Gulf Coast Campus
- Marek Kimmel (Contact, Rice University, Houston, TX, USA)
- Rosemary Braun (Northwestern University)
- John Dobelman (Rice University, Houston, TX, USA)
- Herbert Levine (Rice University, Houston, TX, USA)
- Tomasz Lipniacki (Polish Academy of Science)
- Brian Munsky (Colorado State University, Fort Collins, CO, USA)
- Jennie Harvey - Program Manager (Contact, Los Alamos National Laboratory, NM, USA)
- Bill Hlavacek - Scientific Organizer (Los Alamos National Laboratory, NM, USA)
- Mara Steinkamp - Scientific Organizer (University of New Mexico School of Medicine, NM, USA)
Lecturers teaching this course include:
- John Albeck (University of California, Davis)
- Marek Kochanczyk (POLISH Academy of Science)
- Tomasz Lipniacki (POLISH Academy of Science)
- Brian Munsky (Colorado State University)
- Steve Presse (Arizona State University)
- Michael Savageau (UC Davis)
- A Novel Strategy to Accelerate the Modeling and Analysis of Complex Biological Systems, abstract
- Doug Shepherd (University of Colorado - Denver)
Lecturers teaching this course include:
- Rosemary Braun (Northwestern)
- Lively Networks and multi-scale approaches for analyzing cancer omics data, abstract
- Alexandra Jilkine (Notre Dame University)
- Control of Cell Fraction and Population Recovery during Tissue Regeneration in Stem Cell Lineages, abstract
- Marek Kimmel (Rice University)
- Katherine King (Baylor College of Medicine)
- The Biology of Hematopoietic Stem and Progenitor Cells and the Process of Primitive Hematopoiesis, abstract
- Herbert Levine (Rice University, Houston, TX, USA)
- Phenotypic plasticity and Tumor progression, abstract
- Thomas "Ollie" McDonald (Harvard, Center for Cancer Evolution)
- Tools for modeling optimal treatment schedules to delay cancer progression, abstract
- Sharon Plon (Baylor College of Medicine)
- The evolving landscape of genomic susceptibility to cancer, abstract
- Simon Tavaré (Cambridge/Columbia)
- Jason Xu (University of California, Los Angeles)
- Stochastic Processes for Cell Systems and Cancer Dynamics, abstract
- Curtis R. Pickering (The University of Texas MD Anderson Cancer Center)
- Tutorial: Cancer Basics, abstract
Lecturers teaching this course include:
- James R. Faeder (University of Pittsburgh School of Medicine)
- William S. Hlavacek (Los Alamos National Laboratory)
- Yen Ting Lin (Los Alamos National Laboratory)
About Rosemary Braun
Rosemary Braun is a computational biologist with an interest in the development of methods for integrative, systems-level analysis of high-dimensional ("big") *omic data. These methods incorporate bioinformatic information with experimental data to characterize the networks of interactions that lead to the emergence of complex phenotypes, particularly cancers. Dr. Braun is an Assistant Professor of Biostatistics (Feinberg School of Medicine) and Engineering Sciences & Applied Mathematics at Northwestern University.
Title: Lively Networks
Many systems -- including living cells -- exhibit collective behaviors that emerge from complex networks of many interacting processes. What can the "wiring diagram" of those interactions tell us about the dynamics of the system, and can we deduce the underlying network from the collective dynamics? In this talk, I will discuss what we can learn about the dynamics of interacting systems from the topology of the underlying network of interactions. I will introduce the formalisms of spectral graph theory and network filtration, and illustrate how these approaches can help us model how living systems respond and adapt to perturbations.
Title: Seeing the forest and the trees: multi-scale approaches for analyzing cancer omics data.
Advances in high-throughput "*omic" assays now make it possible to the molecular state of a sample in genome-wide detail, providing unprecedented opportunity to investigate disease mechanisms by simultaneously profiling thousands molecular markers per sample. To date, however, most analyses of *omic data consider each marker independently and treat regulatory pathways as a "sum of their parts." By neglecting the network of interactions, such approaches can miss crucial multi--gene effects associated with disease. This talk will present some recent techniques to incorporate pathway information into the analysis of high--dimensional *omic data. By analyzing data at the systems level, the methods enable us to integrate disparate types of *omic data, make inferences about disease mechanisms, and distinguish sets of cumulatively deleterious alterations from those that compensate one-another to preserve the overall function of a pathway. We will show how these analyses can overcome the high variability of *omics data to yield results that are more reproducible across studies, and demonstrate how these methods can be used to identify novel therapeutic and diagnostic targets.
