Topic 6

Thermodynamics, information processing, and control of chemical reaction networks

Coordinators and Main Speakers

Coordinators

Praful Gagrani
Liang Shiling
Tetsuya J. Kobayashi

Main participants

Yusuke Himeoka
Daniel Maria Busiello

Topic outline

Aim of this session

When we say that evolution optimizes for a particular function, what do we mean, and how should we mathematically formalize it? With their rich mathematical structure and strong ability to model experimental data, chemical reaction networks (CRNs) provide a natural framework for exploring these questions. In this session, we will examine various aspects of CRNs, from thermodynamics and information processing to optimal control. Building on this foundation, we will explore their applications across biology, from the origins of life to cell death, concluding with a discussion on recent advances in optimization within cellular metabolism.

Background

Daniel Maria Busiello

[1] G Nicoletti, and DM Busiello, Information propagation in multilayer networks with higher-order interactions across timescales, Physical Review X 14, 021007 (2024)

[2] R Cheong, A Rhee, CJ Wang, I Nemenman, and A Levchenko, Information transduction capacity of noisy biochemical signaling networks, Science 334, 354-358 (2011)

[3] V Ngampruetikorn, DJ Schwab, and G Stephens, Energy consumption and cooperation for optimal sensing, Nature Communications 11, 975 (2020)

[4] G Nicoletti, M Bruzzone, S Suweis, M Dal Maschio, and DM Busiello, Information gain at the onset of habituation to repeated stimuli, eLife 13:RP99767 (2024)

[5] I Di Terlizzi, et al., Variance sum rule for entropy production, Science 383, 971-976 (2024)

[6] G Nicoletti, and DM Busiello, Tuning transduction from hidden observables to optimize information harvesting, Physical Review Letters 133, 158401 (2024)

Yusuke Himeoka

[1] Himeoka, Yusuke, Shuhei A. Horiguchi, and Tetsuya J. Kobayashi. 2024. “Theoretical Basis for Cell Deaths.” Physical Review Research 6 (4): 043217.Himeoka, Yusuke, Shuhei A. Horiguchi, and Tetsuya J. Kobayashi. 2024. “Theoretical Basis for Cell Deaths.” Physical Review Research 6 (4): 043217.

[2] Himeoka, Yusuke, and Chikara Furusawa. 2024. “Perturbation-Response Analysis of in Silico Metabolic Dynamics in Nonlinear Regime: Hard-Coded Responsiveness in the Cofactors and Network Sparsity.” eLife. https://doi.org/10.7554/elife.98800.1.

[3] Himeoka, Yusuke, and Namiko Mitarai. 2022. “Emergence of Growth and Dormancy from a Kinetic Model of the Escherichia Coli Central Carbon Metabolism.” Physical Review Research 4 (4): 043223.

[4] Lafontaine Rivera, Jimmy G., Matthew K. Theisen, Po-Wei Chen, and James C. Liao. 2017. “Kinetically Accessible Yield (KAY) for Redirection of Metabolism to Produce Exo-Metabolites.” Metabolic Engineering 41 (May): 144–51.

[5] Chakrabarti, Anirikh, Ljubisa Miskovic, Keng Cher Soh, and Vassily Hatzimanikatis. 2013. “Towards Kinetic Modeling of Genome-Scale Metabolic Networks without Sacrificing Stoichiometric, Thermodynamic and Physiological Constraints.” Biotechnology Journal 8 (9): 1043–57.

Time Table

9:30-10:30: Talk and discussion by Shiling Liang

10:30-11:00: Break

11:00-12:00: Talk and discussion by Yuji Hirono

12:00-13:00: Lunch

13:00-14:30: Free time and OIST Lab tour of Simone Pigolotti Lab

14:30-15:30: Talk and discussion by Yusuke Himeoka
"Stability and death of in silico metabolism"

15:30-16:00: Break

16:00-17:00: Talk and discussion by Daniel Maria Busiello
"Information propagation in stochastic multiscale systems, from chemical signaling to transduction mechanisms"

18:00-21:00: Dinner (taxi leaves at 18:00 from Lab5 Parking Lot)

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