Developing a general structure-to-function theory in noisy biological systems

Develop a predictive theoretical framework that explains how molecular structure gives rise to biological function in complex and noisy biological systems, including proteins, RNAs, metabolites, and other biomolecules.

Background

The paper motivates the need for principled theoretical approaches to connect biomolecular structure with function in inherently stochastic, high-dimensional biological systems. Despite technological advances such as genome sequencing, accurately predicting macromolecular behavior from structure remains challenging due to the vast configuration spaces and reaction networks involved.

Topological methods in discrete stochastic systems are proposed as a promising route to capture robust, system-wide behaviors that are insensitive to local details, potentially contributing to a broader theory linking structure to function.

References

However, we still do not have a good theory for how structure leads to function in these complex and noisy systems.

— Topological phases in discrete stochastic systems  (2406.03925 - Agudo-Canalejo et al., 2024) in Introduction (Section 1)

Although structure-prediction models have made incredible progress, the protein folding problem---essential rules governing fold conservation and the problem of function prediction remain unsolved .

— LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling  (2609.37675 - Banerjee et al., 29 Sep 2026) in Section 1, Introduction