Periodic Reporting for period 2 - FLUCTENZ (The Fluctuating Enzyme: From Catalysis to Vibrational Dynamics)
Periodo di rendicontazione: 2023-02-01 al 2024-07-31
Enzymes spin the wheel of life by catalyzing a myriad of chemical reactions central to the growth, development, and metabolism of all living organisms. Without enzymes, essential processes would progress so slowly that life would virtually grind to a halt, and the quest to determine their inner workings thus continues to attract and fascinate scientists over a broad range of disciplines. Cutting-edge methods now allow one to observe and manipulate individual enzymes, as well as other molecules, in their reaction course. These revealed that chemistry at the single-molecule level is inherently stochastic and, at times, extremely unintuitive. Thermal fluctuations push molecules to vibrate erratically and force a probabilistic description, but classical approaches are deeply entrenched in determinism, and so is our basic expectation for how things should behave in the world around us. The main objective of this project is to bring advanced theoretical methods and mathematical tools to the analysis of stochastic fluctuations, of enzymes and other molecules, at the single-molecule level. These will be based on state-of-the-art approaches in statistical physics and stochastic processes that we will adapt and further advance to need. Equipped with mathematical techniques that have so far been foreign to the field, we expect to rectify fundamental flaws in our understanding, predict the emergence of novel phenomena, and develop novel inference methods for physically meaningful parameters that have so far evaded direct measurement. The amalgamation of all these efforts will pave the way to large-scale, multi-tier, characterization of stochastic fluctuations which is expected to transform our understanding of chemical kinetics at the single molecule level.
During the initial period of the project, we have made significant strides in exploring stochastic fluctuations at the single-molecule level and in the development of innovative methods for inferring hidden kinetic parameters from single-molecule measurements. Our work began with foundational research into stochastic resetting, a concept at the heart of the project's initial aim. This shed light on previously unforeseen relations and uncovered new insights into the dynamics of random processes that undergo resetting. These methodological advancements also laid the groundwork for subsequent investigations into single-molecule kinetics which we are currently conducting. In parallel, we delved into various aspects of molecular dynamics, providing new ways to describe random motion in disordered media, developing a microscopic theory of adsorption kinetics, and studying the dynamics of sticky particle escape from complex surface topographies. We also developed novel methodologies to describe and analyze gated chemical reactions, thus providing valuable tools for probing the dynamic nature of biochemical reactions. This endeavor directly addresses the overarching goal of our project, which aims to advance our understanding of enzyme dynamics and the kinetics of other molecular processes crucial for life.
We were also engaged in a series of interdisciplinary collaborations that yielded innovative discoveries and methodologies with significant real-world applications. A collaboration with the groups of Prof. Doron Shabat and Prof. Micha Fridman led to the development of a method for bacterial classification, based on enzymatic activity profiles, demonstrating the project's translation of fundamental research into practical solutions for healthcare and biotechnology. In another collaboration with the group of Prof. Yael Roichman, we launched an experimental study to explore the interplay between stochastic resetting and environmental memory. This challenged existing models and theories, resulting in a better understanding of the impact of environmental factors on search efficiency. Finally, a collaboration with the group of Dr. Barak Hirshberg produced a series of publications where we introduced stochastic resetting to address longstanding challenges in computational chemistry. All these collaborations underscore the project's interdisciplinary nature and its ability to address complex scientific challenges through innovative approaches.
We were also engaged in a series of interdisciplinary collaborations that yielded innovative discoveries and methodologies with significant real-world applications. A collaboration with the groups of Prof. Doron Shabat and Prof. Micha Fridman led to the development of a method for bacterial classification, based on enzymatic activity profiles, demonstrating the project's translation of fundamental research into practical solutions for healthcare and biotechnology. In another collaboration with the group of Prof. Yael Roichman, we launched an experimental study to explore the interplay between stochastic resetting and environmental memory. This challenged existing models and theories, resulting in a better understanding of the impact of environmental factors on search efficiency. Finally, a collaboration with the group of Dr. Barak Hirshberg produced a series of publications where we introduced stochastic resetting to address longstanding challenges in computational chemistry. All these collaborations underscore the project's interdisciplinary nature and its ability to address complex scientific challenges through innovative approaches.
We offered a novel solution to a longstanding problem in queueing systems by showing that a simple service resetting mechanism can reverse the deleterious effects of stochastic service time fluctuations. We are now exploring ways in which we can leverage results coming from this analysis to better understand enzymatic catalysis at the single-molecule level.
We helped develop a method for bacterial classification and characterization, based on enzymatic activity profiles.
We significantly advanced the understanding of adsorption kinetics at the single-molecule level, and developed novel ways to infer physically meaningful quantities like binding and unbinding rates from indirect first-passage time measurements.
We significantly advanced the understanding of gated chemical reactions: a concept that generalizes the all-familiar diffusion-limited reactions by conditioning the occurrence of a reaction upon constituents being in the reactive state when they collide.
We helped to significantly advance the state of the art in molecular dynamics simulations, and in enhanced sampling algorithms, by incorporating stochastic resetting into these methods.
We extended the Montroll-Weiss continuous time random walk to capture effects coming from random diffusivity fluctuations. These have been observed in the motion of asset prices and molecules.
We developed a stylized model for a random walker that interacts with its environment. We used it to show that even a limited ability of the walker to push away obstacles that block its path will eventually lead to caging, and thus to the loss of the percolation transition—a hallmark of non-interacting random walks in disordered media.
We helped develop a method for bacterial classification and characterization, based on enzymatic activity profiles.
We significantly advanced the understanding of adsorption kinetics at the single-molecule level, and developed novel ways to infer physically meaningful quantities like binding and unbinding rates from indirect first-passage time measurements.
We significantly advanced the understanding of gated chemical reactions: a concept that generalizes the all-familiar diffusion-limited reactions by conditioning the occurrence of a reaction upon constituents being in the reactive state when they collide.
We helped to significantly advance the state of the art in molecular dynamics simulations, and in enhanced sampling algorithms, by incorporating stochastic resetting into these methods.
We extended the Montroll-Weiss continuous time random walk to capture effects coming from random diffusivity fluctuations. These have been observed in the motion of asset prices and molecules.
We developed a stylized model for a random walker that interacts with its environment. We used it to show that even a limited ability of the walker to push away obstacles that block its path will eventually lead to caging, and thus to the loss of the percolation transition—a hallmark of non-interacting random walks in disordered media.