Physiology or Medicine
Victor Ambros and Gary Ruvkun discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery in the small worm C. elegans revealed a completely new principle of gene regulation. This turned out to be essential for multicellular organisms, including humans. MicroRNAs are proving to be fundamentally important for how organisms develop and function.
Physics
John Hopfield introduced a spin model that can store and reconstruct information. Geoffrey Hinton built on Hopfield’s idea to invent the Boltzmann Machine, that is able to learn from examples to reconstruct a set of desired patterns. He also popularized and improved Backpropagation of Errors, a method actually used in today’s advanced AI technology (e.g. Deep Learning).
Chemistry
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.
Seminars
Date:
17
November, 2024
Sunday
Hour: 13:15-14:30
The Clore Center for Biological Physics
Yet Another Approach to Loschmidt's Paradox
Dr. Lev Melnikovsky | Department of Molecular Chemistry and Materials Science
Protein function is the combined product of chemical and mechanical interactions encoded in the gene. Thus, the function of enzymes relies on finetuning the chemical groups at the active site, but also on large-scale mechanical motions, allowing enzymes to bind to substrates selectively, reach the transition state, and release products. We will discuss recent work aiming to probe directly the linkage between these collective internal motions and the functionality of enzymes, using nano-rheological measurements, AI-prediction of point mutation effects, and physical theory. This work proposes a physical view of enzymes as viscoelastic catalytic machines with sequence-encoded mechanical specifications, which are modulated via long-ranged force transduction.
In this two-part talk, I will try to cover two separate lines of research: Machine learning of antibiotic resistance and AI-driven Science. In the first half, I will describe our efforts to understand and predict antibiotic resistance at the single patient level. I will describe a series of experimental-computational methodologies for following and identifying recurrent patterns in the evolution of antibiotic resistance in the lab and in the clinic. Combined with machine-learning approaches applied to electronic patient records, these tools lead to predictive diagnostics of antibiotic resistance and algorithms for personalized treatments of microbial infections. In the second part of the talk, we will shift gear and talk about AI-driven science. I will describe and demo “data-to-paper”: a platform that autonomously guides LLMs (like ChatGPT) to perform entire research cycles. Provided with data alone, data-to-paper can raise hypotheses, design research plans, write and debug analysis codes, generate and interpret results, and write complete research papers. Automatic information-tracing through the process creates manuscripts in which results, methods and data are programmatically chained. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability. I will describe the strengths of the approach as well as limitations and challenges.
Prof. Kishony would be available to discuss with students and
postdocs after his seminar (2:15 pm - 3 pm).
So we encourage interested students and postdocs to stay after his talk!
FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/
Seminars
Date:
13
October, 2024
Sunday
Hour: 13:15-14:30
The Clore Center for Biological Physics
Statistical Physics of Multicomponent Systems with Non-Reciprocal Interactions
After a brief introduction related to ultralight (pseudo) scalar dark matter, we shall describe the current status of searches for ultralight dark matter (UDM). We explain why modern clocks can be used to search for both scalar and axion dark matter fields. We review existing and new types of well-motivated models of UDM and argue that they all share one key ingredient - their dominant coupling is to the QCD/nuclear sector.
This is very exciting as we are amidst a revolution in the field of dark matter searches as laser excitation of Th-229 with effective precision of 1:10^13 has been recently achieved, which as we show, is already probing uncharted territory of models. Furthermore, Th-229-based nuclear clock can potentially improve the sensitivity to physics of dark matter and beyond by factor of 10^10! It has several important implications to be discussed.
Seminars
Date:
07
July, 2024
Sunday
Hour: 13:15-14:30
The Clore Center for Biological Physics
What does the system “care about”?
Empirical approaches to identifying biological regulation
Prof. Naama Brenner | Dept. of Chemical Engineering & Network Biology Research Lab, Technion
Biological systems regulate their action at multiple levels of organization, from molecular circuits to physiological function. This “homeostasis” maintains stability of the system in the face of external and internal perturbation. How exactly this is achieved remains a topic of ongoing investigation; challenges are high dimensionality, many coupled positive and negative feedback loops, conflicting regulation demands and interaction with the environment.
Here I will introduce an empirical approach to the fundamental question – how do we know what it is that the system really “cares about”? What variable, or combination of variables, is under regulation? Two data-driven methods will be presented. one based on statistical analysis and applied to bacterial growth and division, revealing a hierarchy of regulation – from tightly regulated to sloppy variables. The second is based on a machine-learning algorithm we developed to identify regulation with minimal assumptions. This provides a different angle on the problem and highlights directions for future research.
FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/
Seminars
Date:
23
June, 2024
Sunday
Hour: 12:45-14:30
The Clore Center for Biological Physics
The role of sign indefinite invariants in shaping turbulent cascades.
Our work answers a nearly 60-year quest to derive the turbulent spectrum of weakly interacting internal gravity waves from first principles. The classical wave-turbulence approach didn’t work, as the underlying equation, both in 2D and 3D, is an anisotropic, non-canonical Hamiltonian equation.
A key consequence of the non-canonical Hamiltonian is the conservation of a sign-indefinite quadratic invariant alongside the sign-definite quadratic energy. In 2D, this allows us to derive a much simpler kinetic equation. We leverage this simplification into the derivation of solutions of the kinetic equation, one of which is the turbulent spectrum of weakly interacting 2D internal gravity waves. Our spectrum exactly matches the phenomenological oceanic Garrett-Munk spectrum in the limit of large vertical wave numbers and zero rotation.
This talk is based on recent joint works with Oliver Bühler and Jalal Shatah
arXiv:2311.04183 (to appear soon in PRL).
arXiv:2406.06010.
FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/
