Links :  UCR Interdisciplinary Center for Quantitative Modeling in Biology

     UCR Math Department seminar

               PDE and Applied Math Seminar

Please contact qixuanw@ucr.edu if you are interested in attending this seminar.

Both virtual and in-person talks will provide hybrid options for seminar participants. One might join the seminars in Skye Hall 268, UC Riverside or online through Zoom.

Fall 2023 

October 3, Odo Diekmann, Universiteit Utrecht, Netherlands

October 10, Reka Albert, Pennsylvania State University

October 17, Radek Erban, Oxford

October 24, Denis Headon, University of Edinburgh

October 31, John Dallon, Brigham Young University

November 7, Yougan Cheng, Quantitative Systems Pharmacology at Daiichi Sankyo, Inc.

November 14,  Andrew Krause, Durham University, UK

November 21,

November 28, Jasmine Foo, UMN

December 5, Maziar Raissi, UCR

December 6 (Wednesday, 10:00-10:50 am), in joint with the PDE & Applied Math seminar, Roberto Santoprete, L'Oréal

Incoming talk:

November 28, 2023, 2:00-3:20 PM Pacific Time

Dr. Jasmine Foo, UMN

Title: Computational methods for inferring tumor evolution and heterogeneity

Abstract: Tumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment-failure, confounds current drug screening efforts to identify effective candidate therapies for individual tumors. In the first part of the talk I will present a modeling-driven statistical framework that enables the deconvolution of tumor samples into individual subcomponents exhibiting differential drug-response, using standard bulk drug-screen measurements. In the second part of the talk I will present some efforts towards obtaining insights about tumor evolution from standard genomic data. In particular, we analyze the site frequency spectrum (SFS), a population summary statistic of genomic data, for exponentially growing tumor populations, and we demonstrate how these results can in principle be used to gain insights into tumor evolutionary parameters.

Bio: Jasmine Foo is the Northrop Professor of Mathematics and co-Director of the Therapy Modeling and Design Center at the University of Minnesota-Twin Cities. Her research group is broadly interested in mathematical biology and applied probability/statistics, with a particular focus in modeling cancer dynamics and treatment.   Prof. Foo received her Ph.D. in applied mathematics and Sc.B. in mathematics and physics from Brown University.  She did postdoctoral work at the Memorial Sloan Kettering Cancer Center and Dana Farber Cancer Institute, and subsequently joined the University of Minnesota in 2011, where she currently also serves as associate department head.

December 5, 2023, 2:00-3:20 PM Pacific Time

Dr. Maziar Raissi, UCR

Title: Physics-informed neural network

Abstract: We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.

Bio: Maziar Raissi is an Assistant Professor of Applied Mathematics at the University of California, Riverside. After receiving his Ph.D. in Applied Mathematics & Statistics, and Scientific Computations from the University of Maryland, College Park, he carried out his postdoctoral research in the Division of Applied Mathematics at Brown University. He then worked at NVIDIA in Silicon Valley as a Senior Software Engineer before moving to Boulder, CO where he was an Assistant Professor of Applied Mathematics at the University of Colorado Boulder. Dr. Raissi's expertise lies at the intersection of Probabilistic Machine Learning, Deep Learning, and Data Driven Scientific Computing. He has been actively involved in the design of learning machines that leverage the underlying physical laws and/or governing equations to extract patterns from high-dimensional data generated from experiments.

December 6 (Wednesday), 2023, 10:00-10:50 am, Pacific Time
in joint with the PDE & Applied Math seminar

Dr. Roberto Santoprete, L’Oréal Research & Innovation lab

Title: Physical simulation and digital science: a driving force in cosmetic science

Abstract: Science and technology are at the heart of cosmetic innovation, they allow to reinvent tomorrow's beauty and to guarantee products quality, efficacy and safety. In this talk I will start by a quick overview of the L’Oréal Research & Innovation. Then I will discuss a few examples where digital science allows us to tackle beauty evaluation or product efficacy. Finally I will illustrate an example of how physical simulation and mechanical modelling provide us with a powerful tool to deeply investigate the biological tissues and their aging processes.

Bio: I graduated in physics at  the University of Pisa (Italy). During my PhD at the Federal University of Rio de Janeiro (Brazil) and the Max Planck Institute of Berlin (Germany) I investigated by atomistic computer simulations how mechanical deformations affected the electronical and optical properties of semiconductor nanostructures. Then, during my postdoc at the Ecole Polytechnique in Paris (France), I investigated the process of growth of semiconductor nanostructures by simulating the impact of the experimental condition inside a plasma reactor. Since 2005 I have been working at the L’Oréal Research & Innovation labs as a research engineer and then as a research associate and project leader. I oversee several research projects in the area of biomechanics of the biological tissues and the mechanics of thin polymer films. 

Past talks:

October 3, 2023, 2:00-3:20 PM Pacific Time

Dr. Odo Diekmann, Universiteit Utrecht, Netherlands

Title: Renewal Equations (and why they should be ubiquitous)

Abstract: In order to show that Renewal Equations offer a flexible framework for incorporating history effects and that a substantial body of theory and a growing toolbox exist, I will show

-- how these 'delay equations' arise in infectious disease models and in ecological models

-- how they define dynamical systems and why one needs some functional analysis to build theory for those dynamical systems 

-- how one can use pseudospectral approximation to facilitate numerical bifurcation analysis

-- how, especially in the epidemic context, one can develop discrete time variants that are suitable for simulation.

Bio: Odo Diekmann is Emeritus Professor in Applied Mathematics at Utrecht University, the Netherlands. He is an Honorary Editor of the Journal of Mathematical Biology. His interests are reflected in the titles of the following three books, of which he is a co-author :

i) The Dynamics of Physiologically Structured Populations, with Hans Metz; 

ii) Delay Differential Equations: functional-, complex- and nonlinear analysis, with Stephan van Gils, Sjoerd Verduyn Lunel and Hans-Otto Walther; 

iii) Mathematical Tools for Understanding Infectious Disease Dynamics, with Hans Heesterbeek and Tom Britton. 

October 10, 2023, 2:00-3:20 PM Pacific Time

Dr. Réka Albert, Pennsylvania State University

Title: Network-based dynamic modeling of biological systems: toward understanding and control

Abstract: Cell types and cellular behaviors can be abstracted as attractors of a dynamic system of interacting molecules. My group collaborates with wet-bench biologists to develop and validate Boolean models of specific systems. We use these models to predict interventions that drive the system into desired attractors and away from undesired ones. Several such predictions were validated by our collaborators.  This talk will present two general topics that arose from the specific models.

The first is our identification of stable motifs, which are self-sustaining cyclic structures that determine points of no return in the dynamics of the system.  We have shown that control of stable motifs can guide any system into a desired attractor. We have translated the concept of stable motif to multi-level and ODE models.
The second is our development of a genetic algorithm-based workflow for automated evaluation and refinement of Boolean models. This implementation applies to any biological system for which an interaction network exists and enough perturbation experiments have been done. The genetic algorithm adjusts the functions of the model to enhance agreement with a corpus of curated experimental results. We leverage existing mechanistic knowledge to automatically limit the search space to biologically plausible models. To account for the interdependence of experiment results, we develop a hierarchical scoring technique for assessing model performance. We demonstrate the effectiveness of the workflow by significantly improving a Boolean model previously developed by our group. The principles of our workflow apply to multi-level models.
We expect that decreasing the effort necessary to formulate validated network-based models will increase the applications of stable motif-based attractor control, forming the foundation of therapeutic strategies on a wide application domain.

Bio: Prof. Réka Albert received her Ph.D. in Physics from the University of Notre Dame, working with Prof. Albert-László Barabási, then did postdoctoral research in mathematical biology at the University of Minnesota, working with Prof. Hans G. Othmer. She joined Penn State in 2003, where she currently is a Distinguished Professor of Physics with adjunct appointments in the Department of Biology and the Huck Institute of the Life Sciences. Prof. Albert is a theoretical/computational scientist who works on predictive modeling of biological regulatory networks at multiple levels of organization. She is a fellow of the American Physical Society, the Network Science Society, and the American Association for the Advancement of Science. She is also an external member of the Hungarian Academy of Sciences. She was a recipient of an NSF Career Award (2007), the Maria Goeppert-Mayer Award (2011), and the Distinguished Graduate Alumna Award of the University of Notre Dame (2016). 

October 17, 2023, 2:00-3:20 PM Pacific Time

Dr. Radek Erban, University of Oxford

Title: Multi-resolution simulations of intracellular processes

Abstract: All-atom and coarse-grained molecular dynamics (MD), Langevin dynamics (LD) and Brownian dynamics (BD) are computational methodologies, which have been applied to spatio-temporal modelling of a number of intracellular processes. I will discuss connections between MD, LD and BD, with a focus on the development, analysis and applications of multi-resolution methods, which use (detailed) MD simulations in localized regions of particular interest (in which accuracy and microscopic details are important) and a (less-detailed) coarser stochastic model in other regions in which accuracy may be traded for simulation efficiency. I will discuss applications of multi-resolution methodologies to modelling of intracellular calcium dynamics, actin dynamics and DNA dynamics.

Bio: Radek Erban is a Professor of Mathematics at the University of Oxford and a Fellow of Merton College, Oxford. He works on the development, analysis and application of mathematical and computational methods to a broad range of real-world systems, ranging from molecular-based modelling of intracellular processes at the nanoscale to studying collective behaviour of cells, animals and robots at the macroscale. He received a European Research Council Starting Grant in 2009 and a Philip Leverhulme Prize in 2010. He was a Royal Society University Research Fellow (2011-2019) in Oxford, where he held junior research fellowships at Brasenose College (2011-2014), Somerville College (2008-2011) and Linacre College (2005-2008). In Cambridge, he was also a visiting fellow of Peterhouse (2016) and a recipient of the Simons Foundation Fellowship at the Isaac Newton Institute in 2016. Since 2020, he has been the Chair of the International Advisory Board of the Institute of Mathematics of the Czech Academy of Sciences. 

October 24, 2023, 2:00-3:20 PM Pacific Time

Dr. Denis Headon, University of Edinburgh

Title: Periodic patterning in waves across the skin

Abstract: Periodic patterns are present in the skin of most vertebrates. Their formation is readily mimicked by Turing reaction-diffusion processes. I will discuss the mechanistic basis for the origins of feather, hair and fingerprint patterns in avian and mammalian skin, focussing on:

- the evidence for the action of Turing mechanisms in their formation

- what we know of the specific molecules and processes acting in these mechanisms and their interactions

- the importance of how these systems are deployed the whole field level in shaping the final pattern configuration

Bio: I am an experimental biologist with an interest in how external traits develop and how they tend to vary. I received my PhD from Baylor College of Medicine and am a professor at the Roslin Institute, which is part of the University of Edinburgh.

October 31, 2023, 2:00-3:20 PM Pacific Time

Dr. John Dallon, Brigham Young University

Title: Modeling Intermediate Filament Transport

Abstract: In this talk I will present two models for intermediate filament transport and analyze intermediate filament transport data.  Intermediate filaments are one of three main fibrous structures of the cytoskeleton. How the cell controls the dynamic network made up of intermediate filaments is not understood. We developed two models of intermediate filament transport by motor molecules to help understand how the cell can regulate the network.One model is stochastic and considers individual motor molecules, whereas the other is an ODE formulation.  By comparing results from the two models we can see how noise affects the system.  Finally, we analyze fluorescence recovery after photobleaching (FRAP) data .  

