14th European Conference on Mathematical and Theoretical Biology - ECMTB 2026, Graz (Austria). 12-17 July 2026
Summary:
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.
Spanish layman's summary:
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.
English layman's summary:
We argue that simple mathematical models are more reliable than complex ones for understanding bacteria, as noisy data often leads to false conclusions about how these microorganisms interact.
Publication date: 12-Jul-2026.
Citation:
M. Castro, "Simplicity in complexity: A probabilistic view microbial ecology", presented at 14th European Conference on Mathematical and Theoretical Biology - ECMTB 2026, Graz, Austria, 12-17 July 2026
IIT-26-099C
