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 (58 of 69: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 29
  Gene deletion: YDL168W YMR303C YBR068C YDR046C YBR069C YPL061W YDR035W YGR087C YLR044C YLR134W YDR380W YDL080C YMR009W YKL192C YLR209C YPL148C YJL060W YCL025C YKR039W YOL020W YOR120W YOL059W YGR191W YDR403W YJL121C YLR348C YPL092W YML120C YLR328W   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.103628 (mmol/gDw/h)
  Minimum Production Rate : 0.112649 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_o2_e : 2.000000
  EX_nh4_e : 1.301240
  EX_pi_e : 0.257661
  EX_so4_e : 0.008010

Product: (mmol/gDw/h)
  EX_h_e : 22.481711
  EX_h2o_e : 13.479666
  EX_succ_e : 7.084623
  EX_3c3hmp_e : 3.452985
  EX_pyr_e : 0.501338
  EX_gly_e : 0.241006
  EX_etoh_e : 0.240468
  Auxiliary production reaction : 0.112649
  EX_pap_e : 0.005938
  EX_for_e : 0.000536

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
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