Search for: All records

Recent experimental advances led to the development of DNA base editors (BEs) with single-nucleotide precision, which is critical for future progress in various scientific and technological fields. The molecular mechanisms of single-base discrimination, however, remain poorly understood. Using a recently developed stochastic approach, we theoretically investigated the dynamics of single-base editing. More specifically, transient and mean times to edit “TC” motifs by cytosine BEs are explicitly evaluated for correct (target) and incorrect (bystander) locations on DNA. In addition, the effect of mutations on the dynamics of the single-base edition is also analyzed. It is found that for most ranges of parameters, it is possible to temporarily separate target and bystander products of base editing, supporting the idea of dynamic selectivity as a method of improving the precision of single-base editing. We conclude that to improve the efficiency of single-base editing, selecting the probability or selecting the time requires different strategies. Physical–chemical arguments to explain the observed dynamic properties are presented. The theoretical analysis clarifies some important aspects of the molecular mechanisms of selective base editing. 

Abstract Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.