Literature DB >> 26646890

Effective reaction rates for diffusion-limited reaction cycles.

Paweł Nałęcz-Jawecki1, Paulina Szymańska1, Marek Kochańczyk2, Jacek Miękisz3, Tomasz Lipniacki2.   

Abstract

Biological signals in cells are transmitted with the use of reaction cycles, such as the phosphorylation-dephosphorylation cycle, in which substrate is modified by antagonistic enzymes. An appreciable share of such reactions takes place in crowded environments of two-dimensional structures, such as plasma membrane or intracellular membranes, and is expected to be diffusion-controlled. In this work, starting from the microscopic bimolecular reaction rate constants and using estimates of the mean first-passage time for an enzyme-substrate encounter, we derive diffusion-dependent effective macroscopic reaction rate coefficients (EMRRC) for a generic reaction cycle. Each EMRRC was found to be half of the harmonic average of the microscopic rate constant (phosphorylation c or dephosphorylation d), and the effective (crowding-dependent) motility divided by a slowly decreasing logarithmic function of the sum of the enzyme concentrations. This implies that when c and d differ, the two EMRRCs scale differently with the motility, rendering the steady-state fraction of phosphorylated substrate molecules diffusion-dependent. Analytical predictions are verified using kinetic Monte Carlo simulations on the two-dimensional triangular lattice at the single-molecule resolution. It is demonstrated that the proposed formulas estimate the steady-state concentrations and effective reaction rates for different sets of microscopic reaction rates and concentrations of reactants, including a non-trivial example where with increasing diffusivity the fraction of phosphorylated substrate molecules changes from 10% to 90%.

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Year:  2015        PMID: 26646890     DOI: 10.1063/1.4936131

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  1 in total

1.  SPATKIN: a simulator for rule-based modeling of biomolecular site dynamics on surfaces.

Authors:  Marek Kochanczyk; William S Hlavacek; Tomasz Lipniacki
Journal:  Bioinformatics       Date:  2017-11-15       Impact factor: 6.937

  1 in total

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