# Revealing mechanisms of specificity and adaptability in molecular information processing through data-driven models > **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2023 · $385,136 ## Abstract Project summary/abstract The success of life on earth derives from its use of molecules to carry information and implement algorithms that control chemistry, allowing organisms to respond adaptively to their environment. The ability to transduce information and respond adaptively ultimately relies on molecular systems being able to selectively recognize one molecular signal from among many other similar signals. The signal could be a molecule (molecular specificity), a combination of molecules (combinatorial specificity), or a time varying concentration pattern (temporal specificity). Further, these molecular systems need to remain adaptable to switch their specificity as needed. The central goal of this proposal is to understand the molecular basis of information processing by building predictive models of molecular, combinatorial and temporal specificity and adaptability of such specificity. We will combine biophysically grounded models, information theory and dynamical systems frameworks for signaling to create data-driven models of molecular, combinatorial and temporal specificity. We will pursue questions on three scales: (1) molecular specificity: how do proteins like antibodies recognize a specific partner, such as an epitope on a viral spike protein, and yet can rapidly change its specificity through mutations? We will develop a biophysically informed machine learning-based toolbox to exploit evolutionary trajectories observed in directed evolution experiments to understand the origin of such adaptability. (2) combinatorial specificity: how do developmental pathways like BMP and TGF-beta resolve specific ligand combinations to determine cell fate, even though each ligand promiscuously binds multiple receptors? We will use an information theory framework for molecular cooperativity to build models of many-many signaling architectures and validate using cell atlas data and experiments that co-express novel combinations of receptor subunits. (3) temporal specificity: how do molecular circuits respond to specific time-varying patterns of concentrations but not others in cytokine signaling and in circadian rhythms? We will develop dynamical systems-theory guided models of stochastic resonance that allow NF-kB to respond to otherwise undetectable levels of cytokines and models of circadian clock-metabolism coupling to understand how cells buffer nutrient fluctuations. Our work is distinguished by combining biophysical models which provide understanding and insight with statistical models that are better able to leverage modern high- throughput data and provide predictive power. In addition, our inference toolboxes and related theory-experiment workflows can used by other labs for similar conceptual questions about alternate systems, such as, molecular specificity for antibodies and spike proteins, combinatorial specificity in the TGF-beta pathway or temporal specificity in EGF signaling respectively for the three thrusts above. ## Key facts - **NIH application ID:** 10715575 - **Project number:** 1R35GM151211-01 - **Recipient organization:** UNIVERSITY OF CHICAGO - **Principal Investigator:** Arvind Murugan - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $385,136 - **Award type:** 1 - **Project period:** 2023-08-01 → 2028-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10715575 ## Citation > US National Institutes of Health, RePORTER application 10715575, Revealing mechanisms of specificity and adaptability in molecular information processing through data-driven models (1R35GM151211-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10715575. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*