Spatiotemporal dynamics of active agents and the buckling behavior of a semiflexible polymer
Host: D. Zeevi
Various 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.
CRISPR based functional genomics for studying the cell biology of neurodegenerative diseases
Host: D. Zeevi
The 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.
How the languages we speak shape the ways we think
Host: D. Zeevi
Do 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)
There 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.
The 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.
Adaptation 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.
Metabolism, 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.
The unreasonable effectiveness of diffusion models in decision neuroscience
Host: D. Zeevi
Diffusion 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.
T cell decision making in the immune system: from low to high dimension
Host: D. Zeevi
Recent progress in systems immunology have ushered a quantitative understanding of T cells’ ability to discriminate antigens (e.g. self vs non-self) on a short 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 thermals
Host: E. Siggia
Soaring 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.
What are the physical principles of microvascular networks?
Host: E. Siggia
Microvascular 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.
All 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.
Mechanical Properties of Transcription and their Role in Genome Structure and Memory
Host: E. Siggia
How 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.
Nanomaterials Engineering to Probe and Control Living Systems
Host: J. Nirody
Unique 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: 10.1101/179549
