Seminars Spring 2017
Seminars are on Tuesdays at 4 pm in Smith Hall Annex, A-Level, NEW Physics Seminar Room, preceded by tea at 3:45, unless otherwise noted.
January 17, 2017: Taekjip Ha, Johns Hopkins UniversityRevisiting and Repurposing the Double HelixHost: P. SulcDNA is an iconic molecule that forms a double helical structure, providing the basis for genetic inheritance, and its physical properties have been studied for decades. In this talk, I will present evidence that surprising physical properties of DNA such as flexibility and self-association may be important for biological functions [1,2]. In addition, I will present a new application of DNA where mechanical modulations of cell behavior can be studied at the single molecule level using rupturable DNA tethers . We found that cells can change their behavior dramatically in response to just two molecules strongly tugging on them . References.  R. Vafabakhsh and T. Ha, “Extreme bendability of DNA less than 100 base pairs long revealed by single molecule cyclization”, Science 337, 1097-1101 (2012).  T. Ngo, Q. Zhang, R. Zhou, J. G. Yodh and T. Ha, “Asymmetric unwrapping of nucleosomes under tension directed by DNA local flexibility”, Cell 160, 1135-1144 (2015).  X. Wang and T. Ha, “Defining Single Molecular Forces Required to Activate Integrin and Notch Signaling”, Science 340, 991-994 (2013).  M. Roein-Peikar, Q. Xu, X. Wang and T. Ha, “Ultrasensitivity of cell adhesion to the presence of mechanically strong ligands,” Physical Review X (2016).
February 7, 2017 (2PM - Physics Fellow Candidate): Henrik Ronellenfitsch, University of PennsylvaniaAdaptation, Growth, and Resilience in Biological Distribution NetworksHost: M. FeigenbaumHighly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. We show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as plant and animal vasculature. In addition, we show how the incorporation of spatially collective fluctuating sources yields a minimal model of realistic reticulation in distribution networks and thus resilience against damage. Distribution networks are shown to exhibit a trade-off between resilience, construction cost, and efficiency, with nature selecting for those phenotypes that lie on the Pareto-efficient front.
February 9, 2017 (2PM - Physics Fellow Candidate): David Zeevi, Weizmann Institute of ScienceCan our microbes tell us what to eat?Host: M. FeigenbaumThe past decades have witnessed a surge in the prevalence of obesity, diabetes and the metabolic syndrome. Many of these disorders are associated with high post-meal blood glucose responses, but common dietary methods for controlling these responses have limited efficacy, mainly due to high interpersonal variability in the response to even the same meal. One of the factors underlying this variability is the gut microbiome: a huge ecosystem of bacteria, archaea, viruses and eukaryotes with vast potential metabolic capacity. In our work we developed new tools for the analysis of the gut microbiome and used these tools, along with blood parameters, dietary habits, anthropometrics and physical activity to accurately predict post-meal blood glucose responses to real-life meals. These predictions were then used to design personalized diets which successfully reduced hyperglycemia. Our results suggest that personalized diets can successfully lower post-meal blood glucose and its grave metabolic consequences.
February 14, 2017 (2PM - Physics Fellow Candidate): Edward Banigan, Northwestern UniversityMechanisms of spatiotemporal chromosome positioningHost: M. FeigenbaumSpatial localization of chromosomes and their genes is tightly controlled throughout the cell cycle, which ensures proper gene expression during interphase and faithful inheritance of the genome by daughter cells during mitosis. The cellular structures that govern these processes — the nucleus and the mitotic spindle — are complex assemblies of biological components, whose mechanical properties are essential to their functions. The two main mechanical components of the cell nucleus are chromatin and the lamina. Corresponding to these components, my coarse-grained polymer model predicts that the nucleus has two mechanical regimes of force response to physical deformations. This picture is supported by micromanipulation experiments in which isolated nuclei are stretched and perturbed through a variety of cell biological and biochemical perturbations. Altogether, this force response observed in the experiments and explained by the model protects the global organization of genome during interphase. During mitosis, after the nucleus disassembles, however, the microtubule spindle governs chromosome positioning. Building on observations from in vitro experiments, I developed a stochastic model for the collective dynamics of microtubules comprising the spindle. The model revealed a striking mechanism for coordinating force-sensitive filament dynamics — a bistable force-velocity relation. Within this model, I capture known metaphase behaviors such as chromosome oscillations and error correction, and I accurately predict the results of novel experiments in which microtubule dynamics are perturbed via Aurora B kinase. The model thus provides a basic framework for physically understanding regulated metaphase chromosome motions as well as defective motions, which can lead to harmful scenarios, such as aneuploidy. Together, these models show how cellular machinery can robustly control the spatiotemporal positions of chromosomes through structure, geometry, and mechanotransduction.
