The creation of simplified representations of the central dogma’s initial processes, encompassing the synthesis of RNA from DNA and the subsequent production of proteins from RNA, allows for in silico analysis of gene expression. This involves developing computational or mathematical frameworks that mimic the molecular events involved in these biological processes. An example includes a system of differential equations that describes the rates of mRNA and protein production and degradation, parameterized by experimentally derived values to predict protein levels under varying conditions.
Such representations provide a cost-effective and rapid means to investigate the complex interactions that govern gene expression, accelerating biological discovery. Historically, these models have evolved from simple deterministic equations to sophisticated stochastic simulations that account for the inherent randomness of cellular processes. The ability to simulate these mechanisms facilitates a deeper understanding of regulatory networks, predicting cellular behavior and response to stimuli. This approach offers significant advantages in identifying potential drug targets and optimizing therapeutic strategies.