Predictive Modeling in Biology and Medicine Conference - Abstracts

Plenary & Invited Speaker Titles & Abstracts


Belinda Akpa
Department of Chemical and Biomolecular Engineering, UT Knoxville, TN

Title: Start with the end in mind: systems modeling to inform molecular design

Abstract: Drug discovery is a molecular search task with a complex objective: modify the function of a complex biological system to interrupt disease processes. Conventionally, it is a costly, high failure-rate process – with molecular candidates clearing preclinical safety and efficacy hurdles only to fail upon delivery to humans. This happens partly because early screens in the discovery pipeline fall short of capturing the ultimate therapeutic value of new molecular candidates. For a molecule to become a successful drug, it should: (1) bind to a desired target protein; (2) be deliverable from a desired site of administration (oral, intravenous, etc.) to the physiological site of activity, with sufficient concentration for a sufficient duration of time; and (3) promote the desired pharmacological effect without causing unwanted toxicity. The chemical space that meets one of these objectives likely requires compromises in another, as binding, delivery, and activity depend on coupled and dynamic biophysical and biochemical interactions. To help improve the success rate of drug discovery, we should ideally look at design through the lens of human physiology. Quantitative systems pharmacology models could offer the molecule-to-therapeutic-outcome mapping required to inform AI-driven drug design. However, these models present multiple challenges – from the complexity of the biological pathways driving disease processes to the knowledge gaps limiting model construction and parameterization, to the challenges presented by data limitations and the relative computational expense of mechanistic systems models. In this talk, I will present our work on enabling physiology-informed, AI-driven design of new therapeutics.

Paul Atzberger
Department of Mathematics, UC Santa Barbara, CA

Title: Strategies and tools for data-driven modeling and simulation of biological soft materials and stochastic non-linear dynamics

Abstract: Recent emerging machine learning methods are presenting new opportunities for grappling with the complexity of biological systems to develop more accurate models and simulations.  We will discuss data-driven methods and related open source software tools our group is developing for learning representations of non-linear dynamics, unknown forces-laws, and reduced-order models.  We first discuss challenges in biological soft materials in developing coarse-grained models and constitutive relations taking into account the roles of microstructure mechanics, hydrodynamic coupling, and thermal fluctuations.  We show how data-driven approaches can be used for modeling and simulation of complex fluids and drift-diffusion dynamics in cellular processes.  We then discuss more generally how representations can be learned for non-linear stochastic dynamics leveraging recent data-driven methods related to variational autoencoders and generative approaches.  More information on the related open source software packages can be found at

Arif Badrou; PhD1; Mona Eskandari, PhD2
1Department of Mechanical Engineering, UC Riverside
2BREATHE Center, School of Medicine, UC Riverside

Title: Towards Predictive Breathing Lung Model: Bridging Experimental Data and Finite Element Models Using Inverse Techniques

Abstract: Pulmonary diseases such as COVID-19 and Chronic Obstructive Pulmonary Disease (COPD) constitute a major cause of mortality and morbidity in the world. Understanding the mechanism inherent in breathing holds the potential to advance both diagnosis and treatment. Our research group engages in comprehensive investigations of lung physiology through a combination of experimental and computational approaches. On the experimental front, our efforts encompass the study of various lung components in diverse species, including mice, rats, pigs, and humans. Our lab has custom-designed a unique testing platform capable of simultaneously replicating diaphragm breathing and artificial ventilation, complemented by high spatial and temporal resolution cameras for novel Digital Image Correlation (DIC) analysis adapted to the notably large and fast deformations that constitute the lung. Additionally, at the tissue level, extensive explorations of biaxial tensile tests and indentation measurements from our lab inform the mechanical properties of various constituents comprising the lung (such as the parenchyma, pleura etc.) and helps to further inform computational models. This talk will focus on the utilization of these experimental data to build Finite Element Models of a breathing dynamic human lung using inverse procedures. We provide two examples: the first will delve into the development of a reduced-order surface model of a pig lung specimen informed and validated by 3D DIC. This pipeline serves as proof of concept for the development of a poroelastic model of a human lung, currently under development with representation of the complex airway network and pressure-distribution during various breathing patterns, as validated against experimental measurements. We will conclude by exploring the many possibilities for in-silico lung studies aimed at developing predictive technologies.

