This paper extends the least cost principle to computational biology, revealing that biological systems optimize reward functions promoting relative motion and quasi-circular orbits. The analysis is validated against established baselines, suggesting the least cost principle as a fundamental organizing principle across disciplines.
Key findings
Least cost principle applied to biological systems optimizes reward functions.
Protein residue interactions, gene expression dynamics, and evolutionary trajectories can be understood through inverse optimal control.
Unified theoretical framework connects physical optimization principles to biological systems.
Limitations & open questions
Further research needed to fully understand the implications of the least cost principle in biology.
Validation against more biological baselines could strengthen the findings.