Bayesian Reverse Ecology using Mutual Information

In stochastic biological systems, it is difficult to predict how the state of the system will evolve in response to a dynamic environment. Various attempts have been proposed in different literature. Some papers contain extreme simplicity in the system or suggest a potentially misleading method. In this study, we propose methodology to infer properties of the environment in which an observed system may have evolved: “reverse ecology”. Here, the system can be a cell, and the environment can be everything else other than the cell. We aim to understand the success of a given system compared to all other possible systems in the given environment. From this, we infer the environment that is the most likely one for the system to have arisen in. This Bayesian approach is applied as an inference method that is different from the existing methods. Two different model systems, Poisson distributions and negative binomial distributions, are applied to infer the evolutionary environment from an observed system.