Dr. Anass Bouchnita University of Texas at Austin
Abstract: Blood clotting is a complex process involving several processes such as platelet deposition and aggregation, biochemical reactions of the coagulation cascade, and blood flow-clot interactions. To gain insights into this process, it is important to develop computational tools that integrate available knowledge and data across multiple scales of space and time. The first part of this talk will be devoted to the hybrid multiscale modeling of fibrin-platelet thrombus formation in flow. We present a multiscale model that is capable of simulating the formation of both arterial 'white' and venous 'red' thrombi [1]. This model describes fluid dynamics and coagulation kinetics using a continuum approach, while it captures platelet interactions using smooth particle dynamics. The interactions between local hemodynamics and platelets are implemented using the immersed boundary method (IBM). In the second part, we investigate the mechanisms of spontaneous blood clotting in recirculation areas and aneurysms using a multiphase approach. The proposed framework adopts a continuum representation that captures complex fluid biorheology, platelet transport, adhesion and aggregation, and biochemical cascades of plasma coagulation in an efficient way [2]. Numerical simulations elucidate the critical role of neck size, hematocrit level and blood flow intensity on the size and the structure of the clot formed in aneurysms. We will conclude the talk by discussing how these complex models can be combined with machine learning to provide accurate, timely and explainable predictions of patient-specific responses to anticoagulant treatments [3].
References:
[1] Bouchnita, A., & Volpert, V. (2019). A multiscale model of platelet-fibrin thrombus growth in the flow. Computers & Fluids, 184, 10-20.
[2] Bouchnita, A., Belyaev, A. V., & Volpert, V. (2021). Multiphase continuum modeling of thrombosis in aneurysms and recirculation zones. Physics of Fluids, 33(9), 093314.
[3] Bouchnita, A., Nony, P., Llored, J.-P., & Volpert, V. Combining mathematical modelling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow. Submitted.
Bio: Dr. Anass Bouchnita was trained as an engineer in Modelling and Scientific Computing. He holds a double PhD in Modelling and Scientific Computing and in Physiological, Biology of organisms, Populations and Interactions. He is a Postdoctoral Fellow at the University of Texas at Austin, where he participates in the efforts of the COVID-19 Modeling Consortium that aims to project and mitigate the spread of COVID-19 in the Austin area and Texas. He has previously worked as a researcher at Uppsala University and as an assistant professor at Ecole Centrale Casablanca. His research interests include the development of multiscale models and their applications in biology and precision medicine.