Manipulating Intracellular Stochasticity Through Tetramerization and Competing Reactions

Abstract

Biological noise is a major challenge in genetic engineering because it makes gene expression unpredictable. This uncertainty affects the efficacy of drugs, the stability of production processes, and the predictability of biological systems. Therefore, we aim to discover ways of regulate gene expression precisely. This study combined the concepts of competing reactions of oligomers in cells and noise regulation. A model was established using the stochastic simulation algorithm (SSA) to explore the tetramerization reaction of proteins as well as the competition between protein dimers and tetramers, and to analyze the impact of these reactions on the downstream product noise. The results showed that when dimers tetramerize, the noise of downstream products can be effectively reduced. Additionally, when dimers and tetramers compete with each other, the noise can be further decreased. This conclusion is of great value for future applications in the design of methods to control biological noise.

Keywords: Genetic Stochasticity, Stochastic Simulation Algorithm, Tetramerization