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 (163 of 163: See next) for growth-coupled production (at least stoichioemetrically feasible)
  Gene deletion size : 32
  Gene deletion: YBR069C YNL270C YEL063C YPL148C YAL054C YCR032W YCR010C YBR085W YMR056C YBL030C YLR089C YCL025C YML042W YKR039W YEL047C YOR120W YLL043W YMR189W YGR191W YJR077C YHR208W YJR078W YKL174C YJL121C YLR348C YLR017W YPL188W YJR049C YEL041W YER086W YOR377W YGR177C   (List of alternative genes)
  Computed by: RandTrimGdel [1] (Step 1, Step 2)

When growth rate is maximized,
  Growth Rate : 0.180196 (mmol/gDw/h)
  Minimum Production Rate : 0.021918 (mmol/gDw/h)

 Substrate: (mmol/gDw/h)
  EX_glc__D_e : 10.000000
  EX_nh4_e : 1.170614
  EX_o2_e : 0.100785
  EX_pi_e : 0.056275
  EX_so4_e : 0.013929

Product: (mmol/gDw/h)
  EX_co2_e : 17.494955
  EX_etoh_e : 17.218373
  EX_h2o_e : 2.802968
  EX_h_e : 1.391611
  EX_2hb_e : 0.278254
  EX_for_e : 0.044769
  EX_gly_e : 0.022854
  Auxiliary production reaction : 0.021918
  EX_pap_e : 0.010325

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.

Last updated: 27-Sep-2023
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