About John Dobelman
John A. Dobelman, Ph.D., is a Professor in the Practice in Statistics and Director of the statistics Professional Master's Program at Rice University. He has a strong background in engineering and leadership. Prior to joining the faculty at Rice University, he was in the Science and Research (S&R) department at PROS Revenue Management as a pricing scientist. Before PROS he owned and operated a financial engineering laboratory. He has been an Adjunct Professor at The University of St. Thomas, Cameron School of Business, Research Analyst at Rice, and consultant. Prior to his statistics career, he was lead engineer and manager for engineering, program management and implementation engineering/installation for terminal Air Traffic Control communications, surveillance, and navigation and landing systems for the Federal Aviation Administration's Facilities & Equipment program. John earned his Ph.D. in Statistics from Rice University. He completed his MBPM from the Jones Graduate School of Management in public management, entrepreneurship and international business. He did his undergraduate studies in Electrical Engineering at Rice University.
About Marek Kimmel
Marek Kimmel is a Professor of Statistics and Bioengineering at Rice University, in Houston, TX, USA, where he is one of the founding Members of the Steering Committee of the Program in Systems Biology. His main focus is mathematical modeling in biology, mainly in cancer research, genetics, and evolution, including stochastic and deterministic models. His monograph, with David Axelrod, Branching Processes in Biology (in second edition) is a popular reference. He has been collaborating with biologists and physicians. He is a Fellow of the American Statistical Association, cited for his works in estimating progression and early detection of lung cancer. He authored several monographs, around 250 refereed papers, and received research funding from NIH, NSF, EPSRC (UK), NATO, NCN (Poland), and ERC. He supervised around 30 PhD theses in the USA and Poland. He established the Cancer Dynamics track at the annual q-bio Summer Schools. His current focus is application of mathematical genetics and branching process methods to understand and predict cancer progression and interactions between cancer and treatment.
About Tomasz Lipniacki
Dr. Tomasz Lipniacki is a professor and head of the department in the Institute of Fundamental Technological Research, Polish Academy of Sciences. He graduated from Department of Physics, Warsaw University. Dr. Lipniacki works on cellular regulatory pathways governing responses to stress. His lab combines stochastic modeling with single cell experiments aiming to elucidate cellular decision making in innate immune signaling. He analyzes how feedbacks and other nonlinear regulatory elements in noisy environments turn information into decision.
Lecture 1: Stochastic modeling of innate immune responses
I will start from deterministic and stochastic modeling of NF-κB pathway and proceed to our recent study in which we analyze cell fate decisions in intertwined NF-κB/IRF3 and STAT signaling pathways.
In first part I will show how noise and feedbacks lead to nearly all-or-nothing responses to small TNF doses that activate single or few receptors TNF receptors. Responses to such weak signals are enabled by the kinase cascade that works as nonlinear signal amplifier.
In the second part I will discuss cell fate decisions that arise in the crosstalk of three transcription factors and are coordinated by paracrine IFNβ signaling at the cell population level. In this example initial heterogeneities in expression of virus sensing proteins lead to formation of distinct subpopulations of cells.
%((%1%))% Tay et al. Single-cell NF-κB dynamics reveal digital activation and analogue information processing, Nature 466:267-271 (2010)
%((%2%))% Czerkies et al. Cell fate in antiviral response arises in the crosstalk of IRF, NF-κB and JAK/STAT pathways, Nature Communications 9:493 (2018)
Lecture 2: Cellular information processing for time-varying stimulation
Two important regulatory pathways of NF-κB and MAPK were found to transmit merely one bit of information about the level of constant stimulation with, respectively, TNF and EGF %((%1,2,3%))%. This somewhat surprising result may suggest that these pathways evolved to process analog inputs into physiologically interpretable binary outputs. Although being capable of transmitting a single bit about the stimulation dose, both NF-κB and MAPK pathways can respond in a pulsatory manner to the repeated pulses of the stimuli.
