# A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions

## TL;DR

This paper introduces a new framework to study how multiple reactions in metabolism are linked, revealing how these links could be used to target cancer cells without harming healthy ones.

## Contribution

The paper introduces the concept of 'forcedly balanced complexes' to analyze multireaction dependencies in metabolic networks.

## Key findings

- Multireaction dependencies in genome-scale networks follow a power law with exponential cut-off.
- Forcedly balanced complexes are lethal to cancer cells but not to healthy tissue models.
- These complexes are specific to certain cancer types and can be targeted via transporter engineering.

## Abstract

Metabolism operates under physico-chemical constraints that result in multireaction dependencies. Understanding how multireaction dependencies affect metabolic phenotypes remains challenging, hindering their biotechnological applications. Here, we propose the concept of a forcedly balