Data-Calibrated Modeling of Biological Soft Matter with Dissipative Particle Dynamics and High-Performance Bayesian Uncertainty Quantification

Bowman, Clark Michael Riordan

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Year:: 2018
Contributor:: Bowman, Clark Michael Riordan (creator); Matzavinos, Anastasios (Advisor); Stein, Derek (Reader); Chaplain, Mark (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: High performance computing; Computational fluid dynamics; Nanofluids; Soft Matter; Computational biology; Uncertainty--Mathematical models
Extent:: 23, 131 p.
DOI: https://doi.org/10.26300/2wc7-ft27

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Abstract:: Fluids play a key role in the mechanics of cells and the soft-matter structures composing them; from the movement of molecules such as amino acids via diffusion to the viscous forces propelling flagellating organisms, accounting for the role of the fluid environment is critical to understanding the mechanisms at play. One of the major challenges in mathematical modeling of such systems is developing an accurate, yet computationally feasible representation of the fluid. Continuum approaches such as Navier-Stokes neglect the thermal fluctuations and boundary effects than can dominate flow behavior at the nanoscale, while explicit models of the fluid via, e.g., molecular dynamics become computationally infeasible for systems on the order of biological cells which may have millions or billions of molecules. In this thesis, we use dissipative particle dynamics, a particle method which uses coarse-grained particles interacting with artificial forces chosen to generate accurate fluid behavior, to model a number of biofluidic systems of experimental interest, including the diffusion of DNA in constrained environments, the mechanics of poration in phospholipid membranes, and the mechanical force profile of polymerizing actin networks in the cytoskeleton. The simulation results are used to examine at the nanoscale the dynamics underlying macroscopic behaviors observed in experiment, answering a number of questions about the forces, energies, motions, and mechanisms of biological soft matter with measurements from the explicitly modeled fluid environment. We also introduce a framework for high-performance Bayesian uncertainty quantification and demonstrate an application to inferring structural properties of lipids in a bilayer membrane, illustrating the feasibility of data-driven model calibration for our complex dissipative particle simulations using parallel computing. Together, these methods allow for a uniquely fine-scale look at the mechanics underpinning a number of biologically relevant phenomena.
Notes:: Thesis (Ph. D.)--Brown University, 2018

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Bowman, Clark Michael Riordan, "Data-Calibrated Modeling of Biological Soft Matter with Dissipative Particle Dynamics and High-Performance Bayesian Uncertainty Quantification" (2018). Applied Mathematics Theses and Dissertations. Brown Digital Repository. Brown University Library. https://doi.org/10.26300/2wc7-ft27

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Applied Mathematics Theses and Dissertations

Theses and Dissertations for the Applied Mathematics department.
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