Periodic Reporting for period 1 - TSBC (Thermodynamics of synthetic biological circuits)
Reporting period: 2023-01-01 to 2024-12-31
The project aimed to deepen the understanding of non-equilibrium thermodynamics within biological networks. To achieve this, the researchers employed the framework of stochastic thermodynamics. Central to their approach was the development of a network theory tailored to stochastic thermodynamics, which they then used to investigate the behavior of biological systems.
The primary contribution of this network theory was methodological. It not only provided new tools for analyzing biological networks but also extended its applicability to other domains, including electronic networks. A second major objective of the project involved applying this theoretical framework to specific biological phenomena. The researchers focused on key biological motifs, such as proofreading mechanisms and positional information.
This work had significant implications for the field of biophysics, offering insights into the optimization and underlying design principles of biological systems.
The primary contribution of this network theory was methodological. It not only provided new tools for analyzing biological networks but also extended its applicability to other domains, including electronic networks. A second major objective of the project involved applying this theoretical framework to specific biological phenomena. The researchers focused on key biological motifs, such as proofreading mechanisms and positional information.
This work had significant implications for the field of biophysics, offering insights into the optimization and underlying design principles of biological systems.
In the scope of Work Package 1, the research team developed several methods to analyze the two distinct types of networks outlined in WP 1.1 and WP 1.2. A central part of this effort involved applying the framework of dynamical mean field theory to explore the thermodynamics of general networks encompassing both categories. To support this analysis, the team drew upon the mathematical foundations of optimal transport theory.
Additionally, a general framework was established to optimize the driving forces within these networks, considering both energy dissipation and other thermodynamic properties such as precision. This optimization relied on linear response theory. Complementing this, the researchers devised a method to infer these thermodynamic quantities by leveraging known thermodynamic bounds.
In Work Package 2, the investigation began with a fundamental network motif: the memory device. A machine learning algorithm was employed to minimize the thermodynamic cost associated with memory erasure. The focus then shifted to sensory systems, particularly proofreading mechanisms. Here, the team extended the thermodynamic uncertainty relation to derive a universal thermodynamic bound. They also examined alternative proofreading strategies, such as energy-relay proofreading.
The work concluded with an analysis of signaling networks, with particular attention to positional information. Using macroscopic fluctuation theory, the researchers derived an upper bound on the achievable positional information, expressed in terms of underlying thermodynamic gradients.
Additionally, a general framework was established to optimize the driving forces within these networks, considering both energy dissipation and other thermodynamic properties such as precision. This optimization relied on linear response theory. Complementing this, the researchers devised a method to infer these thermodynamic quantities by leveraging known thermodynamic bounds.
In Work Package 2, the investigation began with a fundamental network motif: the memory device. A machine learning algorithm was employed to minimize the thermodynamic cost associated with memory erasure. The focus then shifted to sensory systems, particularly proofreading mechanisms. Here, the team extended the thermodynamic uncertainty relation to derive a universal thermodynamic bound. They also examined alternative proofreading strategies, such as energy-relay proofreading.
The work concluded with an analysis of signaling networks, with particular attention to positional information. Using macroscopic fluctuation theory, the researchers derived an upper bound on the achievable positional information, expressed in terms of underlying thermodynamic gradients.
In Work Package 1, the researchers developed a comprehensive theoretical framework to describe, optimize, and infer the thermodynamic properties of non-equilibrium networks. Several of these theoretical findings were also demonstrated in comparison with experimental data, illustrating the practical relevance of the work.
Within Work Package 2, the team successfully investigated the three main types of systems outlined in the project description (WP 2.1 2.2 and 2.3). For each of these systems, a robust thermodynamic characterization was provided, contributing to a unified understanding of their behavior.
Looking ahead, the project opens several avenues for future research. A key direction involves conducting more extensive experimental validations of the theoretical predictions, as the current work was predominantly theoretical. Additionally, exploring more sophisticated biological models holds promise for uncovering deeper insights into the thermodynamic principles governing biological systems.
Within Work Package 2, the team successfully investigated the three main types of systems outlined in the project description (WP 2.1 2.2 and 2.3). For each of these systems, a robust thermodynamic characterization was provided, contributing to a unified understanding of their behavior.
Looking ahead, the project opens several avenues for future research. A key direction involves conducting more extensive experimental validations of the theoretical predictions, as the current work was predominantly theoretical. Additionally, exploring more sophisticated biological models holds promise for uncovering deeper insights into the thermodynamic principles governing biological systems.