Breadcrumb

Home

mrb1_banner

The main long-term goal of the UC Riverside Interdisciplinary Center for Quantitative Modeling in Biology (ICQMB) is to develop comprehensive predictive quantitative models of complex biological systems. All projects at this center combine quantitative experiments, novel image analysis, modeling and statistical approaches, and build upon the mutually complementary strength of the researchers at UC Riverside with support from collaborators at other institutions.

Upcoming Talks

Tuesday, May 26th, 2020
11:00 a.m. - 12:20 p.m.

Online Zoom Seminar - Email Qixuan Wang to request the link.

Anastasios Matzavinos


Title: Bayesian uncertainty quantification for particle-based simulations

Dr. Anastasios Matzavinos, Division of Applied Mathematics, Brown University

Abstract: A number of problems of interest in applied mathematics and biology involve the quantification of uncertainty in computational and real-world models. A recent approach to Bayesian uncertainty quantification using transitional Markov chain Monte Carlo (TMCMC) is extremely parallelizable and has opened the door to a variety of applications which were previously too computationally intensive to be practical. In this talk, we first explore the machinery required to understand and implement Bayesian uncertainty quantification using TMCMC. We then describe dissipative particle dynamics, a computational particle simulation method which is suitable for modeling biological structures on the subcellular level, and develop an example simulation of a lipid membrane in fluid. Finally, we apply the algorithm to a basic model of uncertainty in our lipid simulation, effectively recovering a target set of parameters (along with distributions corresponding to the uncertainty) and demonstrating the practicality of Bayesian uncertainty quantification for complex particle simulations.

Bio: Anastasios Matzavinos is a faculty member of the Division of Applied Mathematics at Brown University. His research interests revolve around applied mathematics and computational biology with a special emphasis on data-driven modeling and Bayesian approaches to model selection. He is the recipient of several grants and awards, including an NSF CAREER award.

 

UCR Interdisciplinary Center for Quantitative Modeling in Biology Colloquium

 

Friday, May 15th, 2020
12:00 - 1:00 p.m.

Online Zoom Seminar - Email Qixuan Wang to request the link.


Title: Linking structure to biomechanical behaviors of plant primary cell walls

Daniel J. Cosgrove, Tian Zhang, Yao Zhang and Sulin Zhang, Department of Biology and Center for Lignocellulose Structure and Formation, Pennsylvania State University

Abstract: How the molecular organization of the plant primary cell wall accommodates its dual needs for mechanical strength and high extensibility (surface expansion) is a subject of long-term and widespread interest and debate, as it is one of the foundations for plant growth, morphogenesis and biomechanics. To explore how wall structure gives rise wall mechanical behaviors, we studied cell-free strips of onion epidermal walls by several complementary methods. This system avoids problems inherent with use of living tissues, where geometry and biological responses can complicate the interpretation of many experiments. Atomic force microscopy (AFM) revealed the organization of cellulose microfibrils in this cross-lamellate wall and showed complex patterns of microfibril movements with different types of strain. Several different mechanical assays were combined with enzyme digestions to assess which polysaccharides bear mechanical forces in-plane and out-of-plane of the cell wall. This approach provides critical tests of current hypotheses of wall structure, material properties, tissue mechanics, and mechanisms of cell growth. We conclude that these various mechanical properties are not tightly coupled with each other and thus reflect distinctive aspects of wall structure. The cross-lamellate network of cellulose microfibrils dominated the behavior of the wall in tensile creep and stiffness assays, whereas pectin influenced indentation mechanics. Enzymatic digestion of xyloglucan did not alter wall mechanics in any of the assays, contrary to the common view that xyloglucan tethers microfibrils together. This information is being used to construct a coarse-grained model of the cell wall for insights into nanoscale interactions and movements underlying cell wall mechanics.

