metabolite X2 inhibits an early step in its own production pathway, which is the synthesis of X1. The metabolite X2 is converted from the metabolite X1, which is a divergence branching point. The degradation processes of X1 into X2 or X4 are independent of each other. Then, the metabolite X4 modulates downstream to activate the transformation of X3. Let’s denote x1(t), x2(t), x3(t), and x4(t) as the concentrations of metabolites X1, X2, X3, and X4, respectively. Based on the metabolic pathway30,31 in evolution in Figure 4, we have the following metabolic reaction network


The stochastic evolutionary game for a population of biological networks under natural selection.

Chen BS, Ho SJ - Evol. Bioinform. Online (2014)

A real branched metabolic pathway of the generic inhibition and activation model where X1,…, X4 are metabolites.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3928070&req=5

f4-ebo-10-2014-017: A real branched metabolic pathway of the generic inhibition and activation model where X1,…, X4 are metabolites.
Mentions: Consider the generic inhibition and activation model in evolution in Figure 4, which is a known metabolic pathway and has been widely used for imitating characteristics of a real metabolic pathway.30,31 In this metabolic pathway, the metabolite X3 converted from the metabolite X2 inhibits an early step in its own production pathway, which is the synthesis of X1. The metabolite X2 is converted from the metabolite X1, which is a divergence branching point. The degradation processes of X1 into X2 or X4 are independent of each other. Then, the metabolite X4 modulates downstream to activate the transformation of X3. Let’s denote x1(t), x2(t), x3(t), and x4(t) as the concentrations of metabolites X1, X2, X3, and X4, respectively. Based on the metabolic pathway30,31 in evolution in Figure 4, we have the following metabolic reaction network

Bottom Line: In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution.In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution.Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics.

View Article: PubMed Central - PubMed

Affiliation: Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.

ABSTRACT
In this study, a population of evolutionary biological networks is described by a stochastic dynamic system with intrinsic random parameter fluctuations due to genetic variations and external disturbances caused by environmental changes in the evolutionary process. Since information on environmental changes is unavailable and their occurrence is unpredictable, they can be considered as a game player with the potential to destroy phenotypic stability. The biological network needs to develop an evolutionary strategy to improve phenotypic stability as much as possible, so it can be considered as another game player in the evolutionary process, ie, a stochastic Nash game of minimizing the maximum network evolution level caused by the worst environmental disturbances. Based on the nonlinear stochastic evolutionary game strategy, we find that some genetic variations can be used in natural selection to construct negative feedback loops, efficiently improving network robustness. This provides larger genetic robustness as a buffer against neutral genetic variations, as well as larger environmental robustness to resist environmental disturbances and maintain a network phenotypic traits in the evolutionary process. In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution. However, if the harbored neutral genetic variations are accumulated to a sufficiently large degree, and environmental disturbances are strong enough that the network robustness can no longer confer enough genetic robustness and environmental robustness, then the phenotype robustness might break down. In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution. Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics.

No MeSH data available.


Related in: MedlinePlus