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 : 2amsa_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (42 of 74: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 17
  Gene deletion: b4382 b4069 b4384 b3708 b0910 b3115 b1849 b2296 b2407 b2797 b3117 b1814 b4471 b0114 b2366 b2492 b0904   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.783012 (mmol/gDw/h)
  Minimum Production Rate : 0.217943 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_o2_e : 24.842379
  EX_glc__D_e : 10.000000
  EX_nh4_e : 8.674408
  EX_pi_e : 0.755298
  EX_so4_e : 0.197178
  EX_k_e : 0.152839
  EX_fe2_e : 0.012576
  EX_mg2_e : 0.006793
  EX_ca2_e : 0.004076
  EX_cl_e : 0.004076
  EX_cu2_e : 0.000555
  EX_mn2_e : 0.000541
  EX_zn2_e : 0.000267
  EX_ni2_e : 0.000253
  EX_cobalt2_e : 0.000020

Product: (mmol/gDw/h)
  EX_h2o_e : 47.408473
  EX_co2_e : 26.295400
  EX_h_e : 7.868430
  EX_ac_e : 0.455859
  Auxiliary production reaction : 0.217943
  DM_5drib_c : 0.000176
  DM_4crsol_c : 0.000175

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: 21-Sep-2023
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