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Principal Investigator: Arvind Murugan
Organization: UNIVERSITY OF CHICAGO
Fiscal Year: 2023
Award: $385,136
Funding agency: National Institute of General Medical Sciences
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.
Terms: <Algorithms><Antibody Specificity><Antigenic Determinants><Architecture><Atlases><Binding><Binding Determinants><Biological Function><Biological Process><Biophysics><Bone-Derived Transforming Growth Factor><Buffers><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><Chemistry><Circadian Rhythms><Coupling><Cytokine Signal Transduction><Cytokine Signaling><Data><Development><Directed Molecular Evolution><EGF><EGF gene><Earth><Engineering><Engineering / Architecture><Environment><Epitopes><Genetic Alteration><Genetic Change><Genetic defect><Goals><Immune system><Immunoglobulin Enhancer-Binding Protein><Information Theory><Intermediary Metabolism><Intracellular Communication and Signaling><Life><Ligands><Machine Learning><Metabolic Processes><Metabolism><Milk Growth Factor><Modeling><Modernization><Molecular><Molecular Interaction><Molecular Modeling Nucleic Acid Biochemistry><Molecular Modeling Protein/Amino Acid Biochemistry><Molecular Models><Mutation><NF-kB><NF-kappa B><NF-kappaB><NFKB><Nuclear Factor kappa B><Nuclear Transcription Factor NF-kB><Nutrient><Nyctohemeral Rhythm><Organism><Pathway interactions><Pattern><Planet Earth><Platelet Transforming Growth Factor><Probabilistic Models><Probability Models><Proteins><Receptor Protein><Research><Sea><Signal Transduction><Signal Transduction Systems><Signaling><Signaling Factor Proto-Oncogene><Signaling Pathway Gene><Signaling Protein><Specificity><Statistical Models><Synthetic antigen binder><System><Systems Theory><TGF B><TGF-beta><TGF-β><TGFbeta><TGFβ><Time><Transcription Factor NF-kB><Transforming Growth Factor beta><Transforming Growth Factor-Beta Family Gene><Twenty-Four Hour Rhythm><Viral><Viral Gene Products><Viral Gene Proteins><Viral Proteins><Work><antibody mimetics><antibody-like binder><antibody-like molecule><antibody-like protein><antibody-like reagent><biological signal transduction><biophysical foundation><biophysical model><biophysical principles><biophysical sciences><circadian clock><circadian pacemaker><circadian process><combinatorial><computational tools><computer based prediction><computerized tools><cytokine><daily biorhythm><data-driven model><developmental><directed evolution><dynamic system><dynamical system><experiment><experimental research><experimental study><experiments><genome mutation><information processing><insight><kappa B Enhancer Binding Protein><living system><machine based learning><model building><molecular modeling><novel><nuclear factor kappa beta><pathway><prediction model><predictive modeling><programs><receptor><statistical linear mixed models><statistical linear models><success><synthetic antigen binding reagent><theories><virus protein>