Embracing Randomness: Developing Stochastic Methods for Advancing Systems and Synthetic Biology
Dr. Ankit Gupta, Department of Biosystems Science and Engineering, ETH Zurich
| Speaker |
Dr. Ankit Gupta, Department of Biosystems Science and Engineering, ETH Zurich
|
|---|---|
| When |
Mar 05, 2025
from 04:00 PM to 05:00 PM |
| Where | LH-006, Ground Floor |
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SEMINAR TALK
Title: Embracing Randomness: Developing Stochastic Methods for Advancing Systems and Synthetic Biology
Abstract: Biological cells operate in an inherently stochastic environment, where random molecular reaction events drive variability in gene expression, signalling, and other processes. Remarkably, cells often maintain precise and robust behaviours despite this noise, raising fundamental questions about how biomolecular networks mitigate, control, or even exploit stochasticity. Stochastic methods offer a powerful mathematical framework to investigate these questions, revealing both the constraints and the design principles necessary for robustness in noisy systems. In this talk, I will discuss how stochastic reaction networks—models that capture both the randomness and structural organisation of biochemical processes—can be used to analyse and engineer biological control mechanisms. As a concrete example, I will introduce the concept of maximal Robust Perfect Adaptation (maxRPA): a property whereby a system maintains an exact steady-state output despite stochastic fluctuations and external perturbations. I will present a complete mathematical characterisation of intracellular networks capable of achieving maxRPA and show how these insights can be harnessed in synthetic biology to design controllers that restore robust adaptation in dysregulated systems. By focusing on this specific problem, I will highlight both the theoretical challenges and the exciting opportunities that stochastic methods create in systems and synthetic biology, bridging the gap between foundational mathematical frameworks and real-world experimental applications.
Title: Embracing Randomness: Developing Stochastic Methods for Advancing Systems and Synthetic Biology
Abstract: Biological cells operate in an inherently stochastic environment, where random molecular reaction events drive variability in gene expression, signalling, and other processes. Remarkably, cells often maintain precise and robust behaviours despite this noise, raising fundamental questions about how biomolecular networks mitigate, control, or even exploit stochasticity. Stochastic methods offer a powerful mathematical framework to investigate these questions, revealing both the constraints and the design principles necessary for robustness in noisy systems. In this talk, I will discuss how stochastic reaction networks—models that capture both the randomness and structural organisation of biochemical processes—can be used to analyse and engineer biological control mechanisms. As a concrete example, I will introduce the concept of maximal Robust Perfect Adaptation (maxRPA): a property whereby a system maintains an exact steady-state output despite stochastic fluctuations and external perturbations. I will present a complete mathematical characterisation of intracellular networks capable of achieving maxRPA and show how these insights can be harnessed in synthetic biology to design controllers that restore robust adaptation in dysregulated systems. By focusing on this specific problem, I will highlight both the theoretical challenges and the exciting opportunities that stochastic methods create in systems and synthetic biology, bridging the gap between foundational mathematical frameworks and real-world experimental applications.
Speaker's Bio: Ankit Gupta is a Permanent Senior Scientist II at the Department of Biosystems Science and Engineering (DBSSE), ETH Zurich. He earned his Ph.D. in Mathematics from University of Wisconsin-Madison, U.S.A. He is an Applied Mathematician, specializing in Probability Theory, Stochastic Processes, and allied areas. His research focuses on developing computational, analytical, and statistical tools for stochastic models in Biology. Many of these tools can also be applied in other domains, such as Quantitative Finance and Statistical Physics.
Join Zoom Meeting : https://zoom.us/j/91536491562?pwd=UgbGKNwno3Snpo1JVAd1CNDs8mMwTH.1
Meeting ID: 915 3649 1562
Passcode: 375186
Meeting ID: 915 3649 1562
Passcode: 375186