Next Generation Chemical Dynamics and Transport Theories for Complex Biological and Material Systems

In this talk, we will introduce new chemical dynamics models and theories useful for a

quantitative investigation into dynamics of stochastic gene expression and signal propagation

processes in living cells [1,2]. Our primary focus will be on the chemical fluctuation theorem

governing gene expression and its pioneering applications to quantitative explanations and

predictions of stochastic gene expression and signal propagation dynamics in and across living

cells. Based on the audience’s preferences, we will showcase applications of our novel

chemical dynamics theory to catalytic reactions of single enzymes and nanocatalytic systems

[3,4], or we will discuss our newly developed transport equation, whose solution provides

unified, quantitative understanding of thermal motion of molecules and ions in various complex

fluids and solid electrolytes [5]. During the second part, we will talk about our recent work on

nuclei seeds formation and their condition-dependent crystallization dynamics. This work shed

light on the thermodynamic origin of stable nuclei formation and provide unified, quantitative

explanation of the time-dependent size distribution and size-dependent growth rate of

nanoparticle systems observed by liquid phase TEM, which cannot be explained by the

classical nucleation theory or other previous theories of nucleation.

Refs:

[1] Park et al., The Chemical Fluctuation Theorem governing gene expression, Nat. Commun. 9, 297 (2018); Lim et al., Quantitative understanding of probabilistic behavior of living cells operated by vibrant reaction networks, Phys. Rev. X 5, 031014 (2015).

[2] Song et al., Frequency spectrum of chemical fluctuation: a probe of reaction mechanism and dynamics, PLoS Comp. Biol. 15, e1007356 (2019); Kang et al., Circuit-guided population acclimation of a synthetic microbial consortium for improved biochemical production, Nat. Commun. 13, 6506 (2022).

[3] Yang et al, Quantitative interpretation of the randomness in single enzyme turnover times, Biophys. J. 101, 519 (2011); Park et al., Nonclassical kinetics of clonal yet heterogeneous enzymes, J. Phys. Chem. Letters 8, 3152 (2017); Jeong et al., Phys. Rev. Letters 119, 099801 (2017).

[4] Kang et al, Stochastic kinetics of nanocatalytic systems, Phys. Rev. Letters 126, 126001 (2021); Kang et al., Real-space imaging of nanoparticle transport and interaction dynamics by graphene liquid cell TEM, Sci. Adv. 7, 49 (2021).

[5] Song et al., Transport Dynamics in Complex Fluids, Proc. Nat. Acad. Scie. U.S.A. 116, 12733 (2019); Poletayev et al., Defect-driven anomalous transport in fast-ion conducting solid electrolytes, Nat. Mater. 21, 1066 (2022).