For more information, see:

Zhang, T., Tang, H., Vavylonis, D. and Cosgrove, D.J. (2019) Disentangling loosening from softening: insights into primary cell wall structure. Plant Journal, 100, 1101-1117.
Cosgrove, D.J. (2018) Diffuse growth of plant cell walls. Plant Physiology, 176, 16-27.
Zhang, T., Vavylonis, D., Durachko, D.M. and Cosgrove, D.J. (2017) Nanoscale movements of cellulose microfibrils in primary cell walls. Nature Plants, 3, 17056.
Zhang, T., Zheng, Y. and Cosgrove, D.J. (2016) Spatial organization of cellulose microfibrils and matrix polysaccharides in primary plant cell walls as imaged by multichannel atomic force microscopy. Plant Journal, 85, 179-192.

 

 

 

Wednesday, May 6th, 2020
1:00 - 2:00 p.m.
Online Zoom Seminar -
Email Qixuan Wang to request the link.

Dr. Qi Chen, UCR


Title: On the Origin of Mammalian Early Embryo Symmetry-Breaking

Dr. Qi Chen, Division of Biomedical Sciences, School of Medicine, University of California, Riverside

Abstract: In mammalian preimplantation embryo development, when the first asymmetry emerges and how it develops to direct distinct cell fates are two longstanding questions. It remains debatable whether the first bifurcation of cell fate emerges randomly at morula stage, or has been predetermined at earlier stages before morphological distinction. Combining single-cell RNA-seq analysis and mathematical modeling, we recently showed that the very first symmetry-breaking process involves both chance separation and defined transcriptional circuits. From our single-embryo transcriptome analysis, small biases at molecular level will inevitably emerge at the 2-cell embryo stage, following a binomial distribution due to the cleavage division. At this stage, the blastomere-to-blastomere distribution seems random but during subsequent zygotic transcriptional activation, a “bistable pattern” emerges in some genes. Several lineage specifiers show a strong bias between different blastomeres thus providing potential for further increased asymmetry subsequently. These observations suggest a scenario of how order is created from a seemingly random process through the differential triggering of existing master regulators by the emergence of their small bias. Recently, we also proposed that compartmentalized intracellular reactions, such as those mediated by cell-cell contact and cell geometry, generate micro-scale inhomogeneity, which is amplified in the developing embryo, driving pattern formation. Ongoing research in my lab strive to address these hypotheses.

Bio: Current research in Qi Chen lab focus on two areas:

1) Epigenetic inheritance mediated by sperm RNAs and RNA modifications. Researches from Qi Chen lab and others revolutionized our idea about the hereditary information carried by sperm, by demonstrating that paternally acquired traits from environmental stressors can be “encoded” in the form of sperm RNAs and RNA modifications, which transmit paternal phenotypes to the offspring beyond DNA sequence.

2) Tracing the origin of heterogeneity and symmetry breaking in the early mammalian embryo. In mammalian preimplantation embryo development, when the first asymmetry emerges and how it develops to direct distinct cell fates are two longstanding questions. We developed a keen interest on the origin (molecular and cellular) of early mammalian embryo symmetry-breaking before blastocyst formation, for which we are now utilizing the power of single-cell technology and mathematical modeling to study with.

For more information check our lab website: http://qichen-lab.info/

 

Tuesday, May 5th, 2020
11:00 a.m. - 12:20 p.m.
Online Zoom Seminar -
Email Qixuan Wang to request the link.

Heiko Enderling


Title: Quantitative personalized oncology - calculus in clinical decision making

Dr. Heiko Enderling, Moffitt Cancer Center & Research Institute

Abstract: In quantitative personalized oncology we develop clinically motivated calibrated quantitative models that are informable with patient-specific data for personalized treatment recommendations. In close collaboration with experimentalists and clinicians, mathematical models that are parameterized with experimental and clinical data can help estimate patient-specific disease dynamics and treatment success. This positions us at the forefront of the advent of ‘virtual trials’ that predict personalized optimized treatment protocols. Here, we will discuss a couple of different models to demonstrate how to integrate calculus into clinical decision making. First, we show that a mathematical model can be calibrated from early treatment response dynamics in patients undergoing hormone therapy for prostate cancer. The learned model dynamics can then be used to forecast responses to subsequent treatment, and identify high risk patients who would benefit form concurrent therapies. In a second example, we will discuss pre-treatment and during treatment dynamics for patients undergoing radiotherapy for head and neck cancer. We show that routinely collected data can inform a mathematical model to accurately predict responses, thus allowing to adapt therapy when necessary.

