A nucleotide binding rectification Brownian ratchet model for translocation of Y-family DNA polymerases

Y-family DNA polymerases are characterized by low-fidelity synthesis on undamaged DNA and ability to catalyze translesion synthesis over the damaged DNA. Their translocation along the DNA template is an important event during processive DNA synthesis. In this work we present a Brownian ratchet model for this translocation, where the directed translocation is rectified by the nucleotide binding to the polymerase. Using the model, different features of the available structures for Dpo4, Dbh and polymerase ι in binary and ternary forms can be easily explained. Other dynamic properties of the Y-family polymerases such as the fast translocation event upon dNTP binding for Dpo4 and the considerable variations of the processivity among the polymerases can also be well explained by using the model. In addition, some predicted results of the DNA synthesis rate versus the external force acting on Dpo4 and Dbh polymerases are presented. Moreover, we compare the effect of the external force on the DNA synthesis rate of the Y-family polymerase with that of the replicative DNA polymerase

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.National Institutes of Health (U.S.) (CA112967)Singapore-MIT Alliance for Research and Technolog