Speakers (in order of appearance)
"Emergence of cell shape and movement from molecular-scale dynamics"
Bio: Julie Theriot attended college at the Massachusetts Institute of Technology, earning dual B. S. degrees in physics and biology in 1988. She completed her Ph. D. in cell biology at the University of California at San Francisco in 1993, and then returned to Cambridge as a Whitehead Fellow at the Whitehead Institute for Biomedical Research. In 1997, she joined the faculty of the Stanford University School of Medicine, with appointments in the Department of Biochemistry and the Department of Microbiology & Immunology, and is currently an Investigator of the Howard Hughes Medical Institute. Her lab moved to the University of Washington in 2018, and she also has an appointment at the Allen Institute for Cell Science. The experimental work of her research group focuses on quantitative measurement of the dynamic and mechanical behavior of structural components in living cells, exploring the molecular and biophysical mechanisms of various forms of cell motility and shape determination across a variety of eukaroytic and bacterial cell types. Theriot has won numerous awards for her research, including the David and Lucile Packard Foundation Fellowship for Science and Engineering and the John D. and Catherine T. MacArthur Foundation Fellowship. She has also received multiple teaching awards from both M. D. and Ph. D. students at Stanford. She is a coauthor of the textbook Physical Biology of the Cell.
"Toward next-generation antibiotics: Systems-level dissection of bacterial responses to phagosomal stressors"
Abstract: Conventional antibiotic discovery has failed to keep pace with the rise of resistance, necessitating the use of novel methodologies to address this growing public health threat. Phagocytes are immune cells that engulf bacteria and kill them by exposure to antimicrobials. Pathogens protect themselves against phagosomal stresses, and those systems constitute therapeutic targets that are projected to be less prone to resistance development than those corrupted by current antibiotics. In this talk, I will discuss our work to understand how those defenses operate as a system, and how such knowledge could lead to new treatments.
Bio: Dr. Mark P. Brynildsen received his B.S. in Chemical Engineering from Rutgers University, New Brunswick in 2002 and earned his Ph.D. in Chemical Engineering from the University of California, Los Angeles (UCLA) in 2008, where he worked with Dr. James C. Liao. After working for 2 years as a Howard Hughes Medical Institute (HHMI) post-doctoral associate with James J. Collins within the Department of Biomedical Engineering at Boston University, Mark joined the faculty of the Department of Chemical and Biological Engineering at Princeton University in 2010. Currently, he holds the position of Associate Professor of Chemical and Biological Engineering at Princeton. The overarching goal of his research group is to improve the performance of current antibiotics and identify targets for novel anti-infectives. To accomplish this, the Brynildsen group uses computational and experimental techniques in systems biology and metabolic engineering to develop novel, fundamental understanding of the molecular mechanisms and networks pathogens use to thwart immune antimicrobials and antibiotics. Mark’s research has been published in journals such as Nature, Nature Biotechnology, Nature Communications, PNAS, and Molecular Cell, and he has been the recipient of a Howard B. Wentz, Jr. Junior Faculty Award and an NSF CAREER Award.
Neda Bagheri, Ph.D. | Northwestern University, University of Washington & Allen Institute for Cell Science
"Predicting how environmental context impacts cell populations"
Abstract: Computational models are essential tools that can be used to simultaneously explain and guide biological intuition. Dr. Bagheri's lab employs machine learning, dynamical systems, and agent-based modeling strategies to help explain biological observations, and to uncover fundamental principles that drive both individual cellular decisions and cell populations. They are interested in the inherent multiscale nature of cells—how “the whole is greater than the sum of its parts”—and in predicting cell population dynamics from the composition of simpler biological modules to advance basic science and medicine.
Bio: Neda Bagheri’s research lab seeks to (i) identify engineering ‘design principles’ that underlie, explain, and rationalize complex biological function, and (ii) understand how extrinsic factors can be used to guide the outcome (or health) of cell populations through intervention. Toward these goals, her interdisciplinary team of engineers, basic scientists and applied mathematicians combine experimental data with computational strategies derived from statistical analysis and control theory to address problems from creative angles not possible with single discipline methods. In recognition for her research accomplishments and vision, Bagheri was awarded a National Science Foundation CAREER Award (2017), and was honored as a Distinguished Speaker for the Accelerated Discover Forum at IBM Research-Almaden (2018) and for the Mindlin Foundation (2019). She serves on multiple scientific advisory and editorial boards, guiding the frontier of multidisciplinary research.
