ICQMB Center Seminar

Tuesday 2:00-3:00 pm PT

Organizers : Mark Alber / Jia Gou

Past Organizers : Qixuan Wang / Weitao Chen

Please contact the organizer (jgou [at] ucr.edu) if you are interested in attending this seminar.

Fall 2021 Schedule

September 28th Organization meeting (Links: Women in MathArt, Xsede)

October 5th Dr. Artem Kaznatcheev, University of Pennsylvania

title: From games in cancer to endless evolution

October 12th Dr. Dongheon Lee, Duke University

title: Hybrid Modeling Approach to Describe Dynamics of Uncertain Biological Systems

October 19th Dr. Yi Jiang, Georgia State University,

title: Leader, Follower, and Intermediate: Modeling Collective Cancer Invasion

October 26th Dr. Yinglong Miao, University of Kansas

title: Accelerated Molecular Simulations and Drug Discovery

November 2nd Dr. Marcos Nahmad Bensusan, Center for Research and Advanced Studies (Cinvestav) - Mexico

title: Interplay between cell proliferation and recruitment controls the duration of growth and final size of the Drosophila wing

November 9th Dr. Anass Bouchnita, University of Texas at Austin

title: Multiscale and multiphase modeling of blood clot formation in veins, arteries, and aneurysms

November 16th Dr. ​​Jeungeun Park, State University of New York at New Paltz

title: Individual motility pattern and collective movement of flagellated bacteria

November 23rd Dr. William Cannon, Pacific Northwest National Laboratory & University of California, Riverside

title: Learning Regulation from the Ground Up: Combining Natural Selection, Thermodynamics and Data

November 30th Dr. Marco Avila Ponce de Leon, University of California, San Diego

title: A phosphoinositide-based model of actin waves in frustrated phagocytosis

Talk Titles & Abstracts:

Speaker: Dr. Artem Kaznatcheev,

Date: Oct 05th, 2pm (PST)

Title: From games in cancer to endless evolution

Abstract: We can view the problem faced by evolving populations as a game between the population and the environment with the distribution of different phenotypes as the strategy and the evolutionary dynamics as specifying the strategy update rule. This allows us to make global conclusions about evolution without knowing all the reductive details of population structure. I want to provide two examples of how this is useful for both experiment and theory.

For an experimental example, I will introduce you to the game assay for measuring the ecological interactions of evolving populations. Recently, we applied this assay to measure the games played by sensitive vs resistant non-small cell lung cancer. We saw that the games played by these cancer cells are not only quantitatively different between different environments, but drug and fibroblasts qualitatively switch the type of game from Leader to Deadlock. Focusing on frequency dependent fitness also reveals a surprising absence of a cost of resistance in non-small cell lung cancer.

For a theoretical example, I will examine how the combinatorial structure of some static fitness landscapes can produce a computational constraint that prevents evolution from finding any local fitness optima. On the hardest landscapes, no evolutionary dynamics can find a local fitness optimum quickly, thus allowing for open-ended evolution. Knowing this computational constraint allows us to use the tools of theoretical computer science and combinatorial optimization to understand maladaptation and characterize the fitness landscapes that we expect to see in nature.


Kaznatcheev, A., Peacock, J., Basanta, D., Marusyk, A., & Scott, J. G. (2019). Fibroblasts and Alectinib switch the evolutionary games played by non-small cell lung cancer. Nature Ecology and Evolution, 3: 450-456.

Kaznatcheev, A. (2019). Computational complexity as an ultimate constraint on evolution. Genetics, 212(1), 245-265.

Short Bio: Dr. Artem Kaznatcheev is a James S. McDonnell Foundation independent postdoctoral fellow in dynamic and multi-scale systems. He is currently hosted by the University of Pennsylvania Department of Biology. He received his DPhil in Computer Science from the University of Oxford with the support of the Cleveland Clinic Department of Translational Hematology and Oncology Research. Before this, he was at the Moffitt Cancer Center and McGill University. His current research studies the dynamics of cancer and other empirical systems to uncover the algorithms of evolution, understand the computational complexity of the natural world, and use that knowledge to help treat cancer.

Speaker: Dr. Dongheon Lee

Date: Oct 12th, 2pm (PST)

Title: Hybrid Modeling Approach to Describe Dynamics of Uncertain Biological Systems

Abstract: A mathematical model is constructed based on the current knowledge about a biological system to predict its dynamics and test hypotheses of the system of interest. However, predictions from such models are often subject to high uncertainty since many biological systems are only partially understood. To address such uncertainties, I proposed a hybrid modeling approach to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN. As case studies, the proposed approach was implemented to develop hybrid models using in silico and in vitro datasets to describe two different systems more accurately when the available mechanistic models were not complete.

