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 : m1mpdol_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (19 of 22: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 23
  Gene deletion: PHATRDRAFT_26934 PHATRDRAFT_34010 PHATRDRAFT_16571 PHATRDRAFT_12762 Phatr3_EG02232 PHATRDRAFT_55126 PHATRDRAFT_3046 Phatr3_EG02269 PHATRDRAFT_45017 PHATRDRAFT_14762 PHATRDRAFT_13951 PHATRDRAFT_32747 PHATRDRAFT_25308 PHATRDRAFT_43194 PHATRDRAFT_46133 PHATRDRAFT_1784 PHATRDRAFT_1358 PHATRDRAFT_21970 PHATRDRAFT_43697 PHATRDRAFT_12452 Phatr3_EG02611 PHATRDRAFT_15536 PHATRDRAFT_54528   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.353366 (mmol/gDw/h)
  Minimum Production Rate : 0.015272 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_photon_e : 1000.000000
  EX_co2_e : 59.085869
  EX_h2o_e : 54.601957
  EX_no3_e : 1.760000
  EX_pi_e : 0.118286
  EX_so4_e : 0.062843
  EX_mg2_e : 0.006315

Product: (mmol/gDw/h)
  EX_o2_e : 45.170439
  SK_for_c : 41.032916
  EX_h_e : 39.081266
  DM_biomass_c : 0.353366
  Auxiliary production reaction : 0.015272

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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