"Measuring and Modeling Variability in Drug Response in Cells, Tissues and Clinical Trials"
Bio: Peter Sorger is the Otto Krayer Professor of Systems Pharmacology at Harvard Medical School. He received his AB from Harvard College and PhD from Trinity College, Cambridge University U.K., working under the supervision of Hugh Pelham. He trained as a postdoctoral fellow at the University of California, San Francisco with Harold Varmus and Andrew Murray. Prior to moving to HMS Peter served as a Professor of Biology and Biological Engineering at MIT. Sorger was cofounder of Merrimack Pharmaceuticals and Glencoe Software and is an advisor to multiple public and private companies and research institutes in the US, Europe and Japan.
Peter’s research focuses on the signal transduction networks controlling cell proliferation and death, dysregulation of these networks in cancer and inflammatory diseases and mechanisms of action of therapeutic drugs targeting signaling proteins. His group uses mathematical and experimental approaches to construct and test computational models of signaling in human and murine cells as a means to understand and predict responses to drugs applied individually and in combination. The Sorger group also develops open-source software for analyzing biological networks and drug mechanism of action and it participates in multiple collaborative programs working to improve data access and reproducibility. Recent research extends a systems pharmacology approach to analysis of clinical samples and interpretation of clinical trials.
As founding head of the Harvard Program in Therapeutic Science (HiTS) and its Laboratory Systems Pharmacology (LSP) Peter leads a multi-institutional effort to advance the basic and translational science used to develop new medicines, create novel drug combinations and identify responsive patients. The LSP applies systems approaches to understanding and mitigating adverse drug effects and to designing new clinical trials. The recently established Harvard-MIT Center of Regulatory Sciences focuses on improving how drugs are evaluated, brought to market and used in patients. HiTS includes faculty from seven institutions.
"Dynamics, Feedback, and Transient Antibiotic Resistance in Single Cells"
Abstract: The majority of our understanding on antimicrobial drug resistance comes from studies on the genetic changes that cause it, however bacteria can also transiently survive antibiotic exposure even without permanent genetic changes. Using a combination of time-lapse microscopy experiments and stochastic modeling I will show how E. coli bacteria use feedback to generate dynamics and noise in expression of a key regulatory protein, providing transient antibiotic resistance at the single-cell level. In addition, I will discuss how expression of resistance genes can predispose cells towards mutation. These results are significant because they reveal important dynamic information about antibiotic resistance.
Bio: Mary Dunlop is an Associate Professor of Biomedical Engineering at Boston University with additional appointments in Molecular Biology, Cell Biology & Biochemistry and Bioinformatics. She graduated from Princeton University with a B.S.E. in Mechanical and Aerospace Engineering and a minor in Computer Science. She then received her Ph.D. from the California Institute of Technology, where she studied synthetic biology with a focus on dynamics and feedback in gene regulation. As a postdoctoral scholar, she conducted research on biofuel production at the Department of Energy's Joint BioEnergy Institute. Her lab engineers novel synthetic feedback control systems and also studies naturally occurring examples of feedback in gene regulation. In recognition of her outstanding research and service contributions, she has received many honors including a Department of Energy Early Career Award, a National Science Foundation CAREER Award, and the ACS Synthetic Biology Young Investigator Award.
