Integration of biological system morphogenesis across scales and species through computational modeling
|Interdisciplinary Areas:||Data and Engineering Applications, Engineering-Medicine
This project is related to Dr. Buganza Tepole's effort as part of the Emergent Mechanisms in Biology of Robustness, Integration and Organization (EMBRIO) Institute. A core thrust of this Institute is to determine how multiple biochemical, biomechanical, and bioelectrical signals are integrated to control cell and organismal fate, how convergent and classical evolution have arrived at similar solutions to diverse biological problems, and especially how the integrative processes for morphogenesis scale from single cells to tissues to organisms. As part of this Institute, Dr. Buganza Tepole leads the simulation and integration of mathematical models from different scales and species. To do so, physics-based models at different scales need to be rigorously up- and downscaled, expensive numerical solvers need to be replaced with efficient metamodels, and biological coupling terms needed for control of morphogenesis need to be identified from the data and simulations. The postdoctoral fellow sought in this project will help lead this core integration thrust. Advances in both traditional physics-based modeling and machine learning will be needed to carry out this integration. The Institute brings together a large group of PIs from different institutions, led by Dr. David Umulis, the Chair of the Weldon School of BME.
Applicants with background on the following areas are sought:
* Numerical solution of partial differential equations (PDEs)
* Physics-informed machine learning
Additional qualifications that would make the application extremely competitive:
* Experience in growth, remodeling and morphogenesis modeling
Tac V, Sree VD, Rausch MK, Tepole AB. Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue. arXiv preprint arXiv:2107.05388. 2021 Jul 8.
Burzawa L, Li L, Wang X, Buganza-Tepole A, Umulis DM. Acceleration of PDE-based biological simulation through the development of neural network metamodels. Current Pathobiology Reports. 2020 Nov 6:1-1.
Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ digital medicine. 2019 Nov 25;2(1):1-1.