Information and fitness

The growth rate of organisms depends both on external conditions and on internal states, such as the expression levels of various genes. We show that to achieve a criterion mean growth rate over an ensemble of conditions, the internal variables must carry a minimum number of bits of information about those conditions. Evolutionary competition thus can select for cellular mechanisms that are more efficient in an abstract, information theoretic sense. Estimates based on recent experiments suggest that the minimum information required for reasonable growth rates is close to the maximum information that can be conveyed through biologically realistic regulatory mechanisms. These ideas are applicable most directly to unicellular organisms, but there are analogies to problems in higher organisms, and we suggest new experiments for both cases.

💡 Research Summary

The paper “Information and fitness” develops a quantitative link between the amount of information a cell’s internal state carries about its environment and the cell’s average fitness (or growth rate) across a distribution of environmental conditions. The authors begin by defining a set of external variables s (e.g., concentrations of limiting nutrients) and a set of internal variables g (e.g., expression levels of enzymes). Fitness is modeled as a function f(g,s). In a fluctuating environment described by a probability distribution P(s), a cell cannot perfectly match the optimal internal state g* for each s because of noise and limited regulatory mechanisms. Instead, the cell implements a stochastic mapping P(g|s). Using Shannon’s mutual information I(g;s)=∫P(g,s)log₂