Mikhal Banwarth-Kuhn
California State University at East Bay

Title: What is actually the point?

Abstract: If you’ve ever taken or taught a differential equations class, you probably found that the main goal of the course was to teach paper-and-pencil techniques to produce explicit solutions. And if you’ve ever used differential equations to model a real-life scenario you most likely found that you had little hope of writing down an explicit solution- and you definitely did not get very far without a computer. There is a critical need for robust strategies to analyze, model, and interpret large amounts of data to explain and predict biological and medical phenomena. It’s for precisely this reason we need to bring our math classes into the 21st century. I invite you to join me as we brainstorm what a 21st century calculus or differential equations class could look like.  In my talk I will share a little about a new calculus course I am developing for the data science major at Cal State East Bay as well as some of the success stories from other institutions that have rethought calculus to better prepare students for studying critical questions in biology and medicine.

Suncica Canic
Department of Mathematics, UC Berkeley, CA

Title: A Mathematical and Computational Approach to the Design of a Bioartificial Pancreas

Abstract: This talk will address the design of a first implantable bioartificial pancreas without the need for immunosuppressant therapy. The design is based on transplanting the healthy (donor) pancreatic cells into a poroelastic medium (alginate hydrogel, or agarose gel) and encapsulating the cell-containing medium between two nanopore semi-permeable membranes.  The nanopore membranes are manufactured to block the immune cells from attacking the organ, while allowing passage of nutrients and oxygen to keep the transplanted cells viable as long as possible. The key challenge is maintaining the survival of transplanted pancreatic cells for an extended period of time by providing sufficient oxygen supply. This challenge is addressed via our nonlinear, multi-scale, multi-physics mathematical and computational models. At the macro scale we designed a nonlinear fluid-poroelastic structure interaction model to study the flow of blood in the bioartificial pancreas, coupled to a nonlinear advection-reaction-diffusion model to study oxygen supply to the cells. At the micro-scale, we use particle based simulations (Smoothed Particle Hydrodynamics) in conjunction with Encoder-Decoder Convolution Neural Networks to capture the fine micro- structure (architecture) of hydrogels and how the architecture influences the macro-scale parameters, such as the spatially dependent permeability tensor. These models inspired the design of a second-generation bioartificial pancreas. They also initiated the development of new mathematical analysis approaches to study multi-layered poroelastic media interacting with incompressible, viscous fluids. Parts of this work are joint with biomedical engineer S. Roy (UCSF), and mathematicians Y. Wang (Texas Tech), J. Webster (University of Maryland Baltimore County), L. Bociu (North Carolina State University), and B. Muha (University of Zagreb, Croatia).

William Cannon
Chief Scientist
Pacific Northwest National Laboratory, Richland WA
Adjunct Faculty
Department of Mathematics, UC Riverside, Riverside, CA

Title: Predicting Cellular Regulation from Natural Selection, Thermodynamics and Control Theory

Abstract: Predicting cellular regulation is a grand challenge in biology. We address this challenge by taking advantage of the fact that natural selection selects for the most fit or optimal individuals out of all solutions. We formulate fitness from a thermodynamic perspective to obtain the most likely kinetic parameters, and then use information derived from data to constrain the solution space. For instance, data on metabolite levels shows that metabolites rarely exceed 20 mM, arguably because the cytoplasm otherwise becomes too viscous for diffusion to occur. Likewise, data shows that proteins are expressed only as much as needed, because otherwise the cell is wasting precious resources and energy. This information is thermodynamic in nature, and these thermodynamic principles can be exploited to predict regulation based on fitness. We provide examples in both bacteria and fungi.

