Kinetic modules in biochemical networks/ Upstream Algorithm

Modules represent fundamental building blocks of cellular networks, and are thought to facilitate robustness of phenotypes against perturbations. While reaction kinetic shapes the concentration of components and reaction rates, its use in identification of modules entails knowledge of parameter values. Here we demonstrate that kinetic modules can be efficiently identified based on steady-state reaction rate couplings in large-scale biochemical networks endowed with mass action kinetics without knowledge of parameter values. We then link the kinetic modules of metabolic networks with robustness of metabolite concentrations to perturbations. Analyzing 34 metabolic network models of 26 organisms, we demonstrate that the ordered binding enzyme mechanism leads to increased concentration robustness compared to random binding. Our findings pave the way for usage of modules in synthetic biology and biotechnological applications.  , , , # Kinetic modules in biochemical networks/ Upstream Algorithm

[https://doi.org/10.5061/dryad.7pvmcvf4v](https://doi.org/10.5061/dryad.7pvmcvf4v)

The repository contains code, data, and (intermediate) results that allow the identification of

* balanced complexes
* concordant complexes
* kinetic modules

The identification of kinetic modules allows one to find metabolites of absolute concentration robustness and pairs with absolute concentration ratio robustness in large-scale metabolic networks.

## Dependencies

* Matlab (tested with 2023b / 2024a)
* R (tested with R-4.3.0), packages igraph and R.matlab
* COBRA toolbox ([https://opencobra.github.io/cobratoolbox/stable/index.html](https://opencobra.github.io/cobratoolbox/stable/index.html))

to compare result with full coupling based on stoichiometry
* F2C2 tool ([https://pubmed.ncbi.nlm.nih.gov/22524245/](https://pubmed.ncbi.nlm.nih.gov/22524245/)) (used with glpk)

## Main functions and scripts

Extract the Upstream_Algorithm_Dr...