14th European Conference on Mathematical and Theoretical Biology - ECMTB 2026, Graz (Austria). 12-17 julio 2026
Resumen:
Combining RNAseq data and models in microbial ecology aims to reveal species interactions and improve health outcomes. However, data is often noisy, and inferred "interactions" are merely model-dependent correlations rather than direct biological mechanisms. This raises a crucial question: when inferring models from data, are complex models better, or is simplicity more effective?
We argue that minimal models generally outperform complex ones. Using information geometry and Bayesian inference, we demonstrate that simple models maximize reliable information extraction, making them information-theoretically optimal. Furthermore, many widely reported microbial macroecological patterns may simply result from data aggregation or lack the robustness required to be genuine laws.
Resumen divulgativo:
Se propone que el uso de modelos matemáticos simples es más fiable que el de los complejos para entender las bacterias, ya que los datos ruidosos suelen conducir a conclusiones falsas sobre cómo interactúan estos microorganismos.
Fecha de publicación: 12-jul-2026
Cita:
M. Castro, "Simplicity in complexity: A probabilistic view microbial ecology", presentado en 14th European Conference on Mathematical and Theoretical Biology - ECMTB 2026, Graz, Austria, 12-17 julio 2026
IIT-26-099C
