Bioinformatic Game Theory and Its Application to Biological Affinity Networks

Abstract

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.work topologies that accurately describe evolutionary history is an open field of research. Early network construction methods often employed some appropriate notion of distance between taxa. Posada and Crandall [4] explain why networks are appropriate representations for several different types of reticulate evolution and describe and compare available methods and software for network estimation. One of the earliest methods for phylogenetic network construction was the statistical geometry method [5]. The authors in [6] use a least-squares fitting technique to infer a reticulated network. Other network construction methods can be found in [2,[7][8][9], each of which is useful in modeling a particular kind of data.Differentiating between vertical and lateral phylogenetic signals is a challenging task in developing accurate models for reticulate evolution. In order to establish a definition for vertical versus lateral transfer it must be that some component of evolutionary signal recovered B. DENG ET AL.