Based on computational model %((%4%))% we estimated MAPK channel information capacity (or simply bitrate), defined as maximal mutual information that can be transmitted over a sufficiently long time t, dived by t %((%5%))%. The MAPK pathway is capable of responding (by activation of kinase ERK) to short EGF pulses of period not shorter than T=60 minutes. As the response is nearly binary, one could expect that the information transmission rate is equal to the classical bandwidth, 1/T. We found however, that the upper bound on information transmission rate is substantially higher and can be achieved for sequences of EGF pulses with carrying frequency higher than 1/T. I will discuss this high-frequency coding/decoding for binary input sequences.
%((%1%))% R. Cheong, A. Rhee, C.J. Wang, I. Nemenman, A. Levchenko. (2011) Information transduction capacity of noisy biochemical signaling networks. Science 334, 354-358.
%((%2%))% J. Selimkhanov, B. Taylor, J. Yao, A. Pilko, J. Albeck, A. Hoffmann, L. Tsimring, R. Wollman. (2014) Accurate information transmission through dynamic biochemical signaling networks. Science 346, 1370-1373.
%((%3%))% K. Tudelska, J. Markiewicz, M. Kochańczyk, M. Czerkies, W. Prus, Z. Korwek, A. Abdi, S. Błoński, B. Kaźmierczak, T. Lipniacki (2017) Information processing in the NF-κB pathway. Sci. Rep. 7, 15926.
%((%4%))% M. Kochańczyk, P. Kocieniewski, E. Kozłowska, J. Jaruszewicz-Błońska, B. Sparta, M. Pargett, J.G. Albeck, W.S. Hlavacek, T. Lipniacki. (2017) Relaxation oscillations and hierarchy of feedbacks in MAPK signaling. Sci. Rep. 7, 38244.
%((%5%))% C.E. Shannon (1948) A mathematical theory of communication. Bell Syst. Tech. J. 27, 623-656.
Nonlinear regulatory elements in signaling pathways
In the first part: In will discuss characteristic nonlinear regulatory elements that shape dynamic of regulatory pathways: stoichiometric saturation, negative and positive feedbacks, feedforwards, kinase cascades. We will analyze temporal dynamics associated with these elements, and learn how this dynamics can be predicted based on bifurcation diagrams. Next we consider three dynamically distinct combinations of positive and negative feedbacks.
In the second part we will look how these elements regulate behavior of regulatory pathways in the presence of noise, and in the case when the spatial aspects of the signaling are important. If time permits we will consider bistable, spatial stochastic systems.
About Brian Munsky
Dr. Munsky joined the Department of Chemical and Biological Engineering and the School of Biomedical Engineering as an assistant professor in January of 2014. He received B.S. and M.S. degrees in Aerospace Engineering from the Pennsylvania State University in 2000 and 2002, respectively, and his Ph.D. in Mechanical Engineering from the University of California at Santa Barbara in 2008. Following his graduate studies, Dr. Munsky worked at the Los Alamos National Laboratory — as a Director’s Postdoctoral Fellow (2008-2010), as a Richard P. Feynman Distinguished Postdoctoral Fellow in Theory and Computing (2010-2013), and as a Staff Scientist (2013). Dr. Munsky is best known for his discovery of Finite State Projection algorithm, which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky’s research interests at CSU are in the integration of stochastic models with single-cell experiments to identify predictive models of gene regulatory systems. He was the recipient of the 2008 UCSB Department of Mechanical Engineering best Ph.D. Dissertation award, the 2010 Leon Heller Postdoctoral Publication Prize and the 2012 LANL Postdoc Distinguished Performance Award for his work in this topic. Dr. Munsky is the contact organizer of the internationally recognized, NIH-funded q-bio summer school, where he runs single-cell stochastic gene regulation (q-bio.org). Dr. Munsky is very excited about the future of quantitative biology, and he would love to talk about this with you!
About John Albeck
John Albeck, PhD, is an assistant professor of Molecular and Cellular Biology at the University of California, Davis. He received a B.A. in Biological Sciences from Cornell University in 2000, completed his doctoral work in Computational and Systems Biology under Peter Sorger at MIT in 2007, and was a postdoc and Instructor in Cell Biology from 2007 to 2013 with Joan Brugge at Harvard Medical School.
His work is focused on identifying systems-level regulatory properties that govern the behavior of cells during development and in cancers. Since 2013, his research group at UC Davis has brought together biologists and engineers to study how multiple pathways are integrated to control cellular metabolism, proliferation, and death. Their approach is centered on time-lapse image analysis and computational modeling of biochemical signaling activities in individual cells.