"A multi-scale, integrated approach to understanding infection"
Abstract: The Paul G. Allen Discovery Center for Systems Modeling at Stanford was established to open an new window into infection by developing cutting-edge tools to characterize both host and pathogen, and integrating these into a combined systems approach. The diverse expertise of the team has enabled us to tackle fundamental questions in infection in all of our targeted areas of: modeling (moving from proteins to integrated whole cell models, and up to cell-cell interactions and tissues), computation (accelerating analysis and simulation, beginning with individual labs in the Center but rapidly moving to scientists worldwide and even beyond to the global non-scientific community through crowdsourcing and interactive visualization), and experimentation (innovating novel measurement technologies which encompass biophysical to physiological measurements to in vivo studies). I will be discussing this work, and how it has led us to a number of insights related to pathogen and host biology, host-pathogen interactions, the heterogeneity of infection and antibiotic development. I will also describe a suite of experimental and computational tools which enable the broader community to accelerate their own research in exciting new ways.
Bio: Markus Covert's lab has generated several new technologies to measure, analyze, and mathematically model the behaviors of individual cells. The lab is best known for constructing the first "whole-cell" computational model, which explicitly represents all known gene functions and molecules in a bacterial cell - an advance which was highlighted by the journal Cell as a highlight publication of the 40-year history of that journal. Dr. Covert is a recipient of the NIH Director's Pioneer Award and the Paul G. Allen Family Foundation Distinguished Investigator and Discovery Center awards. He also advises Emerald Cloud Labs and X Labs, formerly Google [X].
"Hybrid Simulations of Minimal Bacterial and Eukaryal Cells"
Bio: Professor Zaida (Zan) Luthey-Schulten received a B.S. in Chemistry from the University of Southern California in 1969, an M.S. in Chemistry from Harvard University in 1972, and a Ph.D. in Applied Mathematics from Harvard University in 1975. From 1975 to 1980, she was a Research Fellow at the Max-Planck Institute for Biophysical Chemistry in Göttingen, and from 1980 to 1986, a Research Associate in the Department of Theoretical Physics at the Technical University of Munich. Professor Luthey-Schulten has been at the University of Illinois since 1987, where she is currently the Murchison-Mallory Professor of Chemistry, co-director of the NSF Center for the Physics of Living Cells, and co-investigator at the NIH Resource of Macromolecular Modeling and Bioinformatics at the Beckman Institute.
Her research group develops the GPU-based software Lattice Microbes for spatially-resolved stochastic simulations of whole bacterial and eukaryal cells at biologically relevant length (micron), time (hours), and concentrations (nM to mM) scales.
"The Physical Brain"
Abstract: The brain is an immensely complex physical system whose structures and dynamics span many orders of magnitude in space and time, as do measurement and imaging methods. By using techniques from physics and mathematics, it is possible to model brain activity and structure from the millimeter scale up to the whole brain, and to interrelate different types of measurements and phenomena across scales. This talk gives a taste of this multiscale integration and its potential to provide new approaches to data analysis and better links between theory and experiment. Examples of applications are drawn from experiments on normal and epileptic brain activity, electroencephalography, evoked responses to stimuli, brain state tracking, and brain modes.
Bio: Peter Robinson received his PhD in theoretical physics from the University of Sydney in 1987, then held a postdoc at the University of Colorado at Boulder until 1990. He then returned to Australia, and joined the permanent staff of the School of Physics at the University of Sydney in 1993, obtaining a chair in 2000, and subsequently holding two Australian Research Council Federation Fellowships before his present Laureate Fellowship. His current work is mainly on brain dynamics, networks, complex systems, field theory, propagators, critical phenomena, imaging, and waves. He is a Chief Investigator on the ARC Center for Integrative Brain Function, the Cooperative Research Center for Alertness, Safety, and Productivity, the NHMRC Center Neurosleep, and the University of Sydney Center for Complex Systems.
"Evaluating the merit of candidate brain models with brain simulations"
Abstract: A key element of several large-scale brain research projects is the simulation of large-scale model networks of neurons. In the talk I will argue why such simulations will be indispensable for bridging the scales between the neuron and system levels in the brain. To allow for systematic comparison and refinement of candidate network models by comparison with experiments, the simulations should not only predict neuronal action potentials. Instead, they should be multimodal in that they should also predict electric, magnetic, optical, and eventually also hemodynamic signals measuring population- and systems-level activity. Results from preliminary multimodal analysis of the comprehensive model for mouse primary visual cortex developed at the Allen Institute for Brain Science will be presented.
