Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Abstract

The synthetic biology firstly refers to the design and fabrication of biological components and systems that do not already exist in the natural world and to the redesign and fabrication of existing biological systems. The link of computational tools to cell-free systems, converts to synthetic biology is an emerging field expert to build artificial biological systems through the combination of molecular biology and engineering approaches. Herein, most findings describing the differences between in vivo and in vitro reactions and systems have been extensively described. The specific applications of computational tools to the design of an in vitro gene expression platform known as the artificial cell, its components and the strategies developed to predict activities of processor modules and to control the expression of genes have been discussed in detail. Potential applications of artificial cells in drug delivery, in biosynthesis, among others, have been described. Two sources of models for the possible developing of the computational toolbox for cell-free synthetic biology include i) Physical models of single cellular components able to be created from original principles, guiding to focus on tools to predict structure and dynamics of particular components; ii) A wide-range of mathematical models for predicting system dynamics of natural cells. Regarding modeling algorithms, there is a broad kind of models available for synthetic biologists and some areas of potential growth identified for researchers interested in developing tools for cell-free systems. Among them, deterministic, exploratory, molecular dynamic, stochastic, all atom models, among others, have been described and discussed. By using computational models to set up quantitative differences between in vitro reactions and in vivo systems, could identify specific mechanisms in living organisms to be further used in in vitro reactions in order to facilitate their processes. Thus, computational modeling would bridge the gap between in vitro and in vivo reactions.