PT  - JOURNAL ARTICLE
AU  - Yun Min Song
AU  - Hyukpyo Hong
AU  - Jae Kyoung Kim
TI  - Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities
AID  - 10.1101/2021.04.08.438974
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.08.438974
4099  - http://biorxiv.org/content/early/2021/04/26/2021.04.08.438974.short
4100  - http://biorxiv.org/content/early/2021/04/26/2021.04.08.438974.full
AB  - Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation.Competing Interest StatementThe authors have declared no competing interest.