Maria R D'Orsogna
California State University, Northridge
Authors: Lucas Boettcher, Tom Chou, Maria R D'Orsogna

Title: Analyzing past and forecasting future drug overdose mortality in the United States

Abstract: Fatal drug overdoses in the United States have reached unprecedented levels, with almost 110,000 fatalities in the year 2022 alone. The epidemic has evolved over time impacting various age, gender, race and ethnic groups in different ways. In this talk we analyze CDC-WONDER data pertaining to the so-called "third wave" of fatal overdoses, which began in 2013 with the introduction of synthetic opioids to the US market, and study patterns of mortality due to heroin, fentanyl, prescription painkillers and meth overdoses in various demographic and geographic groups. We also present an age-structured model of addiction and overdose deaths and couple it with CDC WONDER observations through a Kalman Filter to offer forecasts of future overdose patterns both nationwide and in three select areas: Los Angeles County, Cook County and the five boroughs of New York City. Understanding and forecasting drug overdose patterns for given geographic or demographic groups can help the design of prevention and treatment measures to better serve the most at-risk communities.

German Enciso
University of California, Irvine

Title: Stochastic Chemical Reaction Modeling of Shadow Enhancers

Abstract: Enhancers are short DNA regulatory sequences that are removed from the promoter region, often observed during development. When multiple enhancers regulate a gene at the same developmental time and in the same cell, they are called shadow enhancers.  In this short presentation I will describe recent work modeling shadow enhancers in Drosophila, with an aim to providing answers to questions such as why there are multiple shadow enhancers rather than a single enhancer.   We use notation from stochastic chemical reaction networks for the mathematical models.

Uduak George
Associate Professor
Department of Mathematics and Statistics
San Diego State University

Title: Mechanical perturbations and branching morphogenesis: findings from mathematical modeling and laboratory experiments

Abstract: Branching morphogenesis governs the formation of tree-like organs such as the mammary glands and lungs. Defects in branching morphogenesis may lead to poor organ function. Understanding the mechanisms that generate branched organs could potentially advance knowledge for regenerating organ function and/or creating artificial organs as a means to combat diseases. Mechanical signaling is believed to regulate branching morphogenesis but how this occurs is not well understood. In particular, how mechanical forces affect branch distributions/organization is not well understood. A holistic approach is essential to understand the intricate interactions that coordinate the formation of branched organs. In this talk, I will describe how we are using combinations of laboratory experiments, digital image analysis, agent-based models and multifractal models to shed light on the effect of mechanical forces on branching morphogenesis.

Marcella Gomez
Associate Professor, Applied Mathematics
Associate Dean for Diversity, Equity, and Inclusion, Baskin Engineering

Title: Data-driven approaches to quantifying states and driving outcomes of complex biological systems

Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori.  Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. We note that a major challenge is the lack of methods to measure system progression and response in real-time. Application areas that have advanced the most are those associated with measurable metrics. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and present a transcriptomic based model of wound healing to track wound stage progression.

Nan Hao
Department of Molecular Biology

University of California San Diego

Title: Engineering Longevity – Computationally-Guided Reprogramming of Single-Cell Aging

Abstract: In this talk, I will present our recent work that combined high-throughput dynamic measurement technologies with math-based theoretical frameworks to interrogate how intracellular molecular networks govern aging processes. Specifically, we investigated single-cell aging dynamics throughout the replicative lifespans of S. cerevisiae, and found that isogenic cells diverge towards two aging paths, with distinct phenotypic changes and death forms. We developed a nonlinear dynamic model of the underlying molecular network of aging, which quantitatively simulated divergent aging trajectories and guided the engineering of a synthetic gene circuit to substantially extend lifespan. Our results establish a causal connection between gene network architecture and cellular longevity and set the stage for the rational design of synthetic gene networks that can effectively slow aging in more complex organisms.