Lecture: Regulation of gene expression by time-varying signaling inputs
- Overview of signaling systems in which the temporal characteristics of the initial response pathway determine which target genes are expressed. We will discuss the MAPK, p53, and NF-kB pathways, along with other examples.
- Conceptual models of networks that perform temporal signaling filtering at the gene expression level. Basic models of gene expression and how negative and positive feedback affect expression output.
- Modern experimental approaches to measuring time-dependent gene expression patterns. Highlights of recent studies combining live-cell microscopy and molecular techniques such as CRISPR or single-mRNA tagging to quantify gene expression over time in individual cells.
- Physiological roles for time-filtering gene expression systems. How timedependent expression controls differentiation and diversification of cell state; implications for plasticity in tumor cells.
Breakout Session: Integration of signaling systems and metabolic control in the proliferation of single cells
I will discuss current research on the connections between the Ras/MAPK, PI3K/Akt, mTOR, and AMPK signaling systems and how these control protein translation, glycolysis, and oxidative phosphorylation. I will focus first on the biological side of this topic and then discuss existing models, along with open questions that have yet to be investigated with a quantitative approach.
About Alexandra Jilkine
Alexandra Jilkine is an Assistant Professor in the Department of Applied and Copmutational Mathematics and Statistics at the University of Notre Dame. Prof. Jilkine is a mathematical biologist, and her research interests include: mathematical modelling of cell lineages and optimal division patterns for delaying cancer, modeling feedbacks governing stem cell renewal and differentiation, and spatiotemporal behaviour of biochemical circuits.
Control of Cell Fraction and Population Recovery during Tissue Regeneration in Stem Cell Lineages
Multicellular tissues are continually turning over, and homeostasis is maintained through regulated proliferation and differentiation of stem cells and progenitors. Following tissue injury, a dramatic increase in cell proliferation is commonly observed, resulting in rapid restoration of tissue size. This regulation is thought to occur via multiple feedback loops acting on cell self-renewal or differentiation. Prior modeling studies of the cell lineage system have suggested that loss of homeostasis and initiation of tumorigenesis can be contributed to the loss of control of these processes, and the rate of symmetric versus asymmetric division of the stem cells may also be altered.
Here, we compare three variants of hierarchical stem cell lineage tissue models with different combinations of negative feedbacks and use sensitivity analysis to examine what are the possible strategies for the cells to achieve certain performance objectives. Our results suggest that multiple negative feedback loops must be present in the stem cell lineage the fractions of stem cells to differentiated cells in the total population as robust as possible to variations in cell division parameters, and to minimize the time for tissue recovery in a non-oscillatory manner. When one of these negative feedback loops on stem cell division been knocked out, most of the stem cell lineage population will be in the form of stem cells, suggestive of "precancerous" tissue. Furthermore, modeling suggests that positive feedback loops in stem cell homeostasis may also be required. We contrast and compare the differences between deterministic and stochastic versions of the models.
About Katherine King
Katherine Y. King MD PhD is an Assistant Professor of Pediatric Infectious Diseases at Baylor College of Medicine, where she is also part of the faculty for the Stem Cells and Regenerative Medicine Center and the Center for Cell and Gene Therapy. She received her MD and PhD degrees from Washington University in St. Louis in 2003 before completing her residency and fellowship training at Baylor College of Medicine where she has been on faculty since 2012. Dr. King has been the recipient of the March of Dimes Basil O’Connor Starter Scholar Award and the Aplastic Anemia and MDS International Foundation Liviya Anderson Award. In her mission to alleviate deaths from infectious diseases, her current research focuses on the molecular mechanisms by which inflammation damages blood and immune cell production by hematopoietic stem cells in the bone marrow. When she is not seeing patients at Texas Children’s Hospital or conducting research in the lab, Dr. King enjoys running, yoga, and volunteering her time for health care advocacy through the group Doctors for Change.
The biology of hematopoietic stem and progenitor cells and the process of primitive hematopoiesis
We will discuss the process of primitive hematopoiesis, including the biology of hematopoietic stem and progenitor cells (HSPCs). We will review experimental methods to define HSPCs and their properties including quiescence, proliferation and differentiation rates. We will explore the current state of knowledge regarding how environmental stresses such as infection and inflammation perturb these properties. We will describe lineage tracing and bar-coding methodologies to study such perturbations. Finally, we will discuss how changes in HSPC populations contribute to clonal hematopoiesis and the potential contributions of infection and inflammation to leukemic transformation. This will include a basic discussion of leukemia, leukemic stem cells, and genetic basis of cancer.
