Thoughtforms Life Cellular Automata and Models of Health and Disease
Mar 11, 2025
Willem Nielsen, a researcher at the Wolfram Institute, shares his insights on cellular automata models of disease. He explains how single-cell perturbations can mimic diseases and discusses the limitations of traditional disease classification. The conversation explores the predictive power of simple metrics, emphasizing how data transparency can enhance disease modeling. Nielsen illustrates how evolving organisms under stress leads to robustness, paralleling biological processes, and highlights the fascinating concept of planaria as a model for understanding morphological competency and adaptability.
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Diseases Form A Continuum In CA Models
- Cellular automata perturbations show diseases form continuous, overlapping clusters rather than discrete categories.
- Simple observables (e.g., width at one time) often fail to predict long-term outcomes in complex systems.
Collect Richer Biological Data
- Improve biological interfaces and gather richer, faster data to make better clinical predictions.
- Don't rely solely on coarse metrics; increase measurement resolution and throughput to reduce unpredictability.
Evolving CA Rules For Robust Finite Lifespans
- Evolution here mutates rule bits and keeps neutral or beneficial changes to maximize finite lifetime.
- They count rules that loop forever as fitness zero, selecting for robustness against infinite loops.