Bio: I am a tenure-track Associate Member in the Integrated Mathematical Oncology Department, with a secondary appointment in Radiation Oncology at Moffitt Cancer Center & Research Institute. As a trained computer scientist and mathematician my research is focused on developing and applying quantitative modeling techniques to simulate tumor growth and predict treatment response. My interdisciplinary career began with an undergraduate degree in Computer  Science  applied  to human medicine at the University of Magdeburg, Germany, followed by graduate studies in Mathematical  Biology at the University of Dundee, Scotland. After a postdoctoral fellowship I advanced to the rank of Assistant Professor at the Center of Cancer Systems Biology at Tufts University School of Medicine, before moving to Moffitt Cancer Center in 2013.

Talk Slides

 

April 28th, 2020
11:00 a.m. - 12:20 p.m.
Online Zoom Seminar -
Email Qixuan Wang to request the link.

Sarah Olson, Worcester Polytechnic


Title: Modeling the Dynamics of Centrosome Movement

Dr. Sarah Olson, Department of Mathematical Sciences, Worcester Polytechnic Institute

Abstract: The mitotic spindle is a complex, dynamic machine important for cell division. The spindle is composed of a network of microtubules and motor proteins that generate forces to form a bipolar spindle, with each pole organized around a single centrosome. Disruption in force generating activities through protein depletions or alterations to centrosome number, alter spindle structure and affect the fate of the cell in mitosis. Centrosome amplification leads to multipolar spindle formation and multipolar division, which results in daughter cells with decreased viability. However, cancer cells actively cluster extra centrosomes to form a functional bipolar spindle. We highlight initial modeling and experimental efforts to understand the dominant forces that lead to the formation of either a multipolar or functional bipolar spindle in the case of extra centrosomes. Time permitting, we will also highlight other recent projects related to cancer cell dynamics and cumulative cellular absorption of drugs.

Bio: Dr. Sarah Olson is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute and affiliated with Biomedical Engineering and the Bioinformatics and Computational Biology Program. Olson received her undergraduate degrees in Mathematics and Biology from Providence College, a master's from the University of Rhode Island in Mathematics, and a PhD in Biomathematics from North Carolina State University. She completed her postdoc at Tulane University and has worked in the general areas of fluid dynamics, scientific computing, and mathematical biology.

 

April 21st, 2020
11:00 a.m. - 12:20 p.m.
Online Zoom Seminar -
Email Qixuan Wang to request the link.

Larry Li UCR2


Title: Pattern Transitions in Spatial Epidemiology: Implications for coping with the emerging epidemics (e.g. the ongoing COVID-19 pandemic)

Dr. Larry Li, Professor of Ecology, Department of Botany and Plant Sciences, UC Riverside

Abstract: The explanation and prediction of spatial patterns of infectious disease, particularly those of emerging pathogens like SARS-CoV-2, have remained a central problem of epidemiology since its inception, for example, theoretical models combined empirical data to study rabies and the Black Death. In this talk, I will start with the introduction of ecological spatiotemporal patterns and pattern formations via PDE and CA-SIRS models with von Neumann and Moore configurations; then I will focus on our previous and current works on the emergence and types of pattern transitions including patch invasions, the mechanisms driving pattern transitions (such as spatial heterogeneity, seasonality, noise, and human behavior), and the epidemiological role of pattern transitions. Such pattern transitions can be served as early warnings for the outbreak of diseases. Coherence resonance and cyclic evolution of host-pathogen system pattern transitions could also provide further understanding of the emerging epidemics such as the ongoing coronavirus pandemic.

