biologyinformed

Biologyinformed is a term used to describe approaches in data analysis, modeling, and design that explicitly incorporate biological knowledge into the method, model, or interpretation. It emphasizes constraining or guiding computational models with mechanistic understanding of biology, rather than relying solely on data-driven pattern discovery or purely theoretical abstractions. In practice, biology-informed methods seek to align models with known biology such as chemical stoichiometry, gene regulatory interactions, metabolic pathways, or evolutionary constraints.

Applications include biology-informed machine learning in genomics, where models incorporate pathway or network information as priors; hybrid models that couple differential equations with data-driven components; and metabolic or signaling models constrained by mass balance and stoichiometry. In synthetic biology, biology-informed design uses regulatory networks and circuit topology to guide experiments. In epidemiology and ecology, models may embed known transmission mechanisms or interaction rules to improve interpretability and extrapolation.

Common techniques include adding biological constraints to loss functions, encoding conservation laws or pathway structures in model architecture, and using priors from curated databases such as KEGG, Reactome, and Gene Ontology. Cross-species transfer learning and multi-task learning can propagate biological priors. Evaluation emphasizes interpretability and robustness, with careful attention to biases from incomplete or contested biological knowledge.

Limitations include potential bias from incomplete knowledge, overconstraining models, and challenges balancing data fit with biological plausibility. The term biologyinformed has gained traction since the mid-2010s with the growth of systems biology and bioengineering, paralleling physics-informed approaches.

See also: systems biology, bioinformatics, physics-informed neural networks, pathway databases.