About Thomas \"Ollie\" McDonald
Thomas "Ollie" McDonald, is the Associate Director of the Center for Cancer Evolution in the Department of Biostastistics and Computational Biology at Dana-Farber Cancer Institute and a Research Associate at T.H. Chan Harvard School of Public Health (cce.dfci.harvard.edu). Ollie obtained his PhD in Statistics at Rice University in 2015 under the direction of Professor Marek Kimmel. He spent time in Franziska Michor’s lab at T.H. Chan Harvard School of Public Health before assuming his new position in the CCE. His main research interest is mathematical modeling of tumor evolution and heterogeneity in cancers with particular emphasis on branching process models. His current work includes creating an optimal dose scheduling software package for administration of targeted drugs.
Tools for modeling optimal treatment schedules to delay cancer progression
Acquired drug resistance is an obstacle for the treatment of cancer. Throughout a tumor’s life, the possibility of genetic or epigenetic alterations may lead to a subclone resistant to the therapy provided. In some cases, additional therapies are possible to continue treatment. However, it’s been shown through modeling that resistance and tumor progression can be delayed by altering the schedule making use of pharmacokinetic parameters and tumor growth data to create an optimal dosing schedule where the drug concentrations may be under the maximum tolerated dose which is typically administered. We will explore the background tools used to create optimal dosing schedules which typically rely on time-inhomogeneous branching process models of growth and methods for elucidating data that can inform these models. An overview of topics will include the following for single drug and combination models:
- Quick review of simulation of branching process models
- Calculating the Probability of Resistance and Expected Number of Resistant Cells
- Incorporating pharmacokinetic parameters into time-dependent branching process models
- Choosing optimal schedules
About Sharon Plon
Dr. Plon is a board-certified medical geneticist and laboratory scientist with a longstanding focus on the field of cancer genetics and clinical genomics. Her translational research has focused on analysis of patients with inherited susceptibility to childhood cancer. Dr. Plon co-chaired the international IARC committee which made recommendations for appropriate classification and clinical reporting of genetic variants in cancer susceptibility genes. Dr. Plon is a principal investigator one of the NHGRI/NCI clinical sequencing evidence generation research (CSER2) project at BCM. In 2013, Dr. Plon was name co-Principal investigator of an NHGRI U01 grant to support the development of a comprehensive Clinical Genomics (ClinGen) Resource. She also co-chairs the germline reporting committee of the Children’s Oncology Group/NCI Pediatric MATCH national precision oncology trial which opened in August 2017. Dr. Plon serves on the National Advisory Council for Human Genome Research of the NIH and is a member of the Board of Directors of the American Society of Human Genetics.
The evolving landscape of genomic susceptibility to cancer
For many years the process of testing for genetic susceptibility to cancer was based on testing a limited number of patients for a limited number of genes. The limitations on patients was based on testing only those patients with a high likelihood of having genetic susceptibility to cancer (extensive family history, etc). The limitation on genes was imposed due to the cost of genetic testing and the uncertainty in evaluating genetic variants when found due to the lack of large population databases for comparisons. Over the last five years the cost of sequencing has plummeted and much larger databases of non-cancer patients who have undergone whole genome or whole exome sequencing (of all genes) are available. With these advances we are transitioning to doing comprehensive understanding of all cancer patients. These early studies have revealed surprising results including that approximately 10% of unselect adult or pediatric patients carry deleterious mutations in cancer susceptibility genes. We have also learned that our prior attempts to select which patients carry these mutations failed to identify about half of the patients with hereditary mutations. Thus, there is a significant need to adjust our approach and the way that we evaluate patients and the genomic data. We will discuss a number of these recent studies, some of the surprising results, e.g. adult breast cancer mutations in children with a variety of cancers and discuss the challenges interpreting many of these results.
About Steve Presse
Steve went to McGill as an undergrad in Chemistry (2000-2003). He did his graduate work under the guidance of Bob Silbey at MIT in the area of Chemical Physics (2003-2008). He later turned to Biophysics for his postdoc with Ken Dill at UCSF (2008-2013). Steve’s research as Assistant Professor of Physics at IUPUI focused on statistical mechanical models of biological systems in addition to questions of inference and data analysis (2013-2016). Most recently, as Associate Professor of Physics and Chemistry at ASU (2017-), Steve is working in methods of nonparametric Bayesian analysis and has continued fluorescence experiments begun at IUPUI aimed at understanding bacterial predator-prey dynamics.
Data Analysis for Biophysics: from writing down models to learning models from the data
Data analysis courses that go beyond teaching elementary topics such as fitting residuals are rarely offered to students in the physical sciences. Thus, dataanalysis, much like programming, is something often learned and improvised “on the job”. Yet, with an explosion of experimental methods generating large quantities of data, the community would benefit from a clear presentation of methods of data analysis many of which are straightforward to implement and would raise our community standard for how data is currently being treated. It is often not realistic to expect graduate or undergraduate students alike to take a course covering topics of statistics relevant to their discipline in the physical sciences without taking multiple prerequisites needed to follow the material presented in such courses. My goal here is therefore to provide an introduction to exciting new developments in data science, machine learning and statistics in a language accessible to physical scientists willing to learn the necessary programming and mathematics. In particular, we will discuss tools of maximum likelihood, Bayesian inference, computational statistics and Bayesian nonparametrics.
Novel Statistical Tools for Single Molecule Biophysics: A foray into Bayesian nonparametrics
One route to modeling biophysical dynamics involves the bottom-up, molecular simulation, approach. In this approach, approximate classical potentials are used to simulate short time local motions in order draw insight on dynamics at longer time and larger length scales. Here we take a different route. Instead we present a top-bottom approach to building models of single molecule conformational dynamics and diffusion. The approach we present exploits a novel branch of Statistics – called Bayesian nonparametrics (BNPs) – first proposed in 1973 and now widely used in data science as the important conceptual advances of BNPs have become computational feasible in the last decade. BNPs are new to the physical sciences. They use flexible (nonparametric) model structures to efficiently learn models from complex data sets. Here we will show how BNPs can be adapted to address important questions in biophysics directly from the data which is often limited by factors such as finite photon budgets as well as other fluorophore artifacts in addition to data collection artifacts (e.g. aliasing, drift). More specifically, we will show that BNPs hold promise by allowing complex spectroscopic time traces (e.g. smFRET, photon arrivals) or images (e.g. single particle tracking) to be analyzed and turned into principled models of single molecule motion – from diffusion to conformational dynamics and beyond.
About Michael Savageau
Michael Savageau is a Distinguished Professor in the Departments of Microbiology & Molecular Genetics and Biomedical Engineering at The University of California Davis. He earned his Ph.D. from Stanford University (Ph.D.), and was a postdoctoral fellow at both UCLA and Stanford University prior to joining the faculty at The University of Michigan. Dr. Savageau initiated Michigan’s interdisciplinary training program in Cellular Biotechnology and its interdisciplinary Bioinformatics Program. He also chaired the Department of Microbiology & Immunology from 1992-2002 and was named the Nicolas Rashevsky Distinguished University Professor in 2002. After moving to the University of California Davis in 2003 he chaired the Department of Biomedical Engineering from 2003 to 2005. His honors include Guggenheim Fellow, Fulbright Senior Research Fellow, American Association for the Advancement of Science Fellow, American Institute for Medical and Biological Engineering Fellow, Institute of Electrical and Electronic Engineers Fellow, Moore Distinguished Scholar at the California Institute of Technology, Invited Scholar at the Institut des Hautes Études Scientifiques, 79th Josiah Willard Gibbs Lecturer for the American Mathematical Society, Stanislaw Ulam Distinguished Scholar Award from the Center for Non-Linear Studies, Los Alamos National Laboratory, Member of the US National Academy of Medicine, Honorary Doctor of Science, Universitat de Lleida, Spain, and The Michael A. Savageau Collegiate Professorship in Computational Medicine and Bioinformatics permanently endowed by the University of Michigan. He was Editor-in-Chief of Mathematical Biosciences from 1995 to 2005, and serves on advisory panels for the National Institutes of Health, the National Science Foundation, the Howard Hughes Medical Institute, the Keck Foundation, and the National Academies of Science. He lectures extensively in the US and abroad on his research, which is focused on biochemical systems theory with an emphasis on function, design and evolution of metabolic networks, signaling cascades, and gene circuitry.