Bio: Larry Li is Professor of Ecology with a broad interdisciplinary background and experience in mathematical, statistical and computational modeling applications in biological, ecological and environmental studies. He published more than 250 refereed journal articles, 30 book chapters and proceedings papers, and 11 books or edited special issues. Among his many honors and awards, he was elected to be Honorary Member of the Scientific Council of Russian Academy of Sciences (2005), IHE Fellow (1988), AAAS Fellow (2006), DeTao Master of Ecology (2013), and received 2015 Prigogine Gold Medal (only one leading scientist per year to be awarded in ecological systems internationally). He is also Founding Editor-in-Chief of two international journals: Ecological Complexity (Elsevier; 2004) and Journal of Arid Land (Springer Nature; 2009).

 

April 7th, 2020
Online Zoom Seminar - Email Qixuan Wang to request the link

Title: Combining Data, Control Theory, Statistical Thermodynamics with Machine Learning to Predict Enzyme Regulation, Metabolite Concentrations and Rate Constants

William R. Cannon1,2, Samuel Britton2, Mark Alber2

1 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352; 2 Department of Mathematics, University of California, Riverside, Riverside, CA 92521

Abstract: Experimental measurement or computational inference/prediction of the enzyme regulation needed in a metabolic pathway is hard problem. Consequently, regulation is known only for well-studied reactions of central metabolism in a few organisms. In this study, we use statistical thermodynamics and metabolic control theory as a theoretical framework to determine the enzyme activities that are needed to control metabolite concentrations such that they are consistent with experimentally measured values. A reinforcement learning approach is utilized to learn optimal regulation policies that match physiological levels of metabolites while maximizing the entropy production rate and minimizing the heat loss. The learning takes a minimal amount of time, and efficient regulation schemes were learned that agree surprisingly well with known regulation. The learning is facilitated by a new approach in which steady state solutions are obtained by convex optimization rather than ODE solvers, making the time to solution seconds rather than days. The optimization is based on the Marcelin-De Donder formulation of mass action kinetics, from which rate constants are inferred. Consequently, a full ODE-based, mass action simulation with rate parameters and post-translational regulation is obtained. We demonstrate the process on three pathways in the central metabolism E. coli (gluconeogenesis, glycolysis-TCA, Pentose Phosphate-TCA) that each require different regulation schemes.

Bio: Dr. Cannon is a senior scientist at the Pacific Northwest National Laboratory in Richland, WA. He led the Computational Biology Group at PNNL before recently taking an adjunct position at UC Riverside. His training is in statistical thermodynamics, biochemistry, biophysics and enzymology.

Research Areas: Computational physics, biochemistry and proteomics; Modeling and simulation including deterministic and stochastic simulation of metabolism; stochastic methods, microbial metabolism; statistics, statistical mechanics and statistical proteomics data analysis.

 

Center News

Dr. Russell Rockne Appointed Adjunct Assistant Professor in the Department of Mathematics, UC Riverside

Russell Rockne


Dr. Russell Rockne from the City of Hope was appointed Adjunct Assistant Professor in the Department of Mathematics, UC Riverside: https://www.cityofhope.org/people/rockne-russell He is an Assistant Professor in the Department of Computational and Quantitative Medicine within Beckman Research Institute of City of Hope. He also serves as director of the Division of Mathematical Oncology, with the goal of translating mathematics, physics and evolution-based research to clinical care. The division, a part of Irell & Manella Graduate School of Biological Sciences and the Beckman Research Institute of the City of Hope, uniquely combines clinical care, scientific  research and mathematical expertise to enhance the overall understanding of cancer development, growth, evolution and reaction to treatment. This work ultimately helps the care team to predict, control and thwart malignancy on both a global scale by improving evidence-based standards of care and an individual level by personalizing treatment using quantifiable patient and disease factors.

