Circuit design in biology and machine learning. II. Anomaly detection
This work addresses the gap in applying anomaly detection to cellular and physiological systems, offering an interdisciplinary approach that enhances understanding of computational strategies in biology and AI, though it is incremental in adapting existing ML methods.
The study tackled the problem of applying machine learning anomaly detection principles to biological circuits by developing minimal, cellular-scale circuits inspired by ML techniques, demonstrating that small circuits can effectively classify anomalies and inform hypotheses about cellular circuit design and evolution.
Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and physiological circuits. This study builds on machine learning techniques -- such as dimensionality reduction, boosted decision trees, and anomaly classification -- to develop a conceptual framework for biological circuits. One problem is that machine learning circuits tend to be unrealistically large for use by cellular and physiological systems. I therefore focus on minimal circuits inspired by machine learning concepts, reduced to cellular scale. Through illustrative models, I demonstrate that small circuits can provide useful classification of anomalies. The analysis also shows how principles from machine learning -- such as temporal and atemporal anomaly detection, multivariate signal integration, and hierarchical decision-making cascades -- can inform hypotheses about the design and evolution of cellular circuits. This interdisciplinary approach enhances our understanding of cellular circuits and highlights the universal nature of computational strategies across biological and artificial systems.