As part of the Engineering Biology Team, David's research is aimed at developing the measurement tools and theoretical approaches necessary for quantitative prediction and control of complex biosystem behavior. He is particularly interested in predictive approaches based on understanding the emergent behavior of biosystems rather than detailed modeling of the molecular mechanisms underlying that behavior. For example, if a biosystem is evolutionarily optimized to perform some function, then, if you can precisely understand and define what "optimized" means, you can calculate the optimal behavior and use it as a prediction – without any specific reference to the underlying mechanisms. This leads to numerous questions which David's research is aimed to address, such as: What are the appropriate "fitness functions" to be optimized? How close are the measureable behaviors to the predicted optimum for real biosystems? And how does this depend on the environmental context that the biosystem is in? When behaviors are not optimal, does a biosystem adapt toward an optimum? And what are the dynamics of that adaptation? Because of the close mathematical connection between growth rates and efficiency of information usage, David's work is focused on theoretical and experimental approaches to understand the adaptation, evolution, and optimization of information flow in complex biological systems, and on the use of that understanding to develop design rules for more robust engineering of biological function.
Postdoctoral Research Opportunities
National Research Council Research Associateship Program at NIST: