OPUS: Robustness and complexity: how evolution builds precise traits from sloppy components

University Of California-Irvine, Irvine CA

Investigators

Abstract

Organisms often repair damage to their tissues and correct errors in their physiology. Similarly, humans often try to make their engineered systems perform robustly, recovering from damage and errors. This project develops the similarities and differences in how natural biological processes and human engineering build robustly performing systems. Understanding the principles by which organisms are built to perform well provides insight into how things sometimes go wrong and how disease develops. For example, our bodies have many different mechanisms to protect against cancer. The time to failure for systems with multiple protections follows the same pattern in both biology and engineering. By understanding these general characteristics of robustness and failure, we understand better how biological organisms are built, how they fail, and how such principles apply also to human engineered systems. Prior studies by this researcher and many other scientists have developed basic understanding of the principles of robustness and failure. However, the ways in which these insights apply to many problems in biology have not been developed completely. This project will bring together the separate studies of the past into a more complete synthesis. That synthesis will help us to apply what we know to a wider range of problems. The synthesis will also help us to see more clearly what we do not understand, identifying the challenges that need further study. How do organisms actually make relatively precise traits from inherently stochastic and often unreliable biological components? How does precision arise from sloppiness? We know many pieces of the puzzle. Redundancy and extra capacity provide backup. Robustness keeps organisms on track. Repair fixes damage. Signal processing and control follow classic engineering principles. These various mechanisms for regulating traits have important consequences for evolutionary dynamics. For example, when a system corrects component-level errors, the direct selective pressure on component performance declines. Weakened selective pressure on components may cause those components to become less reliable, maintain more genetic variability, or drift neutrally in design, creating the basis for new forms of organismal complexity. To understand how these pieces of the puzzle fit together, recent advances in engineering control theory, computational machine learning, and artificial intelligence augment evolutionary theory to provide a broad conceptual foundation. This project builds on that modern conceptual foundation to synthesize diverse biological theories and to unify a wide variety of biological observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.