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 : dgsn_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (14 of 77: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 23
  Gene deletion: b2836 b3399 b2744 b3708 b3008 b0871 b2883 b1982 b2797 b3117 b1814 b4471 b0675 b2361 b0261 b0411 b4381 b0114 b2366 b2492 b0904 b1533 b3927   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.790968 (mmol/gDw/h)
  Minimum Production Rate : 0.069063 (mmol/gDw/h)

Substrate: (mmol/gDw/h)
  EX_o2_e : 25.328486
  EX_glc__D_e : 10.000000
  EX_nh4_e : 8.887702
  EX_pi_e : 0.762972
  EX_so4_e : 0.199182
  EX_k_e : 0.154391
  EX_fe2_e : 0.012704
  EX_mg2_e : 0.006862
  EX_cl_e : 0.004117
  EX_ca2_e : 0.004117
  EX_cu2_e : 0.000561
  EX_mn2_e : 0.000547
  EX_zn2_e : 0.000270
  EX_ni2_e : 0.000255
  EX_cobalt2_e : 0.000020

Product: (mmol/gDw/h)
  EX_h2o_e : 48.490740
  EX_co2_e : 26.843770
  EX_h_e : 7.613045
  Auxiliary production reaction : 0.069063
  DM_5drib_c : 0.000178
  DM_4crsol_c : 0.000176

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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