Short Bio: Dr. Dongheon Lee is a postdoctoral researcher at Duke University working with Professor Lingchong You. He received B.S. in Chemical and Biomolecular Engineering from Rice University in 2015 and Ph.D. in Chemical Engineering from Texas A&M University in 2020. His current research focus is to engineer liquid-liquid phase separation (LLPS) to control complex reaction networks in cells. Specifically, he is interested in using LLPS for engineering metabolic pathways as well as designing synthetic genetic circuit. During his graduate study with Professor Joseph S. Kwon, he leveraged his training in process systems engineering to propose methodologies to identify and calibrate mathematical models for describing complex biological systems such as intracellular signaling pathway and lectin-glycan binding kinetics. Dongheon has published 11 papers relating to his work, in addition to six peer-reviewed conference proceedings.

Speaker: Dr. Yi Jiang

Date: Oct 19th, 2pm (PST)

Title: Leader, Follower, and Intermediate: Modeling Collective Cancer Invasion

Abstract: A major reason for cancer treatment failure and disease progression is the heterogeneous composition of tumor cells at the genetic, epigenetic, and phenotypic levels. While tremendous efforts have tried to characterize the makeups of single cells, much less is known about interactions between heterogeneous cancer cells and between cancer cells and the microenvironment in the context of cancer invasion. Clinical studies show that cancer invasion predominantly occurs via collective invasion packs, which invade more aggressively and result in worse outcomes. Using non-small cell lung cancer spheroids, we show that the invasion packs consist of leaders and followers. In vitro and in silico experiments show that leaders and followers engage in mutualistic social interactions during collective invasion. Many fundamental questions remain: What is the division of labor within the heterogeneous invasion pack? How does the leader phenotype emerge? Are phenotypes plastic? How do the invasion packs interact with the stroma? Can the social interaction network be exploited to devise novel treatment strategies? I will present the recent experimental and modeling efforts that try to address these questions. I will try to convince you that analyzing this social interaction network can potentially reveal the ‘weak-links’, which when perturbed can disrupt collective invasion and potentially prevent malignant progression of cancer.

Short Bio: Prof. Yi Jiang received her PhD in Physics at University of Notre Dame under the direction of James Glazier. She joined the Theoretical Division of Los Alamos National Laboratory, first as a postdoc fellow, then as a research scientist. She is currently Frady Whipple Professor at Department of Mathematics and Statistics at Georgia State University. She serves as Associate Editor for Frontier Physiology, PLoS One, and Mathematical Biosciences and Engineering. Her research interests reside between physics, math, biology, and biomedicine, in recent years focusing on modeling of cancer progression, retinal diseases, liver fibrosis, cell migration and cell-ECM interactions.

Speaker: Dr. Yinglong Miao

Date: Oct 26th, 2pm (PST)

Title: Accelerated Biomolecular Modeling and Drug Discovery

Abstract: Biomolecular recognition plays key roles in cellular signaling. It is critical to quantify thermodynamics and kinetics of biomolecular binding for effective therapeutic design. However, such tasks have proven challenging in computational chemistry and biology. Building on a robust Gaussian accelerated molecular dynamics (GaMD) technique, we have developed new Ligand GaMD (LiGaMD)1, Peptide GaMD (Pep-GaMD)2 and Protein-Protein Interaction GaMD (PPI-GaMD)3 algorithms. They have unprecedentedly enabled microsecond atomic simulations to capture repetitive dissociation and binding of small-molecule ligands, highly flexible peptides and proteins, thereby allowing for highly efficient and accurate calculations of the biomolecular binding free energies and kinetics. In addition, I will talk about recent applications of GaMD and ensemble docking in simulations and drug discovery of flexible biomolecules, in particular G-protein-coupled receptors (GPCRs)4, 5.


1. Y. Miao, A. Bhattarai, and J. Wang, J Chem Theory Comput 16 (2020) 5526.

2. J. Wang, and Y. Miao, The Journal of Chemical Physics 153 (2020) 154109.

3. J. Wang, and Y. Miao, bioRxiv (2021) 2021.09.27.461974.

4. A. Bhattarai, J. Wang, and Y. Miao, BBA - General Subjects 1864 (2020) 129615.

5. C. J. Draper-Joyce, R. Bhola, J. Wang et al., Nature 597 (2021) 571.

Speaker: Dr. Marcos Nahmad Bensusan

Date: Nov 2nd, 2pm (PST)

Title: Interplay between cell proliferation and recruitment controls the duration of growth and final size of the Drosophila wing

Abstract: Organ growth is an exponential process driven mainly by cell proliferation, and therefore, even small variations in cell proliferation rates, when integrated over a relatively long time, will lead to large differences in size. How organs robustly control their final size despite perturbations in cell growth and cell proliferation rates is a fundamental question in developmental biology. Here we use a mathematical model to propose that in the developing wing of Drosophila, cell recruitment, a process in which undifferentiated neighbouring cells are incorporated into the wing primordium, determines the time in which growth is arrested in this system. Our model shows that perturbations in proliferation rates of wing-committed cells are compensated by an inversely proportional duration of growth. This mechanism ensures that the final size of the wing is robust in a range of cell proliferation rates. Furthermore, we predict that growth control is lost when fluctuations in cell proliferation affects both wing-committed and recruitable cells. Our model suggests that cell recruitment may act as a temporal controller of growth to buffer fluctuations in cell proliferation rates.

