k-sacce306

This repository provides the kinetic models k-sacce306-BY4741 and k-sacce306-CENPK that accompanies the following manuscript:

• Mengqi Hu, Hoang V. Dinh, Yihui Shen, Patrick F. Suthers, Charles J. Foster, Catherine M. Call, Xuanjia Ye, Jimmy Pratas, Zia Fatma, Huimin Zhao, Joshua D. Rabinowitz, Costas D. Maranas "Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale." Metab. Eng., 2023, 76:1-17, https://doi.org/10.1016/j.ymben.2023.01.001

Kinetic models are parameterized using the Kinetic Fluxomics Integration Tool (K-FIT). The primary K-FIT repository can be found at https://github.com/maranasgroup/K-FIT (https://doi.org/10.1016/j.ymben.2020.03.001).

Run instructions

Input files required are:

• K-FIT-master folder
• 502_data_BY4741.xlsx or 502_data_CENPK.xlsx ( this is your fluxomics data)
• 502_model.xlsx ( this is your stoichiometric model file)
• 502_mech.xlsx ( this is your regulation mechanism file)
• parallel_run_all_BY4741.m or parallel_run_all_CENPK.m ( matlab run file to start kinetic parameterization)

Output files:

• optimal.mat ( this file will contain the res structure, the model used, and time value of the parameterization process)

results structure (res) the MATLAB res structure contains information for the kinetic parameterization:

• fmin: SSR value
• kinetic_params: kf, kr, ki predicted parameters
• predictions: structure contains predictions for fluxes and relative metabolite concentrations to wild-type (WT)

K-FIT kinetic models for Saccharomyces cerevisiae CEN.PK and BY4 are found in the 'CEN solutions' and 'BY4741 solutions' folders.

Notes about input fluxomics data

• Fluxomics data requires the WT flux distribution and at least one single-gene deletion flux distribution.
• K-FIT can converge to a stable solution more frequently if the flux distribution data is scaled to 100 WT glucose uptake rate
• It is possible to use data with more than one single-gene deletion and over- or under-expression (levels of expression needs to be specified, and assuming level of over- or under- expression as a proxy for enzyme levels) but would require manual code additions to the matlab run file (i.e., parallel_run_all*.m). User would need to modify the model.v.pert(x,y) matrix to specify relative enzyme concentrations, where x is the reaction index number and y is the pertrubed dataset column number. For an example, please see the run_kctherm138.m file at https://github.com/maranasgroup/k-ctherm138 (https://doi.org/10.1016/j.ymben.2021.12.012)

Funding

This work was funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018420). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Funding also provided by the DOE Office of Science, Office of Biological and Environmental Research (Award Number DE-SC0018260) Computations for this research were performed on the Pennsylvania State University's Institute for Computational and Data Sciences' Roar supercomputer.