MetNetComp Database / Minimal gene deletions

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

Model : iHN637 [2].
Target metabolite : btd_RR_e
List of minimal gene deletion strategies (Download)

Gene deletion strategy (11 of 16: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 15
  Gene deletion: CLJU_RS05830 CLJU_RS08100 CLJU_RS08095 CLJU_RS09865 CLJU_RS09915 CLJU_RS20875 CLJU_RS02035 CLJU_RS01155 CLJU_RS07210 CLJU_RS03475 CLJU_RS18510 CLJU_RS08715 CLJU_RS15865 CLJU_RS11985 CLJU_RS10705   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.111247 (mmol/gDw/h)
  Minimum Production Rate : 2.184024 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_fru_e : 5.000000
  EX_nh4_e : 0.847314
  EX_pi_e : 0.128896
  EX_k_e : 0.021268
  EX_so4_e : 0.021007
  EX_fe2_e : 0.001538
  EX_mg2_e : 0.000945
  EX_ca2_e : 0.000567
  EX_cl_e : 0.000567
  EX_fol_e : 0.000099
  EX_cu2_e : 0.000077
  EX_mn2_e : 0.000075
  EX_ribflv_e : 0.000050
  EX_zn2_e : 0.000037
  EX_ni2_e : 0.000035
  EX_cobalt2_e : 0.000028
  EX_thm_e : 0.000025

Product: (mmol/gDw/h)
  EX_co2_e : 8.199799
  EX_etoh_e : 4.108083
  EX_h2o_e : 4.043452
  EX_btd_RR_e : 2.184024
  EX_h_e : 0.618400
  DM_succ_c : 0.014067
  EX_ala__D_e : 0.003161
  DM_mththf_c : 0.000248

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] Orth, J. D., Fleming, R. M., Palsson, B. Ø. (2010). Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal plus, 4(1).
[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
Contact