PICS Colloquium: “Stochastic Reaction-Diffusion-Dynamics Modeling of whole systems: application to fibrin clot contraction and fibrin clot rupture” with Valeri Barsegov
January 30 at 2:00 PM - 3:00 PM
Abstract: Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for particle-based simulations of mechanochemical processes for thermodynamically large systems with high spatial and temporal resolution will be presented. The SRDDM couples the spatially inhomogeneous reaction-diffusion master equation to account for chemical reactions and molecular transport within the Langevin Dynamics (LD) framework to describe force-dependent dynamic processes at the whole system level (e.g. fibrin gel, eukaryotic cell). This computational infrastructure developed allows for the simulation of hours of mechanical processes in reasonable wall-clock time. The SRDDM will be applied to explore: 1) the kinetics, thermodynamics, and mechanisms of blood clot contraction – platelet-driven fibrin network remodeling; and 2) the strength, deformability, damage and fracture toughness of fibrin clot – a prime example of fibrous material networks.
Valeri Barsegov
Professor of Chemistry at the University of Massachusetts Lowell
Dr. Valeri Barsegov is a Chemistry Professor at the University of Massachusetts, Lowell, USA. Dr. Barsegov obtained his PhD degree from the University of Texas at Austin in 2001. He held postdoc positions at the University of Rochester (2001-2003) and Institute for Physical Science and Technology, University of Maryland College Park (2003-2005). Dr. Barsegov’s research in theoretical biophysics and computational material science has contributed to the emergence of new fields of research: (i) biomechanics of hemostasis/thrombosis [NPJ Biol Phys Mech, 2, 6 (2025); Acta Biomaterialia, 190, 329 (2024); ibid 201, 347 (2025); ibid 136, 327 (2021); ibid 131, 355 (2021); PNAS 115, 8575 (2018); Structure 26, 857 (2018); JACS 139, 16168 (2017); ibid 134, 20396 (2012)]; (ii) physical virology [Acta Biomaterialia, 122, 263-277 (2021); Biomacromol 17, 2522 (2016); PLoS Comp Biol 12, e1004729 (2016); JACS 136, 17036 (2014); Biophys J 105, 1893 (2013)]; (iii) high-performance computing [Proteins 78, 2984 (2010); J Phys Chem B 115, 5278 (2011); J Comp Chem 37, 1537 (2016)]; and (iv) computational cell biology [PLoS Comp Biol 18, e1010165 (2022); PNAS, 122, e2416459122 (2025)].