MetNetComp Database [1] / Minimal gene deletions

Minimal gene deletions for simulation-based growth-coupled production. You can also see maximal gene deletions.

Model : iLB1027_lipid [2].
Target metabolite : eicos2coa_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (19 of 49: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 21
  Gene deletion: PHATRDRAFT_20342 PHATRDRAFT_27726 PHATRDRAFT_13987 PHATRDRAFT_draft877 PHATRDRAFT_30145 Phatr3_EG02261 PHATRDRAFT_54478 PHATRDRAFT_8828 PHATRDRAFT_34120 PHATRDRAFT_49339 PHATRDRAFT_45239 PHATRDRAFT_31599 PHATRDRAFT_50742 PHATRDRAFT_19708 Phatr3_EG02042 PHATRDRAFT_36906 PHATRDRAFT_50971 PHATRDRAFT_20318 PHATRDRAFT_49505 PHATRDRAFT_15536 PHATRDRAFT_49903   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.346587 (mmol/gDw/h)
  Minimum Production Rate : 0.009103 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_photon_e : 1000.000000
  EX_co2_e : 18.536201
  EX_h2o_e : 14.265503
  EX_no3_e : 1.760000
  EX_h_e : 0.254850
  EX_pi_e : 0.113370
  EX_so4_e : 0.070740
  EX_mg2_e : 0.006194

Product: (mmol/gDw/h)
  EX_o2_e : 23.977794
  SK_for_c : 1.699578
  DM_biomass_c : 0.346587
  Auxiliary production reaction : 0.009103

Visualization
  1. Download JSON file.
  2. Go to Escher site [3].

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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