Seminars Fall 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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September 18, 2018: Christina Kurzthaler, University of Innsbruck
Spatiotemporal dynamics of active agents and the buckling behavior of a semiflexible polymerHost: D. ZeeviVarious challenges are faced when microorganisms or artificial self-propelled particles move autonomously in aqueous media at low Reynolds number. These active agents are intrinsically out of equilibrium and exhibit peculiar dynamical behavior due to the complex interplay of directed swimming motion and stochastic fluctuations. An intriguing feature displayed by microswimmers is the randomization of their swimming motion at large length scales due to reorientation mechanisms such as rotational diffusion or instantaneous tumbling typical of flagellated bacteria. In this talk, I will present recent theoretical advances in the analysis of the spatiotemporal dynamics of different types of active agents in terms of the experimentally measurable intermediate scattering function. Our analytical predictions fully characterize experimental observations of catalytic Janus particles, a paradigmatic class of synthetic active agents, from the smallest length scales where translational Brownian motion dominates, up to the largest ones, which probe the randomization of the swimming direction due to rotational diffusion. Moreover, we elucidate the run-and-tumble motion of E. coli bacteria in terms of a renewal theory. I will also show that our theoretical framework finds application in polymer physics and present our results on the elastic behavior of a semiflexible polymer under compression.September 25, 2018: Ophir Shalem, UPenn
CRISPR based functional genomics for studying the cell biology of neurodegenerative diseasesHost: D. ZeeviThe CRISPR associated Cas9 nuclease has emerged as an exciting new tool for genome-wide pooled forward genetic screens. I will describe our labs efforts to adapt such approaches to study protein quality control pathways and neurodegenerative disease. Specifically, the development of sensitive fluorescence readouts to follow aberrant protein processing, expression, stability and aggregation associated with many of those diseases and combining it with marker-based pooled CRISPR screens for therapeutic target discovery.October 2, 2018: Lera Boroditsky, UCSD
How the languages we speak shape the ways we thinkHost: D. ZeeviDo people who speak different languages think differently? Do languages merely express thoughts, or do they secretly shape the very thoughts we wish to express? Are some thoughts unthinkable without language? Why do we think the way we do? Why does the world appear to us the way it does? Humans communicate with one another using 7,000 or so different languages, and each language differs from the next in innumerable ways. At stake are basic questions all of us have about ourselves, human nature, and reality. I will discuss research conducted around the world and focus on how language shapes the way we think about color, space, time, causality, and agency.
This seminar will take place at the Carson Family Auditorium (CRC)October 9, 2018: Tami Lieberman, MIT - CANCELLED
De novo mutations in human microbiomesHost: D. ZeeviThere is an enormous potential for evolution within each of our microbiomes, with billions of new mutations being created each day. In this talk, I will highlight the power of tracking within-person evolution for understanding bacterial transmission, identifying genes critical to long-term survival, and for understanding evolutionary principles. I will present examples from infectious diseases, the gut microbiome, and the skin microbiome. Relevant DOIs: 10.1128/mSystems.00171-17, 10.1101/208009, 10.1038/nm.4205.October 18, 2018: Partha Mitra, CSHL
The Study of Intelligent MachinesHost: D. ZeeviThe talk will be in two parts. The first part will summarize ongoing work on generating a brain-wide map of brain circuits in the Mouse and the Marmoset at a mesoscopic scale, defined as the transitional scale from individual-variation dominated to a species-typical pattern. The second part will outline some theoretical work on machine learning, and in particular address the puzzling observation that highly overparametrized models seem to still offer good generalization performance. The possibility of a robust crosstalk between an empirical and theoretical research program on intelligent machines will be discussed.October 23, 2018: Dmitri Petrov, Stanford
