MetNetComp Database [1] / Minimal gene deletions Minimal gene deletions for simulation-based growth-coupled production. You can also see maximal gene deletions.

Model : iML1515 [2].
Target metabolite : fmnh2_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (2 of 15: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 27
  Gene deletion: b3426 b2242 b3553 b1241 b0351 b3114 b3952 b0903 b2779 b2925 b2097 b1851 b1638 b3962 b4139 b4267 b1033 b1415 b4015 b1014 b2799 b4388 b0529 b3028 b4266 b2285 b1378   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.362763 (mmol/gDw/h)
  Minimum Production Rate : 0.358525 (mmol/gDw/h)

Substrate: (mmol/gDw/h)
  EX_o2_e : 38.420443
  EX_glc__D_e : 10.000000
  EX_nh4_e : 5.351909
  EX_pi_e : 0.708449
  EX_so4_e : 0.091351
  EX_k_e : 0.070809
  EX_fe2_e : 0.005826
  EX_mg2_e : 0.003147
  EX_ca2_e : 0.001888
  EX_cl_e : 0.001888
  EX_cu2_e : 0.000257
  EX_mn2_e : 0.000251
  EX_zn2_e : 0.000124
  EX_ni2_e : 0.000117

Product: (mmol/gDw/h)
  EX_h2o_e : 53.255715
  EX_co2_e : 39.015323
  EX_h_e : 4.767311
  Auxiliary production reaction : 0.358525
  DM_5drib_c : 0.000082
  DM_4crsol_c : 0.000081

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.

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Last updated: 21-Sep-2023
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