Bio: Gaute Einevoll is a professor of physics at the Norwegian University of Life Sciences and the University of Oslo working on computational neuroscience/brain physics. His main interest is physics-type modelling of nerve cells, networks of nerve cells, and brain tissue. He is participating in the large-scale EU Human Brain Project started in 2013 and scheduled to end in 2023.
"The role of theory and modeling in neuroscience"
Bio: Adrienne Fairhall is a Professor in the Department of Physiology and Biophysics and adjunct in the Departments of Physics and Applied Mathematics at the University of Washington. She obtained her Honors degree in theoretical physics from the Australian National University and a PhD in statistical physics from the Weizmann Institute of Science. She received her postdoctoral training at NEC Research Institute with Bill Bialek and at Princeton University with Michael J. Berry II. She is the director of the Computational Neuroscience Program at UW and, with Prof. Tom Daniel, co-directs the UW Institute for Neuroengineering. She has directed the MBL course, Methods in Computational Neuroscience. Her work focuses on dynamic neural computation, with a particular interest in the interplay between cellular and circuit dynamics and coding.
"Data-Driven Modeling of the Cortex Based on a Systematic Experimental Platform"
Abstract: This talk will describe realistic models of the mouse primary visual cortex constructed from multimodal experimental data. The data come from extensive surveys of cortical cell types, systematic integration of studies of cortical connectivity, and high-throughput recordings of neuronal activity in vivo. The models reproduce multiple experimental observations and make mechanistic predictions about the structure-function relationships in cortical circuits. To support this work, we developed the software suite called Brain Modeling ToolKit and a modeling file format called SONATA. These tools, the models, and simulation results are all being made freely available via the Allen Institute Modeling Portal.
Bio: Anton Arkhipov joined the Allen Institute in 2013 as an assistant investigator in the Modeling, Analysis, and Theory group. He is leading efforts to carry out biophysically detailed simulations of individual neurons as well as large-scale neuronal circuits from the mouse visual system. The main focus of his research is on integration of experimental anatomical and physiological data to build sophisticated, highly realistic computational models of cortical circuitry, with the aim of elucidating mechanisms underlying processing of visual information in the cortex. Before joining the Allen Institute he was a Postdoctoral Fellow at D. E. Shaw Research in New York City, where he used a specialized supercomputing architecture to perform computational studies of structure-function relationships in proteins, with the emphasis on cancer-associated cell-surface receptors. Arkhipov received his B.S. and M.S. in Physics from Moscow Institute of Physics and Technology and a Ph.D. in Physics from the University of Illinois at Urbana-Champaign.
"A null model of the mouse whole-neocortex micro-connectome"
Abstract: The study of neuronal connectivity has traditionally been conducted on two separate scales: macro- and meso-scale connectomics, considering the connections between regions and populations of neurons, and micro-scale connectomics, considering connections between individual neurons and sub-domains of neurons. We combine these two complementary approaches to build a statistical model of the connectivity of mouse neocortex with sub-cellular resolution. The model is constrained by data on region-to-region connectivity and by reconstructions of long-range projection axons. We predict a targeting principle that models the innervation logic of individual projecting neurons from meso-scale connectomics data. The model recreates biological trends that have been characterized on various scales, and it predicts that an established principle of a scale invariant structure of connectivity extends down to the level of individual neurons. It will enable biologically more detailed whole-brain simulations and can serve as a powerful null model for future discoveries in long-range micro-connectomics.
Bio: Michael Reimann leads the Connectomics group of the Blue Brain Project at the EPFL, Switzerland. For his doctoral research he developed algorithms to derive microcircuit connectivity and study the emergence of microcircuit activity.
He is one of the main contributors of digital microcircuit modeling and simulation efforts in the Blue Brain Project. His research is focused on synaptic connectivity at all scales, how it is shaped by plasticity mechanisms and how this in turn determines brain function. To this end he employs advanced simulation tools on massively parallel supercomputing systems and develops novel analyses, based on classical information theory and algebraic topology.
