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.
