A quantitative method for proteome reallocation using minimal regulatory interventions

摘要: Abstract Engineering resource allocation in biological systems for synthetic biology applications is an ongoing challenge. Wild type organisms allocate abundant cellular resources ensuring survival changing environments, reducing the productivity of engineered functions. Here we present a novel approach engineering Escherichia coli by rationally modifying transcriptional regulatory network bacterium. Our method (ReProMin) identifies minimal set genetic interventions that maximise savings cell would normally be used to express non-essential genes. To this end categorize Transcription Factors (TFs) according essentiality genes they regulate and use available proteomic data rank them based on its balance, defined as net charge release. Using combinatorial approach, design removal TFs release validate model predictions experimentally. Expression profiling resulting strain shows our designed are highly specific. We show containing only three mutations, theoretically releasing 0.5% their proteome, has higher proteome budget increased production yield molecule interest obtained from recombinant metabolic pathway. This combining whole-cell effective way optimizing strains predictable using conventional molecular methods. Importance Biological mechanisms complex occur hierarchical layers such transcription, translation post-translational mechanisms. foresee mechanism control layer will aid phenotypes. ability engineer dependent understanding how cells sense respond environment at system level. Few studies have tackled issue none rational way. By developing workflow current knowledge E. coli’s network, pursue objective minimizing intervention principle. developed reduce hedging allocation. datasets bacterium were able reallocate parts unused laboratory conditions task. (theoretically 0.5%) with mutations way, which results capabilities expression pathways interest. Highlights Proteome reduction principle Regulatory integration identify transcription factor activated Deletion TF combination reduces greater load specific Designed less burden, improved protein violacein