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 (57 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 YNR033W YOL052C YHR002W YJL060W YCL025C YKR039W YOL020W YOR120W YOL059W YGR191W YJL121C YLR348C YPL092W YML120C YEL046C YDR402C YLR328W   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.106017 (mmol/gDw/h)
  Minimum Production Rate : 0.101236 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_o2_e : 2.000000
  EX_nh4_e : 1.029191
  EX_pi_e : 0.235582
  EX_so4_e : 0.008195

Product: (mmol/gDw/h)
  EX_h_e : 22.414763
  EX_h2o_e : 13.633705
  EX_succ_e : 7.119557
  EX_3c3hmp_e : 3.352391
  EX_pyr_e : 0.586869
  EX_3mop_e : 0.217993
  Auxiliary production reaction : 0.101236
  EX_pap_e : 0.006075
  EX_gly_e : 0.000550
  EX_for_e : 0.000548

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