Inferring the Chemotaxis Distortion Function From Cellular Decision Strategies

Abstract

Cellular intelligence allows cells to process environmental signals and make context-dependent decisions. This is clearly seen in chemotaxis, where cells navigate chemical gradients despite noisy signaling pathways. To investigate how cells deal with uncertainty, an information-theoretic framework based on rate-distortion theory (RDT) is applied. The Blahut-Arimoto algorithm (BAA) computes optimal decision strategies that minimize mutual information while satisfying distortion constraints, balancing sensing accuracy with distortion constraint equivalent to resource cost. The inverse Blahut-Arimoto algorithm (IBAA) is proposed to compute the distortion function, which quantifies the system’s decision-making criteria for realizing a decision strategy to map input signals to outputs. This general framework extends beyond chemotaxis to biological and engineered systems requiring efficient information processing under uncertainty. The proposed IBAA is validated by estimating theoretical distortion functions in a binary decision scenario, which is similar to apoptosis. Additionally, using the local excitation global inhibition (LEGI) model to simulate chemotactic responses, the distortion function from the cell’s perspective is computed. The finding reveals that the decision criterion, i.e., the distortion function, of the cell in chemotaxis processes is fixed regardless of the cell’s state. This observation is analogous to any system view where the decision criterion is fixed while the optimal decision strategy can vary based on the environmental conditions.