February 16, 2017 (2PM - Physics Fellow Candidate): Jasmine Nirody, University of California-BerkeleyThe flagellum unwound: Torque generation in the bacterial flagellar motorHost: M. FeigenbaumBacteria were among the first life forms on Earth, and are found in all of its corners. In order to survive in a variety of environments, bacterial species have developed a variety of locomotive strategies. The most common of these is flagellated swimming, in which bacteria are propelled by the motion of several long filaments that sprout of the cell body. A flagellar filament is rotated by a molecular machine at its base, the aptly-named bacterial flagellar motor (BFM). The motor’s central role in bacterial motility has made uncovering its operating principles a fundamental challenge in biophysics and microbiology. In this talk, I will present a model for the BFM's fundamental torque-generation mechanism, and show that model predictions are consistent with experimental observations of motors in various external conditions. I will further discuss recent theoretical and experimental advances in our mechanistic understanding of this nanomachine, and the implications of these advances to several essential biological processes such as bacterial pathogenicity, chemotaxis, and biofilm formation.
February 21, 2017 : Canceled
March 7, 2017: Massimo Vergassola, UC San DiegoNavigating turbulent environmentsHost: P. SulcThermal soaring by birds and olfactory searches by insects are biological examples of navigation in the presence of orientation cues that are complex due to the physics of fluids. The two problems also have technological applications, namely for extending the autonomy of flying vehicles/gliders, and for the development of olfactory robots. I shall first review the animal behavior, then present the physics of the orientation cues, and finally discuss the corresponding navigation problems.
CANCELLED: March 14, 2017: Clifford Brangwynne, Princeton UniversityMeasuring the Intracellular Dew Point: Phase Transitions in CellsHost: P. SulcIn this talk I will discuss our work showing that phase transitions play an important role in organizing the contents of living cells. We focus on a class of membrane-less RNA and protein rich organelles, known as RNP bodies, which help control the flow of genetic information within cells. The nucleolus is one such nuclear RNP body, which is important for cell growth and size homeostasis. We've shown that a phase transition model explains many features of nucleolar assembly, and that the internal subcompartments of the nucleolus arise from multi-phase coexistence, which may have important consequences for sequential RNA processing. I will also discuss our new "Optodroplet" approach, which enables spatiotemporal control of phase transitions within living cells, allowing us to begin quantitatively mapping intracellular phase diagrams. This approach has begun to yield rich insights into the link between intracellular liquids, gels, and the onset of pathological protein aggregation.
March 21, 2017: Julius Lucks, Northwestern UniversityUncovering How RNA Molecules ‘Make Decisions’ On the Fly: Towards Understanding and Engineering Cotranscriptional RNA FoldingHost: P. SulcRNAs are emerging as a powerful substrate for engineering gene expression and cellular behavior since they are now known to control almost all aspects of gene expression. As with all biomolecules, RNA function is intimately related to its structure, since RNA can adopt structures that selectively modulate gene expression. Central questions in biology and bioengineering then are: How do RNAs fold inside cells?; and How can we engineer these folds to control gene expression? In this talk, I will present our work at the interface of these two questions and share results that are beginning to uncover design principles for understanding natural RNAs and engineering RNAs for an array of applications. I will start by presenting our work on engineering RNA molecular switches that control transcription. The desire to uncover design principles for engineering these RNAs motivates our development of SHAPE-Seq, a technology that couples chemical probing with next-generation sequencing and that helps characterize RNA structures on an ‘omics’ scale. I will then describe our exciting recent developments in using SHAPE-Seq to help break open one of the frontiers of RNA structure-function relationships by uncovering at nucleotide resolution how RNAs fold cotranscriptionally. Specifically I will highlight new data on uncovering the ligand-dependent folding pathways of riboswitches, and how we are beginning to use these datasets to computationally reconstruct cotranscriptional folding pathways. This new ability is allowing us to ask deep questions about how RNA molecules make regulatory decisions ‘on the fly’ during the dynamic process of transcription. By probing the fundamental processes of RNA folding and function, these studies are expected to greatly aid RNA engineering.
March 28, 2017: Stephen Altschul, National Center for Biotechnology Information
Inaugural Peter H. Sellers Lecture
Greenberg Building, B-Level, Carson Family AuditoriumDirichlet Mixtures, the Dirichlet Process, and the Topography of Amino Acid Multinomial SpaceHost: Marcelo MagnascoThe Dirichlet Process is used to estimate probability distributions that are mixtures of an unknown and unbounded number of components. Amino acid frequencies at homologous positions within related proteins have been fruitfully modeled by Dirichlet mixtures, and we have used the Dirichlet Process to construct such distributions. The resulting mixtures describe multiple alignment data substantially better than do those previously derived. They consist of over 500 components, in contrast to fewer than 40 previously, and provide a novel perspective on protein structure. Individual protein positions should be seen not as falling into one of several categories, but rather as arrayed near probability ridges winding through amino-acid multinomial space.
April 4, 2017: Kunihiko Kaneko, University of TokyoDeep Linearity in Adaptation and Evolution: Macroscopic theory, microscopic simulation, and bacterial experimentsHost: T. ShendrukQuantitative characterization of plasticity, robustness, and evolvability is one of the most important issues in biology. Based on statistical physics and dynamical-systems theory, we present a macroscopic theory of fluctuation and responses in cellular states. By assuming that cells undergo steady growth, protein expression of thousands of genes is shown to change along a one-dimensional manifold in the state space in response to the environmental stress. This leads to a macroscopic law that cellular-state changes satisfy, as is confirmed by adaptation experiments of bacteria under stress. Next, we present proportionality between phenotypic changes by genetic evolution and by environmental adaptation, uncovered both in bacterial experiments and simulations. This relationship is then formulated by the hypothesis that phenotypic changes in adaptation and evolution are dominantly constrained along one-dimensional path. Possible extension of the theory to non-growing cellular states and to multi-level evolution for multicellularity will be briefly discussed.
1. Kaneko K., Life: An Introduction to Complex Systems Biology, Springer (2006)
2. K. Sato, Y,Ito, T.Yomo, K. Kaneko, "On the Relation between Fluctuation and Response in Biological Systems" Proc. Nat. Acad. Sci. USA 100 (2003) 14086-14090
3. K. Kaneko, "Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics" PLoS One(2007) 2 e434
4. K. Kaneko, "Phenotypic Plasticity and Robustness: Evolutionary Stability Theory, Gene Expression Dynamics Model, and Laboratory Experiments", Evolutionary Systems Biology (2012) (Springer, ed. O. Soyer)
5. K. Kaneko, C.Furusawa, T. Yomo, "Macroscopic phenomenology for cells in steady-growth state", Phys.Rev.X(2015) 011014
7. C. Furusawa, K. Kaneko "Global Relationships in Fluctuation and Response in Adaptive Evolution", J of Royal Society Interface (2015)
April 11, 2017: Dan Landau, Weill Cornell Medical CollegeOn the Evolution of LeukemiaHost: T. ShendrukCancer progression, relapse and resistance are the result of an evolutionary optimization process. Vast intra-tumoral diversity provides the critical substrate for cancer to evolve and adapt to the selective pressures provided by effective therapy. Thus, understanding intra-tumoral diversity and evolutionary dynamics will be a critical step in the development of effective, curative therapies for cancer.
In order to study these questions, we characterized the intra-tumoral genetic heterogeneity of chronic lymphocytic leukemia (CLL) using massively parallel sequencing of large patient cohorts (Landau et al, Cell, 2013, Nature, 2015). These studies have shown that CLLs contain genetically distinct subpopulations that compete and mold the genetic makeup of the malignancy. Furthermore, we have demonstrated that this heterogeneity can help predict the future evolutionary trajectories of CLL, and that higher intra-tumoral heterogeneity in the pre-treatment sample predicts adverse outcome.
Ongoing efforts are dedicated to studying the quantitative evolutionary dynamics that enable the relapse clone to replace the pre-treatment clone (Burger et al, Nature Communications, 2016, and manuscript in review). Using deep sequencing with high temporal resolution we determine the therapy specific clonal fitness with first line chemoimmunotherapy and targeted therapy. These investigations offer a novel framework for the study of the evolutionary dynamics that underlie disease relapse, directly in patients. Moreover, clonal dynamics provide precision prognostication of the timing and clonal composition of relapse. We also use these measurements to tailor personalized therapeutic regimens that would result in longer remissions, laying the foundations for algorithmically driven cancer therapy.
Additionally, in order to comprehensively study cancer evolution, we developed tools to study intra-tumoral epigenetic heterogeneity, as epigenetic somatic changes are heritable and impact the cellular fitness that is selected in the evolutionary process. With these tools, we uncovered a central feature of the cancer epigenome: massive stochastic disorder in methylation patterns. We have further shown that this stochastic disorder impacts transcription, evolution and clinical outcome (Landau et al, Cancer Cell, 2014). Thus, methylation changes in cancer may be similar to the process of genetic diversification, in which stochastic trial and error leads to rare fitness enhancing events. Furthermore, we have performed large-scale single-cell bisulfite sequencing of CLL cells and B cells from healthy donors. We found a close relationship between epimutation and the evolutionary age of the cells. As each generation has a given likelyhood of generating additional stochatic DNA methylation changes, stochastic disorder estimate may reflect the number of generations in the cells evolutionary history. Finally, the phased single cell data allows to reconstruct phylo(epi)genetic relationship between the cells, and infer the stochastic epimutation rate across the genome.
Collectively, these studies demonstrate the tremendous degree of intra-tumoral diversity that fuels cancer evolution, and highlight the need to integrate intra-tumoral heterogeneity in the development of the next generation of cancer therapeutics.
April 13, 2017 (NOTE Thursday): Sidhartha Goyal, University of TorontoStatistical mechanics of stem cellsHost: T. ShendrukMuch of complex biology results from interactions among a large number of individually simpler elements. Behavior of large collection of cells from microbes to stem cells are no different. Nonetheless, the population dynamics of heterogeneous populations is only now beginning to attract attention it deserves because we have only just, within in the last decade or so developed experimental tools for tracking heterogeneous populations. In this talk I will describe how theoretical ideas from statistical mechanics are being used to understand behavior of such heterogeneous populations, focusing on two examples. In first, I will present a coarse-grained model of blood regeneration, which provides a framework to understand large variations (~3 orders of magnitude) among contributions from individual stem cells without active competition. In contrast, the second describes how competition plays a central role in understanding dynamics of reprogramming population of somatic cells.
April 18, 2017: Erwin Frey, LMU MunichProtein Pattern Formation: Rethinking Nonlinear DynamicsHost: T. ShendrukProtein pattern formation is essential for spatial organization of many intracellular processes like cell division, flagellum positioning, and chemotaxis. More generally, these systems serve as model systems for self-organization, one of the core principles of life.
We present a rigorous theoretical framework able to generalize and unify pattern formation for quantitative mass conserving reaction-diffusion models. Mass redistribution controls local chemical equilibria. Separation of diffusive mass redistribution on the level of conserved species provides a general mathematical procedure to decompose complex reaction-diffusion systems into effectively distinct functional units, and to reveal the general underlying bifurcation scenarios. We apply this framework to Min protein pattern formation and identify the mechanistic roles of both involved protein species. MinD generates polarity through phase separation, whereas MinE takes the role of a control variable regulating the existence of polarized MinD patterns. Hence, polarization and not oscillations is the generic core dynamics of Min proteins in vivo. This establishes an intrinsic mechanistic link between the Min system and a broad class of intracellular pattern forming systems based on bistability and phase separation (wave-pinning). Oscillations are facilitated by MinE redistribution and can be understood mechanistically as relaxation oscillations of the polarization direction.
April 25, 2017: Nathan Kutz, University of WashingtonData-driven discovery of governing equations in the engineering, physical and biological sciencesHost: T. ShendrukWe demonstrate that the integration of data-driven dynamical systems and machine learning strategies are now capable of extracting governing laws from time-series measurements of physical/biophysical systems. Specifically, we demonstrate that we can use emerging, large-scale time-series data from modern sensors to directly construct, in an adaptive manner, governing equations, even nonlinear dynamics, that best model the system measured using sparsity-promoting techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of example problems. Ultimately, the method can be used to construct adaptive controllers which are capable of obtaining and maintaining optimal states while the machine learning and sparse sensing techniques characterize the system itself for rapid state identification and improved optimization.
April 27, 2017 (Thursday): Lawrence Williams, Rutgers UniversityTransient Symmetry and Self-Similarity in Proteins: A Protein Structure TheoryHost: P. SulcThis lecture will outline a simple way to understand proteins. Per-residue interaction factors will be introduced and used to describe protein structure and to understand heat sensitive mutants, protein-protein interactions (PPI), protein-small molecule interactions (PSMI), and other phenomena. The per-residue interaction factor is a function of amino acid identity, local structure, and multibody contributions; from it, folding/interaction free energy can be calculated. Despite its simplicity, the method compares remarkably well with all-atom models. Determination of these factors is no more complex than the rules to a common board game. Interaction factor heat maps are an especially convenient way to depict the stabilized core of a protein and how the core leverages exterior hot spots. We will illustrate these and other points with a variety of proteins, including general principles that emerge directly from the model. If time permits, we will examine case studies, such as the BCR-Abl kinase domain, commercial drugs that target this kinase, phosphorylation of the activation loop, the conformational switch, and mutational resistance to inhibitors that target the inactive form, since these and many other features of proteins are readily understood and rationalized by casual inspection with these factors.
May 2, 2017: Carlos Bustamante, UC BerkeleyThe Folding Cooperativity of a Protein is Controlled by the Topology of its Polypeptide ChainHost: P. SulcThe three-dimensional structures of proteins often show a modular architecture comprised of discrete structural regions or domains. Cooperative communications between these regions is important to catalysis, regulation and efficient folding; lack of coupling has been implicated in the formation of fibrils and other misfiling pathologies. How different structural regions of a protein communicate and contribute to a protein's overall energetics and folding, however, is still poorly understood. Here we use a single-molecule optical tweezers approach to indue the selective unfolding of particular regions of T4 lysozyme and monitor the effect of other regions not directly acted on by force. We investigate how the topological organization of a proven (the order of structural elements along the sequence) affects the coupling and folding cooperatively between its domains. To probe the status of the regions not directly subjected to force, we determine the free energy changes during mechanical unfolding using Crooks' fluctuation theorem. We pull on topological variants (circular per mutants) and find that the topological organization of the polypeptide chain critically determine the folding cooperativity between domains and thus what parts of the folding/unfolding landscape are explored. We speculate that proteins may have evolved to select certain topologies that increase coupling between regions to avoid areas of the landscape that lead to kinetic trapping and misfolding.
May 9, 2017: Megan King, Yale School of MedicineTBAHost: T. Shendruk
- TBAHost: T. Shendruk
May 18, 2017 (NOTE Thursday): Francois Nédélec, EMBLTBAHost: T. Shendruk
May 23, 2017: Raul Rabadan, Columbia UniversityTBAHost: T. Shendruk
(Thursday): May 25, 2017: Clifford Brangwynne, Princeton UniversityMeasuring the Intracellular Dew Point: Phase Transitions in CellsHost: P. SulcIn this talk I will discuss our work showing that phase transitions play an important role in organizing the contents of living cells. We focus on a class of membrane-less RNA and protein rich organelles, known as RNP bodies, which help control the flow of genetic information within cells. The nucleolus is one such nuclear RNP body, which is important for cell growth and size homeostasis. We've shown that a phase transition model explains many features of nucleolar assembly, and that the internal subcompartments of the nucleolus arise from multi-phase coexistence, which may have important consequences for sequential RNA processing. I will also discuss our new "Optodroplet" approach, which enables spatiotemporal control of phase transitions within living cells, allowing us to begin quantitatively mapping intracellular phase diagrams. This approach has begun to yield rich insights into the link between intracellular liquids, gels, and the onset of pathological protein aggregation.
Center for Studies in Physics and Biology
1230 York Avenue, New York, NY 10065
Phone (212) 327-8636
Fax (212) 327-8544