"Systems Biology of Mammalian Sleep/Wake Cycles: Phosphorylation Hypothesis of Sleep"
Abstract: The detailed molecular and cellular mechanisms underlying NREM sleep (slow-wave sleep) and REM sleep (paradoxical sleep) in mammals are still elusive. To address these challenges, we first constructed a mathematical model, Averaged Neuron Model (AN Model), which recapitulates the electrophysiological characteristics of the slow-wave sleep. Comprehensive bifurcation analysis predicted that a Ca2+-dependent hyperpolarization pathway may play a role in slow-wave sleep. To experimentally validate this prediction, we generate and analyze 26 KO mice, and found that impaired Ca2+-dependent K+ channels (Kcnn2 and Kcnn3), voltage-gated Ca2+ channels (Cacna1g and Cacna1h), or Ca2+/calmodulin-dependent kinases (Camk2a and Camk2b) decrease sleep duration, while impaired plasma membrane Ca2+ ATPase (Atp2b3) increases sleep duration. Genetical (Nr3a) and pharmacological intervention (PCP, MK-801 for Nr1/Nr2b) and whole-brain imaging validated that impaired NMDA receptors reduce sleep duration and directly increase the excitability of cells. Based on these results, we propose phoshporylation hypothesis of sleep that phosphorylation-dependent regulation of Ca2+-dependent hyperpolarization pathway underlies the regulation of sleep duration in mammals. We also recently developed a simplified mathematical model, Simplified Averaged Neuron Model (SAN Model), which uncover the important role of K+ leak channels in NREM sleep. In this talk, I will also describe how we identify essential genes (Chrm1 and Chrm3) in REM sleep regulation, and propose a plausible molecular definition of a paradoxical state of REM sleep.
Bio: Dr. Hiroki R. Ueda was born in Fukuoka, Japan, in 1975. He graduated from the Faculty of Medicine, the University of Tokyo in 2000, and obtained his Ph.D in 2004 from the same university. He was appointed as a team leader in RIKEN from 2003. He became a professor of Graduate School of Medicine, the University of Tokyo in 2013. He is currently appointed as an affiliate professor in Graduate School of Information Science and Technology and an principle investigator in IRCN (International Research Center for Neurointelligence) in the University of Tokyo, an invited professor in Osaka University, and a visiting professor in Tokushima University.
He has an expertise in systems biology and focus on chronobiology by investigating mammalian circadian clocks and sleep/wake cycles. He found that Ca2+ and CaMKII-dependent hyperpolarization pathways underlie sleep homeostasis and proposed phosphorylation hypothesis of sleep. He also found muscarinic receptors, M1 and M3, as essential genes for REM sleep. To accelerate these studies, he invented whole-brain and whole-body clearing and imaging methods called CUBIC, as well as the next-generation mammalian genetics such as Triple-CRISPR and ES-mice for one-step production and analysis of KO and KI mice without crossing.
He received awards, including Tokyo Techno Forum 21, Gold Medal, Young Investigator Awards and IBM Science Award, a Young Investigator Promotion Awards. He also received Tsukahara Award, Japan Innovator Awards, Yamazaki-Teiichi Prize, Innovator of the Year and The Ichimura Prize in Science for Excellent Achievement.
"Learning to read the cellular adaptive immune repertoire"
Abstract: The genetic alterations that drive tumor formation and growth also enable the cellular adaptive immune system to specifically recognize cancer cells. This enables immunotherapy and cancer vaccines. The specifics of what is actually recognized and attacked on a tumor cell is largely unknown because we lack the tools to connect the targeting molecules of the cellular adaptive immune system, T cell receptors (TCRs), to cancer antigens at scale. We have created a set of technologies to help address this problem. In particular, we have multiplexed a cellular immunology assay that leverages the precision of Next Generation Sequencing to connect TCR sequences to antigen specificity. This technology has been combined with machine learning techniques to significantly expand known antigen specificity of public TCRs to unique private sequences.