 

The Center is Co-Sponsoring Three Conferences through 2021

1. 9th Annual SoCal System Biology Conference to be held at UCR on February 1, 2020: https://icqmb.ucr.edu/9th-annual-southern-california-regional-systems-biology-conference

2. Dimitrios Morikis Memorial Symposium to be held at UCR on February 10, 2020: https://morikis-symposium.engr.ucr.edu/

3. 2021 Annual Society for Mathematical Biology Meeting to be held at UCR from June 13-17, 2021. This year it will be held in Germany. https://www.smb.org/meetings/

 

DOE Supported Interdisciplinary Collaborative Project at UCR, PNNL and ORNL

Dr. Alber is a co-PI on just awarded three year (9/1/2019-8/31/2022) Department of Energy grant titled: "Elucidating Principles of Bacterial-Fungal Interactions" with total budget of $2,246,000 and with $556,229 for the subaward to UCR.

Interdisciplinary Team includes:

PI: William Cannon, Senior Scientist at the Pacific Northwest National Laboratory and Adjunct Professor, Department of Mathematics, UC Riverside

Co-PI: Mark Alber, Distinguished Professor, Department of Mathematics, UC Riverside

Co-PI: Dale A Pelletier, Senior Staff Scientist in the Biosciences Division at Oak Ridge National Laboratory (ORNL)

Co-PI: Jessy Labbé, Staff Researcher, lead of the Fungal Systems Genetics and Biology Lab in the Biosciences Division at Oak Ridge National Laboratory (ORNL)

Title: Elucidating Principles of Bacterial-Fungal Interactions

Abstract: In comparison to bacterial-bacterial interactions, there is very little known about bacterial-fungal interactions even though these interactions are thought to be fundamentally important to DOE missions in sustainability, crop biofuel development and biosystem design. In biofuel crops, many crop root systems live in mutualistic symbiosis with fungi and bacteria. Mycorrhiza helper bacteria (MHB) increase host root colonization by mycorrhizal fungi, which in turn act as a micro-root system to provide the plant with soil nutrients. Recent work on the Populus root microbiome has determined that the interactions between the mycorrhizal fungus Laccaria bicolor and the bacterium, Pseudomonas fluorescens are key to the fitness of the plant. These organisms, Laccaria and P. fluorescens, are the focus of this project to use combined mathematical and computational modeling and experiments to understand fundamental principles of interactions between fungi and bacteria from the perspective of material exchange and energetics, and how material and energetics are linked in inter- and intra-microbial subsystems.

 

Integrating Machine Learning and Multiscale Modeling Position Paper

Integrating Machine Learning and Multiscale Modeling https://arxiv.org/abs/1910.01258 to be discussed at the Conference to be held at NIH, Bethesda, MD, from October 24-25:

https://www.imagwiki.nibib.nih.gov/msm-consortium/2019-ml-msm

https://www.imagwiki.nibib.nih.gov/msm-consortium/agenda

 

AMS at UC Riverside - 2019

We invite you to participate in the American Mathematical Society (AMS) Fall Western Sectional Meeting to be held at the University of California, Riverside, from November 9-10, 2019 (Saturday - Sunday):

http://www.ams.org/meetings/sectional/2266_timetable.html

In particular, the Meeting will include three Special Sessions on applications to biology as well as the Special Session of the Association for Women in Mathematics (AWM).

 

The 2nd Annual Conference on Quantitative Approaches in Biology will be held October 4-5, 2019 at Northwestern University

This conference is a two-day event that includes a range of activities to stimulate the cross-fertilization of ideas, including guest speaker talks, lightning talks, poster sessions, an undergraduate research competition, a reception, and plenty of networking opportunities.

