Inferring the Chemotaxis Distortion Function from Cellular Decision Strategies

2025-10-30

AI Summary

This study aims to inversely infer the implicit distortion function governing cellular chemotactic decision-making—i.e., the optimal input–output mapping that trades off perceptual fidelity against metabolic resource cost. Method: We propose the first Inverse Blahut–Arimoto Algorithm (IBAA), operating within the rate–distortion theory framework, to reconstruct state-dependent distortion functions solely from observed decision responses. The method integrates simulations of the LEGI (local-excitation/global-inhibition) biochemical model with numerical validation under apoptosis-like scenarios, overcoming limitations of conventional forward modeling. Contribution/Results: IBAA accurately recovers ground-truth distortion functions and reveals cell-specific nonlinear, dynamic decision rules in chemotaxis. Beyond elucidating biological perception–decision mechanisms, this work establishes a new theoretical paradigm applicable to the design and analysis of artificial adaptive systems, demonstrating broad theoretical generality and cross-disciplinary impact.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Cellular intelligence enables cells to process environmental signals and make context-dependent decisions, as exemplified by chemotaxis, where cells navigate chemical gradients despite noisy signaling pathways. To investigate how cells deal with uncertainty, we apply an information-theoretic framework based on rate distortion theory (RDT). 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. We propose the inverse Blahut-Arimoto algorithm (IBAA) 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. We validate the proposed IBAA by accurately estimating theoretical distortion functions in a cellular apoptosis scenario. Additionally, using the local excitation global inhibition (LEGI) model to simulate chemotactic responses, we compute the distortion functions from the cell's perspective. Our finding reveals a state-dependent decision criteria by the cell.

Problem

• Inferring cellular decision criteria from observed chemotaxis strategies
• Developing inverse algorithm to compute distortion functions in signaling
• Quantifying state-dependent decision making under uncertain environmental conditions

Innovation

• Inverse Blahut-Arimoto algorithm computes distortion function
• Framework balances sensing accuracy with resource costs
• Method infers decision criteria from cellular strategies