Alexander Hoffmann
Signaling Systems Laboratory, Department of Microbiology,
Immunology, Molecular Genetics, and
Institute for Quantitative and Computational Biosciences, UCLA, CA

Title: Nongenetic heterogeneity of B-cells and antibody affinity maturation

Abstract: Nongenetic heterogeneity of B-cells and antibody affinity maturation Effective vaccine responses depend on a Darwinian selection process involving B-cell somatic hyper-mutation of the antibody genes and selection by antigen affinity. Yet B-cell fates are known to be highly variable and the stochastic Cyton fate decision model accounts for population dynamics. We recently showed by live cell microscopy that propensities for cell fate decisions are heritable through the proliferative burst. A multiscale mechanistic model of the molecular network was used to quantify the variability and heritability of molecular components and parameters (PNAS 115, E2888). In new work we have explored with a mathematical model the consequence of non-genetic B- cell variability and heritability on the resulting antibody repertoire. Considering a classical Darwinian process, stochastic variability has a detrimental effect but heritable variability of founders can accelerate affinity maturation. However, antibodies are only secreted by plasma cells into which high affinity B-cell must differentiate. Incorporating this fate decisions into the model actually renders non-genetic heterogeneity an asset for generating high affinity antibody repertoire, and heritability of those cell states further enhances it. Our study reveals unappreciated roles of cell variability and emphasizes that pre-existing heterogeneity vs stochasticity may have critically different biological consequences.

Reinhard C. Laubenbacher, Ph.D.
Dean’s Professor of Systems Medicine
Director, Laboratory for Systems Medicine 

Division of Pulmonary, Critical Care, and Sleep Medicine
Department of Medicine 
University of Florida        

Title: Multi-scale modeling of the early immune response to respiratory infections

Abstract: Respiratory infections are a significant health burden throughout the world and are a major cause of hospital admissions for pneumonia and other complications. Immune system status and the early immune response is an important determinant of disease progression. This talk will describe a multi-scale computational model of the innate immune response toward fungal and viral pathogens. It will also describe some of the technical challenges that models of this type present.

Brittany Bannish Laverty
Department of Mathematics & Statistics
University of Central Oklahoma

Title: Interdisciplinary Collaboration in Blood Clot Degradation

Abstract: Blood clots are critical to prevent bleeding, but dangerous complications such as heart attack and stroke can arise when clots are not degraded effectively. A clot is composed of red blood cells and platelets held together by a mesh of fibrin fibers. The conditions in which a clot forms impact the resulting clot structure, hence the ease with which the clot is enzymatically degraded. I will present a stochastic multiscale model of clot degradation that includes structural and biochemical details from the single fiber to full clot scales. I will highlight how mathematical modeling has been used in tandem with laboratory experimentation to yield physiological insights that were impossible with models or experiments alone.

Philip K. Maini
Wolfson Centre for Mathematical Biology
Mathematical Institute, Oxford University, UK

Title: Combining theory and experiment to move forward in understanding collective cell migration.

Abstract: Collective cell migration is a very common feature of biological systems, occurring in normal development, wound healing, and disease. In this talk, I will review work from our long-standing collaboration with experimental biologists (the Kulesa laboratory) on neural crest cell migration. Using mathematical modelling to test/generate hypotheses, combined with experimental validation, I will illustrate how we have expanded our knowledge of key aspects of this phenomenon, which is not only of importance in normal development, but also in cancer.