Barcoding Rapid EvolutionHost: D. ZeeviAdaptation is the central evolutionary process and is at the core of some of the greatest challenges facing humanity. Infectious disease, cancer, evolution of resistance to drugs and pesticides are all problems of evolutionary adaptation. Until recently, adaptation was thought to be so idiosyncratic and infrequent that it seemed impossible to obtain enough empirical data about adaptive evolution to investigate it in a systematic way. However, we now know that adaptation is at times sufficiently rapid, recurrent, and involves mutations of such large effect that adaptive dynamics can be quantified on short time scales and with powerful statistical replication. Generally rapid adaptation is a feature of large populations, where the dynamics are not limited by the waiting time for adaptive mutations such that multiple adaptive clones increase in frequency simultaneously, making it difficult to study their individual behaviors. I will describe how we use barcoding and genetic engineering to uncover the dynamics of adaptation in such large populations using experimental evolution in large populations of yeast and in the mouse model of lung cancer.October 30, 2018: Natalie Jeremijenko, NYU Steinhardt
Doctors without Disciplinary Borders, a recruitment discussionHost: D. ZeeviNovember 13, 2018: Daniel Segré, Boston University
Ecosystem-level metabolic networksHost: D. ZeeviMetabolism, in addition to being the “engine” of every living cell, plays a major role in the cell-cell and cell-environment relations that shape the dynamics and evolution of microbial communities, e.g. by mediating competition and cross-feeding interactions between different species. Despite the increasing availability of metagenomic sequencing data for numerous microbial ecosystems, fundamental aspects of these communities, such the maintenance of diversity, the unculturability of many isolates, and the conditions necessary for taxonomic or functional stability, are still poorly understood. In our lab, we develop and test mechanistic computational models for the dynamics and evolution of interactions between different organisms based on the knowledge of their entire metabolic networks, with applications in the study of natural and synthetic microbial communities.November 27, 2018: Jan Drugowitsch, Harvard
The unreasonable effectiveness of diffusion models in decision neuroscienceHost: D. ZeeviDiffusion models, a popular family of decision-making models, are despite their simplicity able to well-describe human and animal decision-making behavior across a surprisingly wide range of different tasks. I will analyze their versatility from a normative perspective, by asking if the behavior they describe is optimal in some sense. This turns out to be the case for a wide range of different tasks that rely on perceptual evidence, and even for decisions that require subjective value judgments, justifying the benefits of acquiring such a decision strategy. Furthermore, it demonstrates that the studied tasks imposed diffusion model-like behavior that might disappear once these tasks become more complex.December 4, 2018: Grégoire Altan-Bonnet, NIH CCR
T cell decision making in the immune system: from low to high dimensionHost: D. ZeeviRecent 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 timescales (< 1 hr). Yet modeling how immune responses unfold over long timescales (>1 week) remains a challenge with fundamental and clinical applications. Here we will discuss how cell-to-cell communications via cytokine exchange constitute a solution to bridge short and long timescales, local and global responses towards achieving accurate antigen discrimination at the system level. We will present our experimental platform (using CyTOF mass cytometry and a dedicated robotic platform ) to deliver multiplexed time-resolved “high-dimensional” measurements of T cell activation. We will show how we classify complex patterns of T cell activation with machine learning algorithms (top-to-bottom approaches). In parallel, we will discuss how we use biochemical modeling (bottom-up approach) to dissect how signaling cross-talks, synergism and antagonism (between antigen and cytokine response) enable scaling, convergence and divergence of T cell activation.December 11, 2018, 2pm: Gautam Reddy Nallamalla, UCSD
Learning to soar like a bird using atmospheric thermalsHost: E. SiggiaSoaring birds often rely on ascending thermal plumes (thermals) in the atmosphere as they search for prey or migrate across large distances. How soaring birds find and navigate thermals is unknown. We used reinforcement learning to train gliders to navigate simulated convective turbulent flows. Lessons from simulations allowed us to teach a glider to navigate atmospheric thermals autonomously in the field. Gliders of two-meter wingspan were equipped with a flight controller that precisely controlled the bank angle and pitch, modulating these at intervals with the aim of gaining as much lift as possible. A navigational strategy was determined solely from the gliders’ pooled experiences collected over several days in the field. The strategy relies on methods to accurately estimate the local vertical wind accelerations and the roll-wise torques on the glider, which serve as navigational cues. We propose vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds and provide a navigational strategy applicable to autonomous soaring vehicles.January 8, 2019, 2pm: Fellow Interview - Shyr-Shea Chang, UCLA