Bio: Dr. Robins leads the scientific research and development teams at Adaptive Biotechnologies as Chief Scientific Officer and Co-Founder. He is also currently a Full Faculty Member at the Fred Hutchinson Cancer Research Center (FHCRC). Dr. Robins’ obtained his bachelor’s degree at Harvard University as a physics major with a concentration in mathematics. He then obtained his Masters and Ph.D. in theoretical physics from the University of California Berkeley with a visiting appointment to the California Institute of Technology. Dr. Robins obtained a postdoctoral appointment in theoretical physics in the particle theory group at the Weizmann Institute of Science in Israel. Interested in the mathematics behind genetics and observing the potential utility of high-level mathematics to study problems in the biological sciences, Dr. Robins took another postdoctoral appointment at the Institute for Advance Study in Princeton to study under the famed biologist Dr. Arnold Levine. Working with Dr. Levine, Dr. Robins concentrated on developing bioinformatic algorithms for micro RNA targets and bacterial genome analysis, a precursor to his current faculty appointment at the Fred Hutchinson Cancer Research Center in the Computational Biology Group, Public Health Sciences and Human Biology Divisions.
"Mapping how small differences of tumor antigenicity translate into divergent functional outcomes for T cell activation"
Abstract: Recent progress in systems immunology have ushered a quantitative understanding of T cells’ ability to discriminate antigens (e.g. self vs non-self) on a short timescale (<1 hr). Yet understanding how T cell differentiation, proliferation and overall response unfold over long timescales (>1 week) is a challenge with fundamental and clinical applications. To model how such exquisite sensitivity in T cell activation emerges, we developed an ex vivo robotic platform that establishes and quantitatively monitors T cell activation cultures, over long timescales. We used this system to map out the dynamics of T cell response to antigens of varied quality and quantity, across multiple readouts. We then quantitatively modeled how such high-dimensional measurements can be reduced to yield rigorous quantification of the quality and quantity of antigens T cell respond to. We will discuss how such quantitative approaches can be applied to help theoreticians test models and clinicians monitor patients’ response..
Bio: Gregoire has trained in Statistical Physics and nonlinear dynamics (PhD) and in Immunology (post-doctoral studies). His field of expertise is Systems Immunology: the ImmunoDynamics group he is heading has been developing experimentally validated quantitative models of different aspects of the immune system. In particular, they have addressed the interplay between the robustness and variability of self/non-self discrimination in the immune system. They are also focused on developing quantitative models of lymphocyte-lymphocyte communications via cytokine. Their current projects focus on the multicellular coordination of immune responses against tumors and pathogenic infections. They are particularly interested in developing quantitative models of the integration of signal transduction, gene regulation, cytokine communications, cell differentiation, and proliferation/death across multiple spatio-temporal scales. Their long-term goal is to help in the development of tailored immunotherapies (e.g. against tumors).
"The long and the short of it: paths to engineering TB treatment"
Abstract: Mycobacterium tuberculosis infects billions of people worldwide, killing more than 1.5 million per year. Motivated by the fact that TB is actually treated by combinations of antibiotics, we have recently developed a quantitative framework to efficiently measure, analyze, and predict pairwise and high-order drug interactions, allowing us to prioritize combinations. Here we leverage the efficacy and ease-of-use of our assay to systematically measure drug combinations in multiple growth conditions. We aim to use this data compendium to more precisely map in vitro to in vivo efficacy data, account for the heterogeneity of tuberculosis infection, and formulate more effective drug regimens.
Bio: Bree Aldridge is an Assistant Professor in the Department of Molecular Biology and Microbiology and Department of Biomedical Engineering at Tufts University. The Aldridge lab seeks to bring a quantitative framework to understand tuberculosis infection and to drive multi-drug regimen design in a data-driven manner. She specializes in combining quantitative experiments and mathematical modeling to create intuitive descriptions of complex cell biology. Her lab website is: https://sites.tufts.edu/aldridgelab/
"Modeling how molecular interactions shape cellular signaling processes"
Abstract: While crystallographers and molecular dynamics modelers continue to identify new details of molecular interactions and how these determine signaling kinetics on small spatio-temporal scales few efforts aim at incorporating such details into computational models of cellular signaling processes. In particular for spatially resolved (reaction-diffusion) models of cellular behavior, strongly simplified mass-action approaches or phenomenological models are used most frequently. I will describe how computational models can be based on the specification of molecular binding site interactions while still being able to scale up to spatially resolved simulations of rather complex cellular signaling pathways and make useful mechanistic predictions.
