Cf-Targeter

CF-Targeter revolutionizes chassis host selection through rational design of microbial cell factories. By integrating 70 genome-scale metabolic models (GEMs) and symbolic reaction databases, it predicts optimal biosynthetic pathways for target compounds—from pharmaceuticals to biofuels—while minimizing experimental trial-and-error.

Powered by supercomputing, the platform precomputed ~500,000 biosynthetic pathways across 70 microorganisms for 6,026 compounds. Key breakthroughs include: 77% compounds synthesized in ≤3 heterologous steps; Real-time yield calculation under customized conditions (pH/temperature/aeration); Discovery of high-efficiency alternatives (e.g., 10.888 g/g glucose 1-octanol yield in Chlamydomonas reinhardtii).

Researchers dynamically visualize pathway networks, compare thermodynamic feasibility across hosts, and identify universal precursors (e.g., adenosylmethionine for 3,455 metabolites). CF-Targeter bridges computational prediction and metabolic engineering for sustainable bioproduction.