What are the physical principles of microvascular networks?Host: E. SiggiaMicrovascular networks are complex networks containing up to tens of thousands of blood vessels in a millimeter cube. While the main function of these networks is transporting oxygen and nutrients to tissues, there are other functions important to survival, such as transport efficiency and damage resistance, and it is not clear which functions are adaptive for microvascular networks. Here we start by looking at zebrafish trunk microvascular network. The fine vessels are like rungs of a ladder with different distances from the heart, yet the blood flows through them do not exponentially decrease as predicted by simple electric circuit approximation. The red blood cell temporarily occludes the fine vessel it passes through, and we found that this occlusion is tuned for achieving uniform blood flow in fine vessels. The uniform flow is maintained at the cost of transport efficiency, suggesting that microvascular networks might not be transport efficient. To further explore this hypothesis, we implemented a gradient descent algorithm that finds optimal networks for general functions and constraints, and we compared zebrafish trunk network with uniform flow network and transport efficient network. We found that the uniform flow network quantitatively describes the real zebrafish trunk network. When transport efficiency is imposed as a constraint instead of a target function, we discovered an abrupt change in network morphology when the burden of transport cost exceeds a threshold, suggesting that biological network always conforms to the most urgent constraint on it. Finally, I will talk about our recent work on a novel vessel adaptation mechanism based on sensing the large shear stress created by red blood cells, and compare our prediction to blood flow data in zebrafish embryos of different ages.January 15, 2019, 2pm: Fellow Interview - Samhita Banavar, UCSB
Mechanical feedback coordinates yeast mating projection morphogenesisHost: E. SiggiaAll morphogenesis processes, whether at the cell scale or tissue level, require the coordination of growth and mechanics to properly shape functional structures. However, the mechanisms that coordinate these two processes in the sculpting of individual cells, and especially in walled cells, remain unknown. Using yeast mating projection growth as a model system, we show that a genetically-encoded mechanical feedback relays information about the mechanical state of the cell wall to the intracellular processes assembling it, thereby coordinating cell wall expansion and growth during cell morphogenesis. We find that mechanical feedback is essential to stabilize cell growth, but the shape and size of the cell are insensitive to the feedback and independently controlled. Beyond the role of mechanical feedback in cell viability during growth, cell wall mechanics may also inform other key cellular processes, such as cell polarization. While simulations of the cell polarization machinery can readily generate a stable polarization cap in a spherical geometry, maintenance of the polarization cap at the tip of the mating projection is not possible in realistic cell geometries. We show that the same mechanical feedback mechanism ensuring cell viability also results in the desired spatial coordination of the location of the polarization cap and the tip of the projection.January 29, 2019, 2pm: Fellow Interview - Stuart Sevier, Rice University
Mechanical Properties of Transcription and their Role in Genome Structure and MemoryHost: E. SiggiaHow is the physical state of DNA maintained so that the right genes are activated, controlled and silenced at the right time and in the right place? How do cells with the same genome di fferienate into cell types which display such di ferent behavior? How are those behaviors maintained? And how do they go wrong? Answering these questions has emerged as a grand challenge for molecular biology in the coming decade. In this talk, I will outline some of the basic physical elements of transcriptional initiation and elongation. A mathematical formulation of the 'twin domain model' of transcription will be presented with a focus on simple descriptions of each process. The resulting framework is combined with well-established stochastic processes of transcription resulting in a model which characterizes the impact of the mechanical properties of transcription on elements of gene expression and DNA structure. Importantly, this model opens a window onto the role of gene expression in shaping the storage and transfer of information through the physical and chemical state of DNA offering insight into a mechanical method of feedback for cells to regulate function.February 7, 2019: Liam Holt, NYU
Control and consequences of the physical properties of the cell interiorHost: E. SiggiaTens of thousands of biochemical reactions occur simultaneously in the cell. Small molecules are channeled through metabolic pathways at blistering speed. Giant complexes assemble to orchestrate transcription and translation. ATP fuels the active transport of organelles along microtubules, and actin networks drive membrane remodeling and agitate the cytoplasm. All of this occurs within a crowded cell interior that approaches the physical limits where jamming will occur. This extreme physical environment is both essential for life, and a potential liability. If cells become too dilute, they senesce and die. On the other hand, increased crowding eventually stalls growth. We found that the central growth regulator, mTORC1, is a crucial regulator of crowding. We also found that mechanical compression affects crowding and phase separation. We propose that perturbations to the physical properties of the cell interior through mechanical forces and the regulation of growth pathways play a crucial role in cell, developmental and disease biology, including cancer and neurodegeneration.February 14, 2018: Otto Cordero, MIT
Microbial interactions and the assembly of micro-scale communitiesHost: D. ZeeviIn this talk I will present work done in my lab showing how ecological interactions control the assembly and function of microbial communities at micro-scales. Using model marine particles composed of a variety of carbohydrate commonly found in the ocean, I will show how microbial interactions such as cross-feeding and social cheating control community dynamics, leading to rapid successions on particles. By comparing successions on different substrates we were able to cluster successional species into two types of functional groups, one type that is specific to each carbohydrate, and a type that substrate-unspecific and instead driven by crossfeeding interactions. We show that this simple logic can be exploited to predict the composition of communities on multiple carbohydrates as simple linear combinations of the compositions on single carbohydrates. Finally, I will focus on the substrate-specific group of chitin degraders to discuss ongoing work on the role of cooperative interactions, mediated by extracellular enzymes.February 19, 2019: Markita Landry, UC Berkeley
Nanomaterials Engineering to Probe and Control Living SystemsHost: J. NirodyUnique physical, chemical, and optical phenomena arise when materials are confined to the nano-scale. We are accustomed to making observations and predictions for the behavior of living systems on a macroscopic scale that is intuitive for the time and size scales of our day-to-day lives. However, the building blocks of life: proteins, nucleic acids, and cells, occupy different spatiotemporal scales. Our lab focuses on understanding and exploiting tunable optical and chemical properties of nanomaterials to access information about biological systems stored at the nano-scale. We present recent work on developing and implementing dopamine nanosensors to image dopamine volume transmission in the extracellular space of the brain striatum in both model (mouse) and non-model (bat) species. We validate our dopamine nanosensor in acute striatal slices with electrical and optogenetic stimulation of dopamine release, and show disrupted dopamine release or reuptake kinetics when brain tissue is exposed to dopamine agonist or antagonist drugs [1]. We characterize our findings in the context of their utility for high spatial and temporal neuromodulator imaging in the brain with 2-photon microscopy [2], and describe nanosensor exciton behavior from a molecular dynamics (MD) perspective [3]. We also discuss how high aspect ratio nanomaterials can be synthesized to carry biomolecular cargo to living systems. In particular, genetic engineering of plants is at the core of environmental sustainability efforts, but the physical barrier presented by the cell wall has limited the ease and throughput with which exogenous biomolecules can be delivered to plants. We will describe how nanomaterials engineering principles can be leveraged to manipulate living plants, in efforts to reconcile the benefits of crop genetic engineering with the demand for non-GMO foods [4]. Our work in the agricultural space provides a promising tool for species-independent, targeted, and passive delivery of genetic material, without transgene integration, into plant cells for rapid and parallelizable testing of plant genotype-phenotype relationships.
1. Beyene, A.B., McFarlane, I.R., Pinals, R.L, Landry, M.P.‡ Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space. ACS Chemical Neuroscience 8 (10), 2275-2289 (2017).
2. Del Bonis O’Donnell, J.T., Page, R.H., Beyene, A.G., Tindall, E.G., McFarlane, I.R., Landry, M.P.‡ Molecular Recognition of Dopamine with Dual Near Infrared Excitation-Emission Two-Photon Microscopy. Advanced Functional Materials (2017). DOI: 10.1002/adfm.201702112
3. Beyene, A.G., Delevich, K., Del Bonis-O’Donnell, J.T., Piekarski, D.J., Lin, W.C., Thomas, A.W., Yang, S.J., Kosillo, P., Yang, D., Wilbrecht, L., Landry, M.P. ‡ Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor. Nano Letters 18 (11), 6995-7003 (2018).
4. Demirer, G.S., Chang, R., Zhang, H., Chio, L., Landry, M.P.‡ Nanoparticle-Guided Biomolecule Delivery for Transgene Expression and Gene Silencing in Mature Plants. bioRxiv (2018). DOI: 10.1101/179549February 26, 2019: Eva Kanso, USC
Unity and Diversity in the biological functions of cilia-driven flowsHost: J. NirodyMotile cilia are micron-scale hair-like protrusions from epithelial cells that beat collectively to transport fluid. On the tissue level, cilia serve diverse biological functions, such as mucociliary clearance in the airways and cerebrospinal fluid transport in the brain ventricles. Yet, the relationship between the structure and organization of ciliated tissues and their biological function remains elusive. Here, I will present a series of mathematical and experimental models that examine: (1) the emergence of self-sustained oscillations in a single cilium, (2) the coordinated beating of neighboring cilia, and (3) the role of cilia-driven flows in particle transport, mixing, capture and filtering. I will conclude by commenting on the implications of these models to understanding the biophysical mechanisms underlying the interaction of ciliated tissues with microbial partners.March 5, 2019: Sean Eddy, Harvard
Peter H. Sellers Lecture
Sequence homology searches: the future of deciphering the pastCarson Family Auditorium, B-Level, Greenberg Building
Host: J. NirodyComputational recognition of distant sequence homology is a key to studying ancient events in molecular evolution. The better our sequence analysis methods are, the deeper in evolutionary time we can see. A major aim in the field is to improve the resolution of homology recognition methods by building increasingly realistic, complex, parameter-rich models. I will describe current and future research in homology search algorithms based on probabilistic inference methods, including hidden Markov models (HMMs) and stochastic context-free grammars (SCFGs). We make these methods available in the HMMER and Infernal software from my laboratory, in collaboration with sequence family databases including Pfam and Rfam.March 12, 2019: Thierry Emonet, Yale
Conflicts and synergies between phenotypic heterogeneity and collective migrationHost: J. NirodyPhenotypic diversity and collective behavior are important properties of living communities that provide selective advantages. While collective behavior requires coordination between individuals, phenotypic diversity tends to reduce coordination. I will report on our recent experimental and theoretical results that used bacterial chemotaxis as model system to examine how phenotypic heterogeneity modulates group performance and how collective behavior affects the amount of diversity in the group. This study was supported by the National Institutes of Health grant R01GM106189, the Allen Distinguished Investigator Program (grant 11562) through The Paul G. Allen Frontiers Group, and the James S. McDonnell Foundation grant on Complexity.March 19, 2019: Richard Bonneau, NYU
Inference of regulatory networks from single-cell and spatial transcriptomics data: new methods and a new benchmark dataset.Host: J. NirodyI will briefly describe scalable models for regulatory network inference that enable multiple study and multiple cell-type comparisons, include some of the key biophysics of transcriptional dynamics, and can be adapted to the latest single-cell and spatial genomics technologies. I will focus on new developments to enable learning networks from single cell and spatial transcriptomics. A key challenge in single-cell and spatial transcriptomics data is that many heterogenous cell types are measured in each experiment; to address this heterogeneity we adapt multi-task approaches to network inference to the context of single cell data. Another key challenge with spatial and single-cell based network inference is the lack of appropriate benchmarks. To address this lack of a suitable benchmark dataset, we (with David Gresham lab) have generated a new single-cell RNA sequencing data set for the model species Saccharomyces cerevisiae (there are many thousands of well characterized regulatory and signaling interactions for this well studied model system), which contains 40,000 individual cells (cells grown in 11 different environmental growth conditions and on multiple genetic backgrounds; TF knockouts). These new scRNA-seq datasets are integrated with ATAC-seq and other genomic resources to adapt and benchmark our network inference tools. We will also provide this dataset to the computational community for tool development any day now. These methods are currently being applied to work on the mammalian immune system, cancer, mouse intercortical neuron development and the spatiotemporal genomics of human and mouse spinal column.March 28, 2019: Thomas Fai, Brandeis
Length regulation of multiple flagella that self-assemble from a shared pool of componentsHost: J. NirodyThe single cell biflagellate Chlamydomonas reinhardtii has proven to be a very useful model organism for studies of size control. The lengths of its two flagella are tightly regulated. We study a model of flagellar length control whose key assumption is that proteins responsible for the intraflagellar transport (IFT) of tubulin are present in limiting amounts. In the case of two simultaneously assembling flagella, regardless of the details of how the flagella are coupled, we find that the widely-used assumption of a constant disassembly rate is inconsistent with experimental results. We therefore propose a model in which diffusion gives rise to a length-dependent concentration of depolymerizer at the flagellar tip. This model is found to be consistent with experimental results and generalizes to other situations such as arbitrary flagellar number.April 2, 2019: William Ryu, University of Toronto
The Physics of Behavior: measuring and modeling the sensorimotor response of C. elegans.Host: J. NirodyOne of the grand goals in science is to understand how neural, genetic, and biochemical circuits produce behavior. While a great deal of work has been done in the development of tools to perturb and measure the circuits underlying sensory behavior, advances in the study of behavior itself has lagged behind. For this talk I will describe some attempts to close this gap with focus on C. elegans locomotion and its response to thermal stimuli. The roundworm C. elegans is a simple organism with only 300 neurons but it can generate complex adaptive behavioral responses to a wide range of sensations including taste, touch, and temperature. C. elegans behavior consists of a number of stereotyped locomotory states such as forward and reverse, pausing, turning, etc. Typically, the worm moves by making stochastic transitions between these states, and in response to sensory measurements it adapts by biasing the probability of these transitions. However, under certain conditions the worm will transition from stochastic to deterministic behavior such as in response to sensory gradients or noxious or “painful” stimuli. We have developed simple desktop experiments to quantitatively capture the behavioral responses of C. elegans to precise thermal stimuli. Using these data we have shown that C. elegans moves through a “shape space” that is low dimensional in which four dimensions capture approximately 95% of the variance in body shape. Here I will give two examples of modeling that take advantage of this low dimensionality and stereotypy. In the first we show that stochastic dynamics within this shape space predicts transitions between attractors corresponding to abrupt reversals in crawling direction. With no free parameters, our inferred stochastic dynamical system generates reversal timescales and stereotyped trajectories in close agreement with experimental observations. In the second we use Sir Isaac, an algorithm that allows inference of the dynamical equations underlying a noisy time series, even if the dynamics are nonlinear—to analyze the thermal “pain” response of C. elegans. Both examples show that it is possible to learn “equations of behavior” of the worm, and that these equations give an interpretable, complementary perspective to traditional biological studies.April 9, 2019: Padmini Rangamani, UCSD
Geometric principles of spatio-temporal dynamics of second messengers in dendritic spinesHost: J. NirodyThe ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic structures, changing in size, shape, and number during development and aging. While spines and synapses have inspired neuromorphic engineering, the biophysical events underlying synaptic and structural plasticity remain poorly understood. Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss two recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and second, a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.April 16, 2019: Kenny Breuer, Brown
Bacterial Swimming in Viscous and Viscoelastic FluidsHost: J. NirodyThere has long been controversy and conflicting data regarding the speed at which flagellated bacteria swim in fluids of different Newtonian and non-Newtonian properties. Some data indicates faster swimming in viscoelastic media, while others suggest slower swimming. There is even controversy regarding the swimming speed in purely Newtonian media of different viscosities. We will present data and analysis from recent experiments in which we follow smooth-swimming and wild-type E coli using a tracking microscope as they move through different Newtonian and non-Newtonian fluids. We find that increasing the bulk viscosity affects swimming speed in two ways - it reduces the effectiveness of flagellar propulsion, but also increases the flagellar bundling time of wild type E coli. Cells are found to swim faster in non-Newtonian fluids, even though the bulk viscosity rises. The dominate contribution is found to be related to the strong shear thinning property of the fluid, although the normal stresses associated with viscoelasticity can also contribute through reduced cell precession, slightly improved propulsive efficacy and faster flagellar bundling times.April 23, 2019: Lars Dietrich, Columbia
The interplay of metabolism and structure in microbial biofilmsHost: J. NirodyStudies of signaling cascades can reveal important mechanisms driving multicellular development, but the models that emerge often lack critical links to environmental cues and metabolites. We study the effects of extra- and intracellular chemistry on biofilm morphogenesis in the pathogenic bacterium Pseudomonas aeruginosa, which produces oxidizing pigments called phenazines. While wild-type colonies are relatively smooth, phenazine-null mutant colonies are wrinkled. Initiation of wrinkling coincides with a maximally reduced intracellular redox state, suggesting that wrinkling is a mechanism for coping with electron acceptor limitation. Mutational analyses and in situ expression profiling have revealed roles for PAS-domain and other redox-sensing regulatory proteins, as well as genes involved in motility and matrix production, in colony morphogenesis. To characterize endogenous electron acceptor production, we have developed a novel chip that serves as a growth support for biofilms and allows electrochemical detection and spatiotemporal resolution of phenazine production in situ. Through these diverse approaches, we are developing a broad picture of the mechanisms and metabolites that exert an integrated influence over redox homeostasis in P. aeruginosa biofilmsApril 24, 2019, 2-3:30p: Two speakers
