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 (64 of 69: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 30
  Gene deletion: YDL168W YMR303C YBR068C YDR046C YBR069C YGR087C YLR044C YLR134W YDR380W YDL080C YKL192C YLR209C YPL148C YBR291C YCL025C YKR039W YOL020W YOR120W YOL059W YGR191W YJR078W YJL121C YLR348C YPL092W YPR069C YBR176W YML120C YHR074W YEL046C YBR180W   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.103332 (mmol/gDw/h)
  Minimum Production Rate : 0.112328 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_o2_e : 2.000000
  EX_nh4_e : 1.057744
  EX_pi_e : 0.256926
  EX_so4_e : 0.007988

Product: (mmol/gDw/h)
  EX_h_e : 22.366154
  EX_h2o_e : 13.715224
  EX_succ_e : 7.087236
  EX_3c3hmp_e : 3.334656
  EX_pyr_e : 0.619798
  EX_3mop_e : 0.239782
  Auxiliary production reaction : 0.112328
  EX_pap_e : 0.005921
  EX_gly_e : 0.000536
  EX_for_e : 0.000534

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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