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 : ade_e
List of minimal gene deletion strategies (Download)

Gene deletion strategy (115 of 133: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 27
  Gene deletion: b3553 b4382 b4384 b0871 b2925 b2097 b0030 b2407 b1004 b3713 b1109 b0046 b3236 b2690 b1033 b3665 b3945 b1602 b0114 b0755 b3612 b0529 b2492 b0904 b1380 b0508 b2285   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.373308 (mmol/gDw/h)
  Minimum Production Rate : 0.083071 (mmol/gDw/h)

Substrate: (mmol/gDw/h)
  EX_o2_e : 30.288058
  EX_glc__D_e : 10.000000
  EX_nh4_e : 4.447053
  EX_pi_e : 0.360095
  EX_so4_e : 0.094006
  EX_k_e : 0.072867
  EX_fe2_e : 0.005996
  EX_mg2_e : 0.003238
  EX_ca2_e : 0.001943
  EX_cl_e : 0.001943
  EX_cu2_e : 0.000265
  EX_mn2_e : 0.000258
  EX_zn2_e : 0.000127
  EX_ni2_e : 0.000121

Product: (mmol/gDw/h)
  EX_h2o_e : 44.242783
  EX_co2_e : 28.200020
  EX_h_e : 9.199474
  EX_pyr_e : 5.354013
  EX_ade_e : 0.083071
  DM_5drib_c : 0.000084
  DM_4crsol_c : 0.000083

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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