Bayesian network modeling for evolutionary genetic structures

Evolutionary theory states that stronger genetic characteristics reflect the organism’s ability to adapt to its environment and to survive the harsh competition faced by every species. Evolution normally takes millions of generations to assess and measure changes in heredity. Determining the connections, which constrain genotypes and lead superior ones to survive is an interesting problem. In order to accelerate this process,we develop an artificial genetic dataset, based on an artificial life (AL) environment genetic expression (ALGAE). ALGAE can provide a useful and unique set of meaningful data, which can not only describe the characteristics of genetic data, but also simplify its complexity for later analysis.To explore the hidden dependencies among the variables, Bayesian Networks (BNs) are used to analyze genotype data derived from simulated evolutionary processes and provide a graphical model to describe various connections among genes. There are a number of models available for data analysis such as artificial neural networks, decision trees, factor analysis, BNs, and so on. Yet BNs have distinct advantages as analytical methods which can discern hidden relationships among variables. Two main approaches, constraint based and score based, have been used to learn the BN structure. However, both suit either sparse structures or dense structures. Firstly, we introduce a hybrid algorithm, called “the E-algorithm”, to complement the benefits and limitations in both approaches for BN structure learning. Testing E-algorithm against a standardized benchmark dataset ALARM, suggests valid and accurate results. BAyesian Network ANAlysis (BANANA) is then developed which incorporates the E-algorithm to analyze the genetic data from ALGAE. The resulting BN topological structure with conditional probabilistic distributions reveals the principles of how survivors adapt during evolution producing an optimal genetic profile for evolutionary fitness.