Dynamic Models of Metabolic Networks and Analysis of Cell-Free Protein Synthesis

Abstract

Metabolism is the central process through which cells manage their resources to survive, adapt and meet energetic demands. To implement these diverse functions, cells have complex and highly interconnected networks of chemical reactions between genes, RNA, proteins and metabolites. Systems modeling and metabolic engineering arose from the desire to harness the power of metabolism to produce products that benefit society. A primary challenge is the development of metabolic mathematical models that are able to describe the effect genetic perturbations have on cellular behavior. In this study, we first review metabolic modeling methods and go on to develop computational tools for the analysis and engineering of microbial systems with a focus on cell-free protein synthesis (CFPS). Cell-free protein expression has become a widely used research tool in systems and synthetic biology, and a promising technology for biomanufacturing of proteins. Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Central amongst these is direct access to metabolites and the biosynthetic machinery without the interference of a cell wall or the complications associated with cell growth. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Cell-free protein synthesis relies on transcription and translation machinery to produce a protein of interest. To fuel this process requires biochemical enzymes and reactions that are involved in complex metabolic pathways. Toward this, we developed dynamic mathematical models that describe CFPS metabolism and provide insights into improving these systems. We began with a sequence specific constraint based model that coupled transcription/translation processes and the regulation of gene expression with the availability of metabolic resources. Then, we developed a robust comprehensive analysis to quantify absolute levels of metabolites in CFPS using isotopically labeled standards. We expanded our modeling framework by integrating absolute metabolite measurements along with kinetic parameters, enzyme levels, and enzyme activity assays. The framework predicted the overall production of mRNA and protein along with changes in metabolic behavior with two different oxidative phosphorylation inhibitors. Taken together, we provided a comprehensive mathematical framework of CFPS metabolism that could be used to identify strategies for the improvement of CFPS productivity, yield and efficiency.