Roeland Merks
Universiteit Leiden

Title: Hybrid cellular Potts modeling of cell-extracellular matrix interactions driving cell shape, cell migration and collective cell behavior

Abstract: To form the patterns and behaviors that we observe in multicellular development, cells must carefully coordinate their behavior through biophysical and biochemical cues. Numerical modeling and theory are essential for analyzing the mechanism of such coordinated, collective cell behavior. To do so, single-cell models must be sufficiently detailed so they correctly capture essential aspects of individual cells and do not oversimplify. At the same time, the models must be sufficiently simple and computationally efficient so general principles can be understood and the models can be upscaled to multicellular systems. My team analyzes single cell behavior and multicellular development using a combination of mathematical, computational and experimental approaches. Our central tool is the cellular Potts model (CPM), a widely-used, lattice-based framework for modeling cell behavior. We typically couple the CPM with simulation models of the cellular microenvironment and relevant intracellular dynamics, a technique known as hybrid CPMs. I will present a series of our recent hybrid CPMs for modeling individual cell behavior, and show how these can be used to study the coordinated cell behavior that is seen in biological development. I will first discuss a series of models used to analyze observations such as anomalous cell migration patterns of immune cells, the effect of extracellular matrix stiffness on cell shape, cellular force transduction in fibrous ECMs, and models of anisotropic force generation. I will then discuss how insights from single cell models translate to understanding of multicellular development. In our ongoing work, we are developing strategies for experimental falsification and iterative correction of multicellular models of angiogenesis. Recent versions of our cell-ECM interaction models focus on how our descriptions of focal adhesions, the mechanosensitive ‘feet’ of cells by which they hold on the extracellular matrix, must be improved to analyze mechanical cell-ECM interactions. Also we invest in computational improvements to advance towards more detailed multicellular models. Altogether, I will present the use of cell-based modeling in analyzing how local cell-microenvironment interactions coordinate cell behavior during multicellular patterning.

Chris Miles
Department of Mathematics

Assistant Professor
Department of Mathematics
University of California, Irvine

Title: Decoding spatial stochastic RNA dynamics from static imaging data with point process inference

Abstract: Advances in microscopy can now provide snapshot images of individual RNA molecules within a nucleus. Decoding the underlying spatiotemporal dynamics is important for understanding gene expression, but challenging due to the static, heterogeneous, and stochastic nature of the data. I will write down a stochastic reaction-diffusion model and show that observations of this process follow a spatial point (Cox) process constrained by a reaction-diffusion PDE. Inference on this data resembles a classical “inverse problem” but differs in the observations of individual particles rather than concentrations. We perform inference using variational Bayesian Monte Carlo with promising results. However, many open computational and modeling challenges remain in the development of scalable and extendable techniques for this inverse problem. This work is in collaboration with the Fangyuan Ding lab of Biomedical Engineering at UCI and Scott McKinley at Tulane.

Lissete de Pillis
Department of Mathematics
Harvey Mudd College, Claremont, CA

Title: Mathematical Modeling of Immune Activity in Human Disease

Abstract: We are interested in better understanding the response of the immune system to a variety of triggers. The immune response can be both helpful and harmful, depending on context. Gaining insight into the balance of the interacting components of the immune system is crucial in controlling disease. We will discuss some of the approaches we have taken to mathematically modeling immune dynamics in the context of cancer, type I diabetes, and viral infection (SARS-CoV2).

Padmini Rangamani
Professor and Jacobs Faculty Scholar
Department of Mechanical and Aerospace Engineering
University of California, San Diego

Title: Mechanochemical modeling of YAP/TAZ-circadian coupling

Abstract: YAP/TAZ nuclear-cytoplasmic shuttling is a fundamental readout of cellular mechanotransduction. Recently, this process has been studied using computational modeling to explore different mechanical and chemical conditions that regulate YAP/TAZ nuclear mechanotransduction. In this talk, I will briefly summarize our modeling efforts on understanding some of the issues of substrate dimensions and crosstalk between calcium signaling and YAP/TAZ nuclear translocation. I will then talk about our recent efforts in coupling YAP/TAZ signaling to circadian clocks in cells. Building on previous models of YAP/TAZ and MRTF mechanotransduction and delay models of the cell circadian clock, we posit that activation of TEAD or SRF in the nucleus leads to increased expression of circadian proteins, PER/CRY and BMAL1. This coupled model recapitulates findings from recent experiments. Specifically, our model predicts that inhibition of the cytoskeleton or reduced substrate stiffness lead to increases in the circadian oscillation period. Furthermore, our model predicts that mutations in YAP or lamin A disrupt circadian oscillations, resulting in decaying oscillations for a significant fraction of cells in a population.