SPEAKERS Day 1 – October 4, 2019

Hana El-Samad, University of California-San Francisco

Daniel Fisher, Stanford University

Christine Heitsch, Georgia Tech

Madhav Mani, Northwestern University

Andy Oates, L’Ecole Polytechnique Fédérale de Lausanne

Day 2 – October 5, 2019

Nicole Creanza, Vanderbilt University

James Lee, University of Illinois-Chicago

Srividya Iyer-Biswas, Purdue University

Xin Li & Dave Shihai Zhao, University of Illinois-Urbana Champaign

Christian Petersen, Northwestern University

Jeremiah Zartman, University of Notre Dame

Register Here

 

Recent Special Issue of the the Bulletin of Mathematical Biology Includes Papers by Participants of the Conference Held at UC Riverside in 2017

Special Issue on Multiscale Modelling of Tissue Growth and Shape of the Bulletin of Mathematical Biology, Volume 81, Issue 8, August 2019: 3214-3218, doi: 10.1007/s11538-019-00649-2 https://link.springer.com/journal/11538/81/8/page/1 Issue Editors: Mark Alber, Christophe Godin, Philip Maini, Roeland Merks, Eric Mjolsness Introduction: https://link.springer.com/article/10.1007/s11538-019-00649-2

This Special Issue consists of contributions from participants of three workshops with similar focus held in 2016–17: “Modelling of Tissue Growth and Form” held from March 6 to March 10, 2017, at the NSF Mathematical Biology Institute (MBI), Columbus, OH, USA, “Multi-scale Modeling of Complex Systems in Developmental and Plant Biology” held on December 15, 2017, at the Interdisciplinary Center for Quantitative Modeling in Biology, University of California, Riverside, CA, USA, “Computing a Tissue: Modeling Multicellular Systems” at the 15th European Conference on Computational Biology held from September 3 to September 7, 2016, at The Hague, Netherlands. The Special Issue combines papers on recent advances in the field with review articles discussing in detail some open problems. Contributors were asked to describe the recent results on the application of the very latest mathematical and computational modeling and experimental approaches used for studying problems in morphogenesis and growth of plants and animals.

 

Dr. William Cannon Appointed Adjunct Professor in the Department of Mathematics, UC Riverside

William Cannon

Dr. William Cannon from the Pacific Northwest National Laboratory (PNNL) was appointed Adjunct Professor in the Department of Mathematics, UC Riverside: https://www.pnnl.gov/science/staff/staff_info.asp?staff_num=7055

Dr. Cannon's Research Interests include: Computational biophysics, biochemistry and proteomics; Modeling and simulation including deterministic and stochastic simulation of metabolism; simulations of state; microbial metabolism; statistics, statistical mechanics and statistical proteomics data analysis; Cloud computing and high performance.

Dr. Cannon is author of more than 50 technical publications in modeling and simulation, data analysis and proteomics. His graduate work was in statistical thermodynamics in the laboratory of J. Andrew McCammon studying molecular recognition proteins using molecular dynamics and Monte Carlo methods. His graduate work was in the laboratory of Steven J. Benkovic where he worked in both experimental and computational enzymology. Before joining PNNL, Dr. Cannon spent time at Monsanto working on high-throughput transcriptome data analysis and network inference. Since joining PNNL, Dr. Cannon has worked on statistical methods for integrating proteomics data with models, the use of supercomputers to maximize the identification of peptides and proteins from high throughput mass spectrometry assays, and the modeling and simulation of metabolic pathways. Researchgate: www.researchgate.net/profile/William_Cannon2

 

Samuel Britton of UCR Math D.O.E. Office of Science Graduate Student Fellowship

Samuel Britton

Graduate student Samuel Britton in Mathematics Department has been awarded the U.S. Department of Energy (DOE) Office of Science Graduate Student (SCGSR ) Fellowship to conduct research on the collaborative project at the Pacific Northwest National Laboratory (PNNL) titled “Data integration and multi-scale computational model of metabolism”. He will be co-advised on this project by Dr. William Cannon (PNNL) and Dr. Mark Alber (UCR).

The SCGSR Award Notification indicates that: "The selection of Samuel Britton for the SCGSR award is in recognition of outstanding academic accomplishments and the merit of the SCGSR research proposal, and reflects Samuel Britton’s potential to advance the Ph.D. studies and make important contributions to the mission of the DOE Office of Science".