Computational biology for cell-free systems

Cell-free systems (CFS) decouple gene expression and metabolic pathways from living cells, offering a rapid, modular platform for biosensing, pathway prototyping, and protein production. This review surveys mechanistic and data-driven computational approaches tailored to CFS design and optimization. We compare deterministic ordinary differential equation (ODE) and stochastic simulation frameworks for modeling transcription-translation dynamics, describe adaptations of genome-scale metabolic models (GEMs) and flux balance analysis (FBA) for extract-based systems, and evaluate machine-learning strategies that learn sequence-to-function mappings from high-throughput cell-free assays. We summarize key software and discuss applications in paper-based diagnostics, reconstructed metabolic pathways, and high-yield cell-free protein synthesis. Recent advances in CRISPR based regulation using pre expressed dCas9 or RNA processing enzymes enable construction of multi-layer genetic circuits in extracts. Finally, we identify current gaps limited standardization of kinetic assays, sparse public datasets, and few hybrids kinetic-constraint modeling studies and propose a roadmap for community resources and hybrid modeling efforts that combine mechanistic clarity with machine learning (ML)-driven speed.