Host: A. LibchaberVincent Noireaux, University of Minnesota
Cell-free expression systems: from the genetic code to synthetic cellsCell-free expression has crossed the ages as a tool to address fundamental questions in biology and as an experimental platform to prototype biochemical systems. In the last decade, a new post-genomic generation of cell-free expression systems has emerged. In vitro protein synthesis has become powerful, versatile and scalable. Larger and larger DNA programs can be executed in vitro to construct living systems from scratch. Cell-free expression has also been devised to expand the capabilities of natural cells. In this talk, I will present the current field of cell-free constructive biology and show several examples of current capabilities, including prototyping CRISPR technologies and assembling synthetic cells.Yusuke Maeda, University of Tokyo
On-chip membrane-bound TXTL as minimal cellsArtificial cells made of molecular components and lipid membrane are emerging platforms to characterize, by construction, the properties of living systems. Cell-free transcription-translation (TXTL) system offers several advantages for the bottom-up synthesis of cellular reactors. Yet, scaling up their design within well-defined geometries remains challenging. We present a microfluidic device hosting TXTL reactions of a single reporter gene in thousands of microwells separated from an external buffer by a phospholipid membrane. The yield of the protein synthesis in sealed micro-reactors exhibits a bimodal distribution of active and inactive states, in spite of the absence of transcriptional feedback regulation. Adding fatty acid and polymer in buffer increases the membrane stability along with the proportion of active micro-reactors. Protein synthesis is greater in micro-reactors with stable membranes. This indicates that boundary plays regulatory role on cell-free TXTL systems as a part of minimal artificial cells.May 7, 2019: Liana Lareau, UC Berkeley
Ribosome dynamics captured by deep sequencing and deep learningHost: J. NirodySynonymous codon choice can have dramatic effects on ribosome speed, RNA stability, and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs, exposing a need for models of translation that capture the full range of empirically observed variation. Previously, we showed that deep sequencing of ribosome-protected mRNA fragments reveals not only the position of each ribosome but also, unexpectedly, its particular stage of the elongation cycle. This provides a new way to study the detailed kinetics of translation and a new probe with which to identify sequence elements that affect each step in the elongation cycle. More recently, we modeled variation in translation elongation using a feedforward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. We applied our model to design synonymous variants of a fluorescent protein in yeast and concluded that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output.May 14, 2019: Robert Endres, Imperial College, London
Chemotaxis: linking cell shape, behavior, and strategyHost: J. NirodyThe behavior of an organism often reflects a strategy for coping with its environment. Such behavior in higher organisms can often be reduced to a few stereotyped modes of movement due to physiological limitations, but finding such modes in amoeboid cells is more difficult as they lack these constraints. In this talk, I will examine cell shape and movement in starved Dictyostelium amoebae during single-cell and collective cell migration. I will show that the incredible variety in amoeboid shape across a population can be reduced to a few modes of variation, and that different modes are used depending on the steepness of the applied chemical gradient or drug treatment. The origins of cell shape and behavior are tackled by both forward and inverse modeling, predicting long-term cell behavior without reference to biochemical details. Analogies with statistical mechanics are made throughout the talk.May 21, 2019: Christian Machens, Champalimaud Foundation
An alternative view of what neural circuits may be doingHost: J. NirodyThe classical picture of a neural network assumes that each neuron sums its inputs, followed by a nonlinear activation function. Interesting computations emerge by combining and wiring up these individual units. While immensely successful (both in neuroscience and AI), this view has also created several persistent puzzles about the organization of neural systems. One puzzle are spikes, which have largely remained a nuisance, rather than a feature of neural systems. Another puzzle is robustness to perturbations, which is ubiquitous in biology, but largely ignored in neural network modeling. I will show that these puzzles can be resolved if we shift our perspective of how neural systems operate. Based on only two assumptions - that the effective output of a neural network can be extracted via linear readouts, and that each neuron only fires to bound an error on this output, I will show how to derive a spiking network of integrate-and-fire neurons that exhibits irregular and asynchronous spike trains, balance of excitatory and inhibitory currents, and robustness to perturbations. I will provide geometric intuitions for the network's functionality, and use these insights to show how these networks can control the precision of their output via bottom-up and top-down gain modulation, even though they consist of integrate-and-fire neurons only.
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