Short Bio: Dr. Marcos Nahmad's lab works on the fundamental question of how global (organ-level) information is perceived and interpreted at the local (cellular level) during organ development and growth. To address these problems he uses tools from genetics, quantitative data analysis, image processing, and mathematical modeling of biological systems. In addition, he is deeply interested in science education and promotes active learning of STEM. He is the founder and organizer of the Science Teaching Workshop and frequently offers workshops on active learning for science teachers. Currently, he coordinates the Masters of Science and PhD programs in the Department of Physiology, Biophysics and Neurosciences at Cinvestav, and teaches at the Medical School of Universidad Anáhuac - Mexico.

Speaker: Dr. Anass Bouchnita

Date: Nov 9th, 2pm (PST)

Title: Multiscale and multiphase modeling of blood clot formation in veins, arteries, and aneurysms

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].


[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.

Short Bio: 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.

Speaker: Dr. Jeungeun Park

Date: Nov 16th, 2pm (PST)

Title: Individual motility pattern and collective movement of flagellated bacteria

Abstract: Bacterial swimming mediated by flagellar rotation is one of the most ubiquitous forms of cellular locomotion, and it plays a major role in many biological processes. A typical swimming path of flagellated bacteria looks like a random walk with no purpose, but the random movement becomes modified as environmental conditions change. Modified random movement is particularly characterized by their motility pattern or a combination of their swimming modes. Further, such individual swimming patterns characterize the collective behavior of a population of the bacteria. In this talk, we present several distinct motility patterns exhibited by bacterial species. We also discuss how to analyze the collective behavior of bacteria from the individual swimming pattern, particularly, by using an example of E. coli’s swimming behavior in response to chemical signals. This talk is based on two joint works with Zahra Aminzare (Univ. of Iowa) and Yongsam Kim (Chung-Ang Univ.), Wanho Lee (National Institute for Mathematical Sciences, South Korea) and Sookkyung Lim (Univ. of Cincinnati).

Short Bio: Dr. Park newly joined the department of Mathematics at State University of New York at New Paltz as an Assistant Professor this semester. Dr. Park is interested in modeling bacterial chemotaxis and analyzing pattern formations. Recently, Dr. Park has been working on modeling swimming behavior of flagellated bacteria at the microscopic and macroscopic levels.

Speaker: Dr. William Cannon

Date: Nov 23rd, 2pm (PST)

Title: Learning Regulation from the Ground Up: Combining Natural Selection, Thermodynamics and Data

Abstract: Modeling cells has many challenges: data is sparse, noisy, and measured over a population instead of over individuals or cell compartments. Moreover, parameters needed to build kinetic and thermodynamic models are extremely labor intensive to obtain. This makes model building a physics-based model a very hard problem. We address this challenge by taking advantage of the fact that natural selection selects for the most optimal individuals out of all solutions. We formulate fitness from a thermodynamic perspective to obtain the most likely model parameters, and then use data to constrain the solution space. Rate parameters that are statistically the most likely can be inferred in this way. Then we predict regulation of the cellular system using one of two approaches: Assuming that we have an optimal control problem and using control theory to infer regulation, or widely sample the solution space for regulation using reinforcement learning. The result is a model with reasonable parameters and predicts regulation for central metabolism that agrees with the literature.

Speaker: Dr. Marco Avila Ponce de Leon

Date: Nov 30th, 2pm (PST)

Title: A phosphoinositide-based model of actin waves in frustrated phagocytosis

Abstract: Phagocytosis is a complex process by which phagocytes such as lymphocytes or macrophages engulf and destroy foreign bodies called pathogens in a tissue. The process is triggered by the detection of antibodies that trigger signaling mechanisms that control the changes of the cellular cytoskeleton needed for engulfment of the pathogen. A mathematical model of the entire process would be extremely complicated, because the signaling and cytoskeletal changes produce large mechanical deformations of the cell. Recent experiments have used a confinement technique that leads to a process called frustrated phagocytosis, in which the membrane does not deform, but rather, signaling triggers actin waves that propagate along the boundary of the cell. This eliminates the large-scale deformations and facilitates modeling of the wave dynamics. Herein we develop a model of the actin dynamics observed in frustrated phagocytosis and show that it can replicate the experimental observations. We identify the key components that control the actin waves and make a number of experimentally-testable predictions. In particular, we predict that diffusion coefficients of membrane-bound species must be larger behind the wavefront to replicate the internal structure of the waves. Our model is a first step toward a more complete model of phagocytosis, and provides insights into circular dorsal ruffles as well.

Short Bio: Dr. Marco Avila Ponce de Leon is currently a Postdoctoral Scholar at the University of California, San Diego working in the Padmini Rangamani Lab. He graduated with a PhD in Mathematics from the University of Minnesota working under Hans Othmer. His research interests are in membrane mechanics and cell signaling.