Russell Rockne
Director, Division of Mathematical Oncology and Computational Systems Biology
Co-director, Biostatistics and Mathematical Oncology Core
City of Hope

Title: Applications of function regression to predictive modeling in oncology

Abstract: In the field of Mathematical Oncology, we are often faced with the challenge of building models that both provide insight and make accurate predictions of an individual patient's disease. Compounding this challenge is deciding what features to include in the model based on the available data, and having some degree of confidence we have built a 'good' model. Recent advances in the field of function regression provide a novel approach to these problems by identifying equations that both fit the data and provide parsimonious mechanistic models. This approach provides the best of both worlds from machine learning and mechanistic modeling by leveraging large amounts of data with knowledge of the biological system to yield interpretable and predictive models. In this talk I will discuss several examples of how we have used variations of the sparse identification of nonlinear dynamics (SINDy) algorithm to build and parameterize predictive models of cancer growth and response to therapy from clinical data at City of Hope, with a focus on brain cancer and CAR T-cell-based therapies.

Suzanne Sindi
Professor of Applied Mathematics
Chair of Applied Mathematics Department
University of California Merced

Title: Mathematical Modeling as a Tool for Biological Discovery: Stories from Blood Coagulation and Protein Aggregation

Abstract: Mathematical models are powerful tools that can aid in biological discovery. By serving as a testbed for hypothesis generation, mathematical modeling facilitates exploration beyond traditional experimental approaches. This talk explores the challenges and opportunities presented by mathematical modeling in enhancing our understanding of biological systems. Specifically, I will provide an overview of two systems in which mathematical modeling played a pivotal role in driving discovery: hemophilia A and prions.
Variability in bleeding frequency and severity among clinical categories of hemophilia A remains a perplexing challenge, with the underlying causes largely unknown. To investigate this problem, we utilized an established mechanistic mathematical model of blood coagulation, involving dozens of proteins and hundreds of reactions, to conduct our own "synthetic clinical trial." Synthetic patients were constructed by systematically varying factor and inhibitor levels within physiological ranges. The mathematical model's output served as surrogate measures for bleeding severity, enabling statistical analysis for hypothesis generation. Our synthetic clinical trial identified novel modifiers that could significantly enhance clotting behavior in hemophilia A, a discovery subsequently validated in further experimental assays.
Prions, responsible for a range of irreversible neurodegenerative diseases such as Creutzfeldt-Jakob disease in humans and "mad-cow" disease in cattle, are characterized by their ability to self-assemble into transmissible aggregates. While fatal in mammals, benign prion phenotypes exist in yeast, providing an ideal experimental platform for studying prion aggregate dynamics. However, most mathematical approaches focus solely on protein dynamics, often in isolation from living (and dividing) cells, and have failed to recapitulate in vivo properties of yeast prion strains. By developing a stochastic model of prion amplification and identifying a plausible mechanism explaining the stability of two prion variants, we have uncovered—and subsequently experimentally validated—a previously unanticipated structural difference between these variants.

Amber Smith
Associate Professor
University of Tennessee Health Science Center
Department of Pediatrics
Institute for the Study of Host-Pathogen Systems

Title: Modeling Immune Responses to Influenza Infections and Coinfections

Abstract: Influenza viruses infect millions of individuals each year, and complications can arise from bacterial invasion. While experimental methods can be used to identify mechanisms of single or multi-pathogen infections, mathematical models provide a unique lens to determine their contribution to susceptibility and pathogenicity and define hidden mechanisms. I’ll discuss an integrative model-experiment exchange that we used to disentangle pathogen-specific effects on various aspects of host immunity, dissemination within the lung, and disease severity during influenza infections and coinfections.