MetNetComp Database [1] / Minimal gene deletions

Minimal gene deletions for simulation-based growth-coupled production. You can also see maximal gene deletions.

Model : iMM904 [2].
Target metabolite : cdpea_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (55 of 69: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 29
  Gene deletion: YDL168W YMR303C YBR068C YDR046C YBR069C YGR087C YLR044C YLR134W YDR380W YDL080C YKL192C YLR209C YOL052C YBR291C YHR002W YJL060W YCL025C YKR039W YOL020W YOR120W YOL059W YGR191W YBR166C YJL121C YLR348C YPL092W YML120C YEL046C YFR047C   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.102366 (mmol/gDw/h)
  Minimum Production Rate : 0.116498 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_o2_e : 2.000000
  EX_nh4_e : 1.068736
  EX_pi_e : 0.264964
  EX_so4_e : 0.007913

Product: (mmol/gDw/h)
  EX_h_e : 22.368381
  EX_h2o_e : 13.735729
  EX_succ_e : 7.095772
  EX_3c3hmp_e : 3.338267
  EX_pyr_e : 0.600951
  EX_3mop_e : 0.237540
  Auxiliary production reaction : 0.116498
  EX_pap_e : 0.005866
  EX_gly_e : 0.000531
  EX_for_e : 0.000529

Visualization
  1. Download JSON file.
  2. Go to Escher site [3].
  3. Select "Data > Load reaction data" and apply the downloaded file.

References
[1] Tamura, T. MetNetComp: Database for minimal and maximal gene deletion strategies for growth-coupled production of genome-scale metabolic networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press.
[2] Norsigian, C. J., Pusarla, N., McConn, J. L., Yurkovich, J. T., Dräger, A., Palsson, B. O., & King, Z. (2020). BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic acids research, 48(D1), D402-D406.
[3] King, Z. A., Dräger, A., Ebrahim, A., Sonnenschein, N., Lewis, N. E., & Palsson, B. O. (2015). Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS computational biology, 11(8), e1004321.

Last updated: 27-Sep-2023
Contact