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 (48 of 69: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 29
  Gene deletion: YBR068C YMR083W YDR046C YBR069C YPL061W YGR087C YLR044C YLR134W YDR380W YKL192C YLR209C YNL141W YBR291C YHR002W YCL025C YKR039W YOL020W YOR120W YOL059W YGR191W YBR166C YJR078W YJL121C YLR348C YPL092W YPR069C YLR146C YML120C YFR047C   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.124562 (mmol/gDw/h)
  Minimum Production Rate : 0.141758 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_o2_e : 2.000000
  EX_nh4_e : 1.300473
  EX_pi_e : 0.322418
  EX_so4_e : 0.009629

Product: (mmol/gDw/h)
  EX_h_e : 20.308808
  EX_h2o_e : 13.967570
  EX_succ_e : 6.755182
  EX_pyr_e : 5.993129
  EX_iamoh_e : 1.488998
  EX_2mbtoh_e : 0.289046
  Auxiliary production reaction : 0.141758
  EX_pap_e : 0.007137
  EX_gly_e : 0.000646
  EX_for_e : 0.000644

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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