Bioinformatic Game Theory and Its Application to Biological Affinity Networks


The exact evolutionary history of any set of biological taxa is unknown, and all phylogenetic reconstructions are approximations. The problem becomes harder when one must consider a mix of vertical and lateral phylogenetic signals. In this paper we propose a game theoretic approach to constructing biological networks. The key hypothesis is that evolution is driven by distinct mechanisms that seek to maximize two competing objectives, taxonomic conservation and diversity. One branch of the mathematical theory of games is brought to bear. It translates this evolutionary game hypothesis into a mathematical model in two-player zero-sum games, with the zero-sum assumption conforming to one of the fundamental constraints in nature in mass and energy conservation. We demonstrate why and how a mechanistic and localized adaptation to seek out greater information for conservation and diversity may always lead to a global Nash equilibrium in phylogenetic affinity. Our game theoretic method, referred to as bioinformatic game theory, is used to construct network clusters. As an example, we applied this method to clustering of a multidomain protein family. The protein clusters identified were consistent with known protein subfamilies, indicating that this game-theoretic approach provides a new framework in biological sequence analysis, especially in studying gene-genome and domain-protein relationships.

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B. Deng, B. Hinds, X. Zheng and E. Moriyama, "Bioinformatic Game Theory and Its Application to Biological Affinity Networks," Applied Mathematics, Vol. 4 No. 10B, 2013, pp. 92-108. doi: 10.4236/am.2013.410A2010.

Conflicts of Interest

The authors declare no conflicts of interest.


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