Biology is full of stunning examples of emergent behaviors ? behaviors that arise from, but cannot be reduced to, the interactions of the constituent parts that make up the system under consideration. These behaviors span the full spectrum of length scales, from the emergence of distinct cell fates (e.g. neurons, muscle, etc.) due to the interactions of genes within cells, to the formation of complex ecological communities arising from the interactions of thousands of species. The overarching goal of my research is to develop new conceptual, theoretical, and computational tools to model such emergent, system-level behaviors in biology. To do so, we utilize an interdisciplinary approach that is grounded in Biological Physics, but draws heavily from Machine Learning, Information Theory, and Theoretical Ecology. Our work is unified and distinguished by our deep commitment to integrating theory with the vast amount of biological data now being generated by modern DNA sequencing-based techniques and quantitative microscopy. An important goal of the proposed research is to find common concepts and tools that transcend traditional biological sub-disciplines and models systems. The proposed research pursues four distinct but conceptually interrelated research directions: (1) understanding how distinct cell fates emerge from bimolecular interactions within mammalian cells (2) investigating how bimolecular networks within cells exploit energy consumption to improve computations, with applications to Synthetic Biology; (3) identifying the ecological principles governing community assembly in microbial communities and developing techniques for synthetically engineering ecological communities; and (4) developing new machine learning algorithms and techniques for biological data analysis. In addition to developing physics-based models for diverse biological phenomena, the proposed research will yield a series of practical important tools and algorithms which we will make publically available including: (1) a new linear-algebra based algorithm for assessing the fidelity of directed differentiation and cellular reprogramming protocols and visualizing reprogramming/differentiation dynamics and (2) improved algorithms for inferring microbial interactions in the human microbiome from high-throughput sequence data. These computational tools will allow scientists to realize the immense therapeutic potential of cellular reprogramming and microbial ecology-based techniques for studying and treating human disease.
This project will develop new theoretical and computational tools that will allow scientists to realize the immense therapeutic potential of cellular reprogramming (the conversion of one cell type into another cell type) and microbial ecology-based techniques for studying and treating human diseases.
|Cui, Wenping; Mehta, Pankaj (2018) Identifying feasible operating regimes for early T-cell recognition: The speed, energy, accuracy trade-off in kinetic proofreading and adaptive sorting. PLoS One 13:e0202331|
|Goldford, Joshua E; Lu, Nanxi; Baji?, Djordje et al. (2018) Emergent simplicity in microbial community assembly. Science 361:469-474|
|Wang, Ching-Hao; Mehta, Pankaj; Elbaum, Michael (2017) Thermodynamic Paradigm for Solution Demixing Inspired by Nuclear Transport in Living Cells. Phys Rev Lett 118:158101|
|Pusuluri, Sai Teja; Lang, Alex H; Mehta, Pankaj et al. (2017) Cellular reprogramming dynamics follow a simple one-dimensional reaction coordinate. Phys Biol :|
|Dame, Keri; Cincotta, Steven; Lang, Alex H et al. (2017) Thyroid Progenitors Are Robustly Derived from Embryonic Stem Cells through Transient, Developmental Stage-Specific Overexpression of Nkx2-1. Stem Cell Reports 8:216-225|