Bio: Dr. Meier-Schellersheim obtained a Ph.D. in physics in 2001 from the University of Hamburg, Germany. His research focuses on building a bridge between experimental and computational cell biology through the development and application of modeling tools that combine accessible graphical interfaces with the capability to perform spatially and temporally highly resolved simulations, even for models of complex cellular signaling processes.
"Connect the Dots – An integrative cell centered view of immunity"
Abstract: Protective immunity is not the end outcome of any single cell, but rather draws on functionality elicited by many cell types communicating between one another. To date, using reductionist techniques, immunologists have elucidated many of the basic principles of individual cell type behavior at a given condition. However, complex inter-cellular circuitry and whole system effects are difficult to capture or understand.
Recent technological advances allow us to probe the immune system at high resolution and explore its variation between individuals. Yet the question remains how we move from just a high dimensional data pile to truly thinking about immunity as a system which can be modeled such that we can intelligently reason on system-level effects of perturbations.
Here, Dr. Shen-Orr will describe his laboratory's ongoing efforts to build a system level cell-centered view of genomic data, and its integration with knowledge in the primary immunology literature. Data and knowledge put together in this cell-centered framework establish a means to 'connect the dots' across immunology as well as systematic de novo hypotheses generation.
Bio: Shai Shen-Orr is an Associate Prof. at the Faculty of Medicine at the Technion – Israel Institute of Technology, where he heads the Systems Immunology & Precision Medicine laboratory since 2012. His research is focused on charting the immune system landscape – namely the principles by which the immune system varies over time as a function of environment and genetics. For this, his lab develops novel computational methodologies, empowering human immune monitoring for precision medicine.
Prof. Shen-Orr received a BSc from the Technion in Information Systems (1999), an M.Sc. in Bioinformatics at the Weizmann Institute of Science (2002), a Ph.D. from Harvard University in Biochemistry (2007) and performed his postdoctoral studies at Stanford University with Prof. Mark Davis and Prof. Atul Butte.
Prof. Shen-Orr is also the Chief Scientist of CytoReason. CytoReason is building a machine learning model of the immune system; which it applies to drug development.
"Exploring a role for cell-to-cell variability in macrophage functional plasticity"
Abstract: Macrophages are cells of the innate immune system with functional roles in homeostasis, tissue repair, and immunity. Cell-to-cell variability might allow macrophages to precisely respond to cues in their microenvironment, while also maintaining functional plasticity to enable these diverse roles. To explore this possibility, we coupled single-cell measurements with computational analyses to understand how macrophages coordinate their response when exposed to inflammatory (M1) cues in the presence and absence of conflicting alternate (M2) cues. We found that macrophages exhibit significant cell-to-cell variability in secreted signals in response to the M1-cue LPS, such that a small fraction of cells appears to drive the population response. Modeling of our single-cell data uncovered regulatory connections between secreted cytokines in the network. When macrophage populations were stimulated with both M1 and M2 cues, we found that transcription of many M1- and M2-associated genes was negatively cross-regulated in the population, but analysis of single-cell transcription and secretion suggested that subsets of macrophages downregulate only one of these programs. Our results support a role for cell-to-cell variability in tuning macrophage responses in complex microenvironments.
Bio: Kathryn Miller-Jensen is an Associate Professor of Biomedical Engineering and Molecular, Cellular, and Developmental Biology at Yale University. Her lab combines experimental and computational approaches to study signaling and transcriptional regulation in the immune system, with a focus on how intercellular heterogeneity drives disease phenotypes. Recent work in the lab explores the regulation of functional heterogeneity in macrophages in inflammation, and how macrophage-mediated extracellular signaling networks shape the tumor microenvironment. Her research is supported by the National Institutes of Health, the Bill and Melinda Gates Foundation, and a National Science Foundation CAREER Award. Prof. Miller-Jensen was an NIH NSRA Postdoctoral Fellow at the University of California at Berkeley and she holds a Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology.