Seminars 2018
Seminars are on Tuesdays at 4 pm in Smith Hall Annex, A-Level, Physics Seminar Room, preceded by tea at 3:45, unless otherwise noted.
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January 9, 2018: Dan Yamins, Stanford University
Using artificial-intelligence-driven deep neural networks to uncover principles of brain representation and organizationHost: C. KirstHuman behavior is founded on the ability to identify meaningful entities in complex noisy data streams that constantly bombard the senses. For example, in vision, retinal input is transformed into rich object-based scenes; in audition, sound waves are transformed into words and sentences. In this talk, I will describe my work using computational models to help uncover how sensory cortex accomplishes these enormous computational feats. The core observation underlying my work is that optimizing neural networks to solve challenging real-world artificial intelligence (AI) tasks can yield predictive models of the cortical neurons that support these tasks. I will first describe how we leveraged recent advances in AI to train a neural network that approaches human-level performance on a challenging visual object recognition task. Critically, even though this network was not explicitly fit to neural data, it is nonetheless predictive of neural response patterns of neurons in multiple areas of the visual pathway, including higher cortical areas that have long resisted modeling attempts. Intriguingly, an analogous approach turns out be helpful for studying audition, where we recently found that neural networks optimized for word recognition and speaker identification tasks naturally predict responses in human auditory cortex to a wide spectrum of natural sound stimuli, and help differentiate poorly understood non-primary auditory cortical regions. Together, these findings suggest the beginnings of a general approach to understanding sensory processing the brain. I'll give an overview of these results, explain how they fit into the historical trajectory of AI and computational neuroscience, and discuss future questions of great interest that may benefit from a similar approach.January 16, 2018: Michael Murrell, Yale
From active liquid crystals to de-wetting liquid droplets: Using mesoscopic models to understand the physical behaviors of cells and tissuesHost: T. ShendrukLiving cells generate and transmit mechanical forces over diverse time-scales and length-scales to determine the dynamics of cell and tissue shape during both homeostatic and pathological processes, from early embryonic development to cancer metastasis. These forces arise from the cell cytoskeleton, a scaffolding network of entangled protein polymers driven out-of-equilibrium by enzymes that convert chemical energy into mechanical work. However, how molecular interactions within the cytoskeleton lead to the accumulation of mechanical stresses that determine the dynamics of cell shape is unknown. Furthermore, how cellular interactions are subsequently modulated to determine the shape of the tissue is also unclear. To bridge these scales, our group in collaboration with others, uses a combination of experimental, computational and theoretical approaches. On the molecular scale, we use active nematic liquid crystals as a framework to understand how mechanical stresses are produced and transmitted within the cell cytoskeleton. On the scale of cells and tissues, we abstract these stresses to surface tension in a liquid droplet and draw analogies between the dynamics of droplet wetting (and dewetting) and the shape dynamics of cells and simple tissues. Together, we attempt to develop comprehensive description for how cytoskeletal stresses translate to the physical behaviors of cells and tissues with significant phenotypic outcomes such as cancer metastasis.January 23, 2018: Vladimir Itskov, Pennsylvania State University
Inferring a sensory space from neural responsesHost: T. ShendrukThe brain builds its internal sensory representations based on the structure of neural activity. A number of modalities, such as the early visual system and the spatial map in hippocampus, are relatively well-characterized. However some sensory systems, such as olfaction, remain enigmatic. Perhaps the primary difficulty is that the underlying perceptual space is not well-understood. Can we "build" the sensory space from neural activity alone, without a prior understanding of how the stimuli are organized? I will describe a set of mathematical tools that allow to infer the dimension and the topology of stimulus space and illustrate their utility for two neural systems: hippocampus and early olfaction.January 30, 2018: Michael D Graham, University of Wisconsin-Madison
Theory of margination in blood and other multicomponent suspensionsHost: T. ShendrukBlood is a suspension of objects of various shapes, sizes and mechanical properties, whose distribution during flow is important in many contexts. Red blood cells tend to migrate toward the center of a blood vessel, leaving a cell-free layer at the vessel wall, while white blood cells and platelets are preferentially found near the walls, a phenomenon called margination that is critical for the physiological responses of inflammation and hemostasis. Additionally, drug delivery particles in the bloodstream also undergo margination – the influence of these phenomena on the efficacy of such particles is unknown.
In this talk a mechanistic theory is developed to describe segregation in blood and other confined multicomponent suspensions. It incorporates the two key phenomena arising in these systems at low Reynolds number: hydrodynamic pair collisions and wall-induced migration. The theory predicts that the cell-free layer thickness follows a master curve relating it in a specific way to confinement ratio and volume fraction. Results from experiments and detailed simulations with different parameters (flexibility of different components in the suspension, viscosity ratio, confinement, among others) collapse onto the same curve. In simple shear flow, several regimes of segregation arise, depending on the value of a ``margination parameter'' M. Most importantly, there is a critical value of M below which a sharp ``drainage transition'' occurs: one component is completely depleted from the bulk flow to the vicinity of the walls. Direct simulations also exhibit this transition as the size or flexibility ratio of the components changes. Results are presented for both Couette and plane Poiseuille flow. Experiments performed in the laboratory of Wilbur Lam indicate the physiological and clinical importance of these observations.February 1, 2018 (2 PM) Archishman Raju, Cornell University
Information geometry and the renormalization groupHost: E. SiggiaMicroscopically diverse systems often yield to surprisingly simple effective theories. The renormalization group (RG) describes how system parameters change as the scale of observation grows and gives a precise explanation for this emergent simplicity in physics. We use information geometry to reformulate the RG as a statement about how the distinguishability of microscopic parameters depends on the scale of observation. We show that information about relevant parameters is preserved, with distances along relevant directions maintained under flow. By contrast, irrelevant parameters become less distinguishable under the flow, with distances along irrelevant directions contracting. We apply our tools to understand the emergence of the diffusion equation and more general statistical systems described by a free energy. This suggests a way to identify relevant directions for more general coarsening procedures.February 6, 2018 (2 PM) Shou-Wen Wang, Princeton University
Adaptation unifies emergent oscillations in quorum sensing populationsHost: E. SiggiaAdaptation is a ubiquitous trait of living organisms in dealing with the outside world. At the cellular level, high sensitivity over a broad range of environmental conditions is achieved via biomolecular circuits that operate out of thermal equilibrium. Here we report an unexpected link between sensory adaptation and auto-induced collective oscillations in dense cell populations. We uncover a frequency regime where adaptive cells amplify temporal variations of the extracellular signal. Deeply rooted in nonequilibrium thermodynamics, the design principle unites several known examples of dynamical quorum sensing, and provides a new perspective on regulatory mechanisms behind glycolytic oscillations in yeast cell suspensions.February 13, 2018
TBAHost: T. ShendrukFebruary 20, 2018: (2 PM) Alexander Mietke, MPI Physics of Complex Systems
Self-organization of curved and deforming active surfacesHost: E. SiggiaMechano-chemical processes in thin biological structures, such as the cellular cortex or epithelial sheets, play a key role during the morphogenesis of cells and tissues. Emergent dynamics in these processes can arise from a feedback loop in which active mechanical forces, by inducing material flows, indirectly affect their own chemical regulation. It has been demonstrated in simple, fixed geometries that this mechanism enables self-organized patterning, but the interplay of mechano-chemical processes with complex surface geometries and shape changes of the material remains to be explored. In this work, we employ the theory of active surfaces and develop analytical and numerical tools to study these materials in curved and dynamically evolving geometries. Additionally, diffusive and advective transport processes can redistribute molecules responsible for local stress generation within the surface, which resembles the interplay between active forces, the shape changes they imply and the effects this has on their regulation. Within this framework, cell polarization, contractile ring formation before cytokinesis, as well as pulsatile dynamics in active tubular structures can be understood as natural emergent phenomena. Our approach provides novel opportunities to explore different scenarios of mechano-chemical self-organization and can help understand the role of shape as an effective regulatory element in morphogenetic processes.February 27, 2018: Nikolaus Kriegeskorte, Columbia University
Building deep neural network models to understand biological visionHost: C. KirstRecent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. This brain-inspired technology provides the basis for tomorrow’s computational neuroscience [1]. Deep convolutional neural nets trained for visual object recognition have internal representational spaces remarkably similar to those of the human and monkey ventral visual pathway [2]. Functional imaging and invasive neuronal recording provide increasingly rich measurements of brain activity in humans and animals, but a challenge is to leverage such data to gain insight into the brain’s computational mechanisms [3, 4].
In my lab, we build neural network models of primate vision, inspired by biology and guided by engineering considerations [5]. We also develop statistical inference techniques that enable us to adjudicate between complex brain-computational models on the basis of brain and behavioral data [3, 4]. I will discuss recent work extending deep convolutional feedforward vision models by adding recurrent signal flow and stochasticity, two characteristics of biological neural networks. This improves inferential performance and enables neural networks to more accurately represent their own uncertainty.
[1] Kriegeskorte N (2015) Annu. Rev. Vis. Sci. 2015. 1:417-46.
[2] Khaligh-Razavi SM, N Kriegeskorte (2014) PLoS Computational Biology
[3] Diedrichsen J, Kriegeskorte N (2017) PLoS Computational Biology
[4] Kriegeskorte N, Diedrichsen (2016) J Phil. Trans. R. Soc. B.
[5] Spoerer CJ, McClure P, Kriegeskorte N (2017) Frontiers.March 6, 2018
TBAHost: D. ZeeviMarch 13, 2018: Maxime Deforet, Laboratoire Jean Perrin UPMC
To move, or not to move, that is the evolutionary question: Evolution of growth and dispersal in bacterial populationsHost: T. ShendrukI will present two projects demonstrating the interplay between growth and dispersal in evolutionary contexts. In the first one, I focus on the evolution of expanding populations, such as bacterial colonies or cancerous tumors. Population expansion is driven by growth and dispersal. The evolutionary fate of a mutant also depends on both growth and dispersal. For instance, when could moving faster and growing slower be a favorable strategy? Starting with the Fisher-KPP equation, I come up with a quantitative rule of invasion, which depends on growth and dispersal rates of the ancestor and the mutant. Theoretical findings are supported by experiments with bacterial swarming colonies, as well as data from ecology literature. In the second project, I address the question of design optimization of a biochemical network (the C-di-GMP network in bacteria) that integrates signals from the environment and regulates genes for growth and dispersal. This network needs to be trained to provide the optimal outcome when the environment alternatively favors growth or dispersal. I show that natural selection in this network architecture is mathematically equivalent to training a machine learning model to solve a classification problem.March 20, 2018: Madhav Mani, Northwestern University
Drosophila eye development: A physical viewHost: T. ShendrukDevelopmental biology is at a special point in its history: 1) Model organisms have been extensively characterized, 2) the molecular parts list has largely been enumerated, and 3) live-imaging and sequencing tools have matured. What remains is to understand how the system works. I will present work in progress in collaboration with Richard Carthew that focuses on a diverse set of phenomena made manifest during Drosophila eye development. Incomplete results related to collective cell-fate determination, collective polarization, and mechanics will be presented. An emphasis will be placed on the importance of taking a dynamical view of development, and mathematical modeling as a way to synthesize disparate observations, guide experimentation, and make predictions.March 27, 2018: Sharon Swartz, Brown University
Evolution and motor control in bat flight - A wing and a prayer?Host: T. ShendrukBat wings evolved from grasping, manipulating mammalian hands, and this origin influences the biomechanics of flight in bats in comparison to flight in birds and insects. Therefore, an evolutionary perspective is critical to advancing the comparative biology of flight, and helps distinguish those aspects of flight that are shared in all flying animals and those features that are unique to bats. Low weight, particularly in the wings, is important for all flying animals, but selection for reduced wing mass in bats must interact with aspects of neural control in the most morphologically complex of animal wings. In addition, the nature of wing skin as a complex functional material and the capacity to modulate wing mechanical properties during flight by an unusual group of muscles found only in bats proves critical to bat flight performance. Improved understanding of the functional architecture of bat wings not only provides insight into steady-state flight behaviors, but also holds promise for solving problems concerning bats’ abilities to recover from perturbations, fly effectively even following wing damage or injury, etc. This approach requires sophisticated bioengineering techniques such as particle image velocimetry, multi-camera high speed videography, and dynamic modeling, but also low-tech methods including polarized light photography, histology, and anatomical description.April 3, 2018: Lakshminarayanan Mahadevan, Harvard
Wisdom of hives and mounds: collective problem solving by super-organismsHost: T. ShendrukSocial insects are capable of solving complex physiological problems using collective strategies. I will discuss our work on some of these problems that include the physiology and morphogenesis of termite mounds, and active mechanisms for ventilation, mechanical adaptation and thermoregulation in bee aggregates.April 10, 2018: Julie Theriot, Stanford
The Fast and the Furious: Mechanics and dynamics of rapid cell motilityHost: T. ShendrukDirected crawling motility of animal cell types ranging from neurons to macrophages requires the coordinated force-generating activity of multiple mechanical elements. Much molecular detail is now known about the constituents of some mechanical submachines such as the polymerizing actin network and the adhesion complexes, but it is not yet clear how these elements all work together to generate coherent, directed motion at the level of the whole cell. In order to understand cellular mechanisms of large-scale coordination, our work focuses on two extremely fast-moving cell types, the fish epidermal basal keratocyte responsible for the rapid closure of wounds in fish skin, and the human neutrophil that hunts down and kills microbial invaders. Despite their very different biological roles and apparent behaviors, these cell share fundamental biophysical mechanisms of self-organization and movement coordination.April 17, 2018: Ehud Meron, Ben-Gurion University
From patterns to function in dryland ecosystemsHost: T. ShendrukDryland landscapes show a variety of vegetation pattern-formation phenomena, two striking examples of which are banded vegetation on hill slopes and nearly hexagonal patterns of bare-soil gaps in grasslands (“fairy circles”). Vegetation pattern formation is a population-level mechanism to cope with water stress, that is coupled to other response mechanisms operating at lower and higher organization levels, such as phenotypic changes at the organism level and biodiversity changes at the community level. Uncovering the roles that vegetation pattern formation plays in the functioning of dryland ecosystems is a challenging problem of particular significance in the current era of climate change and massive human intervention in natural ecosystems. In this talk I will present a platform of mathematical models for dryland ecosystems and use it to study (i) mechanisms of vegetation pattern formation, (ii) the variety of extended and localized patterns that can appear along the rainfall gradient, (iii) the impact of pattern formation on ecosystem response to droughts, and (iv) forms of high-integrity human intervention that do not impair ecosystem function. Universal aspects that might be applicable to other living systems will be emphasized. >April 24, 2018: Leslie Greengard, Flatiron Institute and Courant Institute
Second Annual Peter H. Sellers Lecture
Carson Family Auditorium, CRC, B-Level
The mathematics of biomedical and biophysical imagingHosts: M. Magnasco and S. StricklandOver the last few decades, new mathematical techniques have played an important role in a variety of biomedical and biophysical imaging modalities. Following a brief survey of the field, we will focus on recent progress in nonlinear optimization that is bringing large-scale problems in acoustic scattering and cryo-electron microscopy within practical reach.May 1, 2018: Allon Klein, Harvard
Tracing lineage and cell differentiation at single cell resolutionHost: T. ShendrukI will present our efforts to map cellular differentiation hierarchies in zebrafish and xenopus embryos, with further examples in mammalian tissues. A few years ago we (and others) developed droplet-microfluidic technology for single cell RNA-Seq (inDrop), opening up the possibility to infer cell states in an unbiased manner. We recently carried out single-cell RNA sequencing of >200,000 cells from time series of the first 24 hours of life, in two vertebrate species: zebrafish (Danio rerio) and the frog (Xenopus tropicalis). We reconstructed the first tens of cell fate choices in the embryo from axis patterning, germ layer formation, and early organogenesis, and compared the two organisms in their differentiation dynamics. We tested how clonally-related cells traverse these fate choices by developing a transposon-based barcoding approach (“TracerSeq”) for reconstructing single-cell lineage histories. Through different examples, I will try to show some of the challenges and opportunities of single cell RNA-Seq approaches: finding and validating new cell types, identifying new growth factor regulators of fate choice, clarifying points of fate commitment of tissues, but also showing where single cell RNA-Seq data can be misleading.May 3, 2018: Paul François, McGill
Untangling the biological hairball: Network evolution and fitness based reductionHost: E. SiggiaComplex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal understanding of their behavior. I will introduce several models of immune recognition by T cells and will show how a simple procedure can be used to reduce them to functional submodules, using statistical mechanics of complex systems combined with a fitness-based approach inspired by in silico evolution. Our procedure works by putting parameters or combination of parameters to some asymptotic limit, while keeping (or slightly improving) the model performance, and requires parameter symmetry breaking for more complex models. An intractable model of immune recognition with close to a hundred individual transition rates is reduced to a simple two-parameter model, and connected to the ``adaptive sorting" principle that we previously identified and experimentally validated. Our procedure extracts three different mechanisms for early immune recognition, and automatically discovers similar functional modules in different models of the same process allowing for model classification and comparison.May 8, 2018: Jeffrey Guasto, Tufts University
Transport, topology, and taxis: Bacterial motility in porous media flowsHost: T. ShendrukSwimming cells, including bacteria, sperm, and plankton, play integral roles in processes ranging from human reproduction to ecosystem dynamics to bioremediation in soil. In particular, swimming cells ubiquitously live in dynamic fluid environments that are characterized by complex porous microstructure. In this talk, we will examine the physical mechanisms underlying the transport of motile bacteria in a model microfluidic porous medium, which is used to precisely control both microstructure and flow. We show that such confined flows generate striking heterogeneity in the spatial distribution of motile bacteria due to interactions between cell shape and fluid flow. Unlike passive Brownian particles, the effective diffusion of actively swimming ‘run-and-tumble’ bacteria is significantly hindered in flow. In the case of magnetotactic bacteria - which use Earth’s magnetic field for navigation - we demonstrate that porous media flows can entirely halt their directed motility, locally trapping cells in the porous microstructure. This work illustrates how the physical environment impacts fundamental survival strategies of swimming cells and carries potential implications for biofilm formation and resource competition biomes.May 15, 2018: Martin Jonikas, Princeton
Systems and Synthetic Biology of Photosynthetic OrganismsHost: D. ZeeviApproximately one-third of global carbon-fixation occurs in an overlooked algal organelle called the pyrenoid. The pyrenoid contains the CO2-fixing enzyme Rubisco, and enhances carbon-fixation by supplying Rubisco with a high concentration of CO2. Since the discovery of the pyrenoid over 130 years ago, the molecular structure and biogenesis of this ecologically fundamental organelle have remained enigmatic. To advance our understanding of the pyrenoid and of photosynthetic organisms more broadly, we have developed new tools for the unicellular model alga Chlamydomonas reinhardtii. These tools include the world’s first genome-wide collection of mapped mutants in any single-celled photosynthetic organism, as well as methods for high-throughput localization of proteins and identification of protein-protein interactions. By applying these tools, we have increased the number of known pyrenoid components from 6 to over 80, and discovered the existence of three new protein layers in the pyrenoid- a plate-like layer, a mesh layer, and a punctate layer. We discovered that an abundant pyrenoid protein, Essential Pyrenoid Component 1 (EPYC1), works as a molecular glue that binds Rubisco holoenzymes together to form the matrix at the core of the pyrenoid. Furthermore, contrary to longstanding belief that the pyrenoid matrix is a solid structure, we have discovered that the matrix behaves as a liquid droplet, which mixes internally, divides by fission, and dissolves and condenses during the cell cycle. Our efforts have provided fundamental insights into pyrenoid protein composition, structural organization and biogenesis. Working with our collaborators in the Combining Algal and Plant Photosynthesis project, we aim to transfer algal pyrenoid components into higher plants to enhance carbon fixation and yields in crops.May 22, 2018: Alexandra Zidovska, CSMR NYU
The "self-stirred" genome: Bulk and surface dynamics of the chromatin globuleHost: T. ShendrukChromatin structure and dynamics control all aspects of DNA biology yet are poorly understood. In interphase, time between two cell divisions, chromatin fills the cell nucleus in its minimally condensed polymeric state. Chromatin serves as substrate to a number of biological processes, e.g. gene expression and DNA replication, which require it to become locally restructured. These are energy-consuming processes giving rise to non-equilibrium dynamics. Chromatin dynamics has been traditionally studied by imaging of fluorescently labeled nuclear proteins and single DNA-sites, thus focusing only on a small number of tracer particles. Recently, we developed an approach, displacement correlation spectroscopy (DCS) based on time-resolved image correlation analysis, to map chromatin dynamics simultaneously across the whole nucleus in cultured human cells [1]. DCS revealed that chromatin movement was coherent across large regions (4–5μm) for several seconds. Regions of coherent motion extended beyond the boundaries of single-chromosome territories, suggesting elastic coupling of motion over length scales much larger than those of genes [1]. These large-scale, coupled motions were ATP-dependent and unidirectional for several seconds. Following these observations, we developed a hydrodynamic theory [2] as well as a microscopic model [3] of active chromatin dynamics. In this work we investigate the chromatin interactions with the nuclear envelope and compare the surface dynamics of the chromatin globule with its bulk dynamics [4].
[1] Zidovska A, Weitz DA, Mitchison TJ, PNAS, 110 (39), 15555-15560, 2013
[2] Bruinsma R, Grosberg AY, Rabin Y, Zidovska A, Biophys. J., 106 (9), 1871-1881, 2014
[3] Saintillan D, Shelley MJ, Zidovska A, https://www.biorxiv.org/content/early/2018/05/10/319756, 2018
[4] Chu F, Haley SC, Zidovska A, PNAS, 114 (39), 10338-10343, 2017June 26, 2018: Mladen Barbic, HHMI
Possible Magneto-Thermal and Magneto-Mechanical Mechanisms of Ion Channel Activation in Magneto-GeneticsHosts: A. Vaziri and J. FriedmanThe palette of tools for stimulation and regulation of neural activity is continually expanding. One of the new methods being introduced is Magneto-Genetics, where thermo-sensitive and mechano-sensitive ion channels are genetically engineered to be closely coupled to the iron-storage protein ferritin. Such genetic constructs provide a powerful new way of non-invasively activating ion channels in-vivo using external magnetic fields that easily penetrate biological tissue. Initial reports that introduced this new technology have sparked a vigorous debate on the plausibility of physical mechanisms of ion channel activation by means of external magnetic fields. I will argue that the initial criticisms leveled against Magneto-Genetics as being physically implausible were based on the overly simplistic and unnecessarily pessimistic assumptions about the magnetic spin configurations of iron in ferritin protein, and did not comprehensively consider all possible magnetic-field-based mechanisms of ion channel activation in Magneto-Genetics. I will present and propose several new magneto-thermal and magneto-mechanical mechanisms of ion channel activation by iron-loaded ferritin protein, and suggest experiments on a single iron particle-ferritin protein level that may elucidate and clarify some of the mysteries that presently challenge our understanding of the reported biological experiments.
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