Molecular Biophysics (MolBiophys)

How do biomolecules achieve their function?

As biophysics moves from learning molecular structures to learning molecular mechanisms and actions, the Laufer Center has led in physical sciences principles and computational tools. The LC niche has been, in solving the math/physics bottlenecks of biological problems toward bigger-better-faster modeling. We develop computational and experimental models, methods, and tools (see our Resources) that handle ever larger complexes/assemblies and populations, longer timescales and collective actions in the cell, to help discover or design new modulators of function against disease actors such as amyloids, aggregates, deleterious mutations, and disease-causing interactions.

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Cell Biology (CellBio)

How do cells adapt to changing conditions?

How do the behaviors of cells and populations arise from their underlying molecular interactions, biochemical networks, and cycles? How do cells make decisions between survival and death, or engage in different forces, motions, and action, fueled by energy? How do cell populations determine their sizes and growth? What drives cell aging, as functions of metabolism, hormone production, immunity, and sleep?  We explore principles of adaptation , and roles of gene expression, epigenetics and the 4D genome. We design synthetic gene circuits to probe and/or intervene, to affect cell fitness, to regulate actions, and reverse disorders.

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Network Biology (NetBio)

How do biological networks make decisions?

How are biological behaviors encoded in networks of molecular interactions, biochemical pathways, and neuronal circuits?  How are signals in the brain encoded in global patterns? How do immunological synapses trigger responses to antigens; how do metabolic changes modulate those signals in aging and disease? How are cell fate decisions made that drive autophagy, survival, cell death (apoptosis, ferroptosis) or systemic collapse (sepsis)? A challenge is to find subnets having coordinated actions, particularly at the mesoscale between micro and macro. We do experiments on neuronal systems, and synthetic and systems biology, combined with theory and computation, toward developing therapeutic strategies.

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Synthetic Biology (SynBio)

What can we learn from artificial genetic systems?

What can we build from genes and their regulatory elements? How can we control synthetic gene networks and the cells that carry them? How do the behaviors of natural gene networks and cells compare to those of synthetic gene circuits and cells? What can we learn about natural gene networks if we perturb them through synthetic gene circuits? How do synthetic gene circuits function in altered physical, chemical, and biological contexts, such as different temperatures, drugs and host cells? How can we engineer cells to streamline gene circuit insertion? How can we use synthetic gene circuits to better understand single cells’ behavior and cell populations? Can we use synthetic gene circuits as diagnostics or therapeutics? We design synthetic gene circuits to probe and/or intervene, to affect cell fitness, to study evolution, to regulate actions, and reverse disorders.

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Drug Discovery (DrugDisc)

How can we design drugs with desirable actions?

How can we design drugs, or modulators of function, using first principles of chemistry and physics, knowing the target protein? How can we even choose the target protein itself? The answer to the 1st question lies in molecular modeling and simulations of drug-target interactions, to find the best binding pose out of a myriad of conformations. Yet, high binding affinity is not sufficient. We need a deeper understanding of the molecular mechanisms we would like to alter. We consider drug-resistance, specificity, and binding kinetics.  As to the 2nd question, a systems level perspective, connecting drugs to targets, to cellular pathways, and diseases is essential, with the congruence of physics-based models and machine-learning methods. At the Laufer Center, we approach drug discovery from both molecular- and systems-level perspectives.  

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AI  in  Biophysics (AIBio)

How to harness the power of AI in biophysics, and vice versa?

Recent years have seen an explosion in the number of studies developing and using AI tools in a wealth of research areas. A prominent advance has been the development of AlphaFold as an AI-enabled tool for predicting protein structures. Many other data-driven tools have gained traction in various areas of biological, biomedical, and pharmacological sciences. Current AI-powered approaches are a natural culmination of decades-long efforts to generate, organize, and collect data on physicochemical properties, structures, and interactions of biomolecular systems. In the same way as AI benefited from this accumulation of knowledge, LC members are now in a position to harness the power of fundamental principles, theories, and methods of physical sciences to build interpretable AI tools. This is an excellent opportunity to combine these two fields to tackle challenging problems in complex diseases and personalized medicine.