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Modern chemical science and industries critically depend on the application of various catalytic methods. However, the underlying molecular mechanisms of these processes still remain not fully understood. Recent experimental advances that produced highly-efficient nanoparticle catalysts allowed researchers to obtain more quantitative descriptions, opening the way to clarify the microscopic picture of catalysis. Stimulated by these developments, we present a minimal theoretical model that investigates the effect of heterogeneity in catalytic processes at the single-particle level. Using a discrete-state stochastic framework that accounts for the most relevant chemical transitions, we explicitly evaluated the dynamics of chemical reactions on single heterogeneous nanocatalysts with different types of active sites. It is found that the degree of stochastic noise in nanoparticle catalytic systems depends on several factors that include the heterogeneity of catalytic efficiencies of active sites and distinctions between chemical mechanisms on different active sites. The proposed theoretical approach provides a single-molecule view of heterogeneous catalysis and also suggests possible quantitative routes to clarify some important molecular details of nanocatalysts.
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Power of stochastic kinetic models: From biological signaling and antibiotic activities to T cell activation and cancer initiation dynamics
All chemical processes exhibit two main universal features. They are stochastic because chemical reactions might happen only after random successful collisions of reacting species, and they are dynamic because the amount of reactants and products change with time. Since biological processes rely heavily on specific chemical reactions, stochasticity and dynamics are also crucial features for all living systems. To understand the molecular mechanisms of chemical and biological processes, it is important to develop and apply theoretical methods that fully incorporate the randomness and dynamic nature of these systems. In recent years, there have been significant advances in formulating and exploring such theoretical methods. As an illustration of such developments, in this review, the recent applications of stochastic kinetic models for various biological processes are discussed. Specifically, we focus on applying these theoretical approaches to investigate the biological signaling, clearance of bacteria under antibiotics, T cells activation in the immune system, and cancer initiation dynamics. The main advantage of the presented stochastic kinetic models is that they generally can be solved analytically, allowing to clarify the underlying microscopic picture, as well as explain the existing experimental observations and make new testable predictions. This theoretical approach becomes a powerful tool in uncovering the molecular mechanisms of complex natural phenomena.
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- PAR ID:
- 10324766
- Date Published:
- Journal Name:
- WIREs Computational Molecular Science
- ISSN:
- 1759-0876
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/wcms.1612