A Novel Strategy to Accelerate the Modeling and Analysis of Complex Biological Systems
Although we have a well-defined concept of an organism’s genotype, its phenotypes – the biological functions implemented by its underlying biochemistry – are difficult to define and predict. We ultimately want to represent the phenotype by a mechanistic model that accurately describes the changes in the concentrations of the compounds under various conditions. The ‘architecture’ of a system can be inferred from high-throughput data, but numerous unknown kinetic parameters influence exactly how change in one concentration affects others. Often the phenotype becomes clear only when those parameters are known; even a simple model can exhibit many phenotypes given different parameter choices. Current approaches to determining phenotype thus focus first on finding parameter values for the underlying biochemistry, typically through a mixture of ad-hoc experimentation and computationally inefficient highdimensional numerical search. While these strategies have been used to fully characterize small systems in the pre-genomic era, a mechanistic understanding of systems, even of moderate size, derived from genotype data remains elusive. We propose a fundamental shift towards a post-genomic computational paradigm in which we first analytically determine the space of possible phenotypes for a given network architecture and then predict parameter values for their realization, predictions that can guide experimentation and further numerical analysis. This ‘phenotype-centric’ paradigm combines four innovations with the potential to accelerate our understanding of complex biological systems: (1) a rigorous mathematical definition of biochemical phenotypes, (2) a method for enumerating the phenotypic repertoire based on the biomolecular network architecture, (3) an integrated suite of computational algorithms for the efficient prediction of parameter values and analysis of the phenotypic repertoire, and (4) a userfocused environment for navigating the resulting space of phenotypes and identifying biologically relevant features. These innovations will facilitate deterministic and stochastic simulations that require parameter values, will accelerate both hypothesis discrimination in systems biology and the design cycle in synthetic biology, and will enable investigators to achieve predictive understanding of biomolecular phenotypes from genotype.
In the lecture, I will describe the underlying theory, review recent progress toward the realization of its potential, and outline some of the remaining challenges.
In the breakout session, I will work through a few specific applications, highlight areas of theory in need computer implementation, and explore connections to other modeling approaches and issues of scalability.
About Douglas Shepherd
Dr. Shepherd is an Assistant Professor in the Departments of Physics and Pediatrics at the University of Colorado Denver. He received his B.S. in Physics from University of California Santa Barbara in 2003 and his Ph.D. in Physics from Colorado State University in 2011. He was a postdoctoral scholar at Los Alamos National Laboratory from 2011-2013 in the Center for Integrated Nanotechnologies and Center for Nonlinear Studies. His interests are in developing and applying new fluorescent microscopy techniques, data processing algorithms, and statistical modeling tools to study single-cell heterogeneity in cellular decision-making processes. He has been involved in the q-Bio Summer School since 2011, including starting the Membrane Dynamics track and serving as co-organizer for the Single Cell Gene Regulation track.
About Simon Tavaré
Simon Tavaré is a Professor in the University of Cambridge Department of Applied Mathematics and Theoretical Physics and its Department of Oncology. Until end of January 2018, he was the Director of the Cancer Research UK Cambridge Institute, now part of the University of Cambridge. Simon’s research interests include statistical bioinformatics, computational biology, evolutionary approaches to cancer, statistics and stochastic computation. Simon received his BSc, MSc and PhD in probability and statistics from the University of Sheffield, UK. He spent 25 years in academia in the USA, where his research was funded by the NSF and the NIH. He moved to Cambridge in 2004. He has supervised some 35 postdoctoral fellows and 40 PhD students. His research has led to his election as a Fellow of the Academy of Medical Sciences (FMedSci) in 2009, a Fellow of the Royal Society (FRS) in 2011 and a member of EMBO in 2015. He is currently Director of the Wellcome Trust PhD programme in Mathematical Genomics and Medicine. He gave the American Mathematical Society’s Einstein Public Lecture in Mathematics, entitled “Cancer by the numbers”, in 2015 and was an invited speaker at ICIAM2015 in Beijing. He was, until recently, President of the London Mathematical Society.
About Jason Xu
Jason Xu received his PhD in Statistics from the University of Washington, and his research develops rich models for biological phenomena and inferential procedures for fitting such models to data. Methodologically, his recent work focuses on likelihood-based estimators under missing data arising from branching processes and other continuous-time Markov chains. Dr. Xu is particularly interested in applications to cancer proliferation, hematopoiesis, and other cell differentiation systems. Dr. Xu is an NSF Mathematical Sciences Postdoctoral Fellow in the Department of Biomathematics at the University of California Los Angeles, and will be joining the Department of Statistical Science at Duke University as an Assistant Professor in Fall 2018.