MetNetComp Database [1] / Minimal gene deletions Minimal gene deletions for simulation-based growth-coupled production. You can also see maximal gene deletions.

Model : iMM904 [2].
Target metabolite : dmlz_c
List of minimal gene deletion strategies (Download)

Gene deletion strategy (162 of 163: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 29
  Gene deletion: YBR069C YPL061W YML035C YNL270C YEL063C YER170W YLR089C YHR002W YCL025C YKR039W YOR120W YLL043W YBR192W YPL262W YIL155C YMR189W YGR191W YJR077C YHR208W YJR078W YKL174C YJL121C YLR348C YBR006W YDL085W YMR145C YPL188W YJR049C YEL041W   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.191659 (mmol/gDw/h)
  Minimum Production Rate : 0.001864 (mmol/gDw/h)

Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_nh4_e : 1.137835
  EX_o2_e : 0.212222
  EX_pi_e : 0.059855
  EX_so4_e : 0.014815

Product: (mmol/gDw/h)
  EX_co2_e : 17.171827
  EX_etoh_e : 17.034612
  EX_h2o_e : 2.982460
  EX_h_e : 1.859592
  EX_succ_e : 0.394088
  EX_2hb_e : 0.037377
  EX_pap_e : 0.010982
  EX_for_e : 0.004719
  EX_gly_e : 0.002859
  Auxiliary production reaction : 0.001864

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: 27-Sep-2023
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