Bio: John Dallon graduated from the University of Utah with a Doctor of Philosophy in mathematics in 1996. He has worked at Brigham Young University since 1999.  Before coming to BYU, Dr. Dallon was a research assistant at the University of Warwick and at Heriot-Watt University.  While at BYU, Dr. Dallon has spent several months visiting: the Cell Polarity, Migration and Cancer Lab at the Institut Pasteur,  Isaac Newton Institute for Mathematical Sciences, Politecnico di Torino, and the Division of Plastic Surgery, Department of Surgery at Penn State University College of Medicine. For more information see his website at  https://math.byu.edu/~dallon/

November 7, 2023, 2:00-3:20 PM Pacific Time

Dr. Yougan Cheng, Quantitative Systems Pharmacology at Daiichi Sankyo, Inc. (DSI)

Title: Virtual Populations for Quantitative Systems Pharmacology Models

Abstract: Quantitative systems pharmacology (QSP) is a discipline that incorporates elements of systems biology and pharmacodynamics with an emphasis on dynamic systems modeling, often with the goal to quantitatively predict the effects of clinical interventions, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require substantial computational time and resources. In this talk, I will present an algorithmically automated iterative VPop development workflow and demonstrate how it was applied to an Immuno-Oncology (I-O) muti-therapy QSP disease platform that focuses on mechanisms of action of a Cytotoxic T Lymphocyte Associated Protein 4 (CTLA4) targeted immunotherapy and a programmed cell death protein 1 (PD-1) targeted immunotherapy. By applying the VPop development workflow, the resulting VPop could accurately predict second line anti-CTLA4 therapy after progression on anti-PD1 therapy, as well as the anti-CTLA4 and anti-PD1 therapy combination response. Applications of I-O QSP disease platforms and VPops to new clinical assets development will also be discussed.

Bio: Yougan Cheng PhD is currently Director, Quantitative Systems Pharmacology at Daiichi Sankyo, Inc. (DSI), leading the I-O QSP Model Development Team responsible for the translational pharmacology and clinical development of assets in Immuno-Oncology using QSP disease modeling. Prior to DSI, he was at Bristol Myers Squibb heading the Oncology/Immuno-Oncology Solid Tumor QSP team. Before that, he was a postdoctoral fellow in the School of Mathematics at the University of Minnesota working on mathematical modeling and computational analysis of cell motility.  Dr. Cheng obtained his PhD in Applied Mathematics from Case Western Reserve University. His dissertation focuses on computational models of brain energy metabolism at different scales.

November 14, 2023, 2:00-3:20 PM Pacific Time

Dr. Andrew Krause, Durham University, UK

Title: Dynamical Systems Approaches and Interactive Visualisations for Pattern Formation

Abstract: Natural patterns, such as those created during embryological development, can arise from enormously complex processes occurring across vast scales of space and time. A key scientific challenge is to conceptually map out these processes in terms of distinct mechanisms, and their interplay. Dynamical systems theory provides several tools for developing hypotheses regarding such processes, and for understanding the limitations of potential mechanisms.
We will discuss the uses and limitations of linear and nonlinear analyses of reaction-transport models in the context of understanding problems of multiscale periodic patterning. A focus will be on understanding robustness and the ability for 'generic' models to exhibit different patterning behaviours, without having to quantify molecular details of a particular system. We will aim to demonstrate how these kinds of models and ideas can help generalize insights from specific systems and numerical simulations, while also discussing fundamental limitations to this kind of modelling. VisualPDE.com will be introduced as a tool to rapidly prototype simple models, as well as to teach and communicate aspects of PDEs more generally. We will end with a range of open problems, both technical and conceptual.

Bio: I am an Assistant Professor in Applied Mathematics at the Department of Mathematical Sciences at Durham University. I am primarily involved in teaching and research within mathematical biology and nonlinear dynamical systems, but I am also interested in broader scientific and philosophical themes. I am chronically-ill and legally blind (very nearsighted).

I grew up in New Mexico, USA, where I earned undergraduate degrees in Mathematics and Computer Science at the New Mexico Institute of Mining and Technology and a Masters degree, in Mathematical Analysis. I then obtained a DPhil (PhD) in Mathematics within the Oxford Centre for Industrial and Applied Mathematics, and continued as a postdoc in the Wolfson Centre for Mathematical Biology before becoming a Departmental Lecturer in Applied Mathematics jointly between these two groups. I then joined Durham in 2021.