Generalized Inverse Optimal Control and its Application in Biology

TL;DR

This paper introduces a generalized inverse optimal control framework to infer biological optimality principles directly from data, accounting for complex criteria, constraints, and switches, with applications in bio-medicine and agriculture.

Contribution

It presents a novel, data-driven method for inferring multi-criteria and nested optimality principles in biological systems, incorporating uncertainties and dynamic switches.

Findings

Successfully infers biological optimality principles from experimental data.

Demonstrates the approach's applicability in predicting and controlling biological systems.

Highlights the interdisciplinary effort needed for robust implementation.

Abstract

Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This novel approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness, and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. Furthermore, the inferred principles can be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.