Genome-scale metabolic models (GSMM) have been playing increasingly important roles in a wide range of application including, but not limited to, bacterial evolution, gene annotation, physiological analysis and metabolic engineering.

Flux balance analysis (FBA) is the core algorithm used to calculate metabolic flux distribution within a genome-scale metabolic model. However, due to the linear nature of GSMM, the existence of multiple equivalent solutions is inevitable in most cases of FBA.

Supported by National Basic Research Program of China (973 program, synthetic cell factory), Prof. LI Yin’s group in Institute of Microbiology, Chinese Academy of Sciences (IMCAS) developed, in a joint efforts with researchers in Tianjin Institute of Industrial Biotechnology, CAS, a novel algorithm designated as Thermodynamic Optimum Searching (TOS).

This novel algorithm aims to find the thermodynamic optimum from the FBA equivalent solutions, by applying several thermodynamic principles such as maximum entropy production, minimizing energy magnitudes and satisfying the thermodynamic second law to the maximum extent.

The novel TOS algorithm was subsequently applied to calculate the flux distribution patterns of E. coli under 5 different physiological conditions. The prediction accuracy of TOS was evaluated by comparing with the (13)C-fluxome data, and it turned out to be improved by 10.7-48.5% compared with that predicted by FBA.

Moreover, the thermodynamic driving forces of metabolic reactions within GSMM can be easily calculated with TOS method, thereby providing useful information for redesign and optimization of the energy-consuming bioprocesses.

This work has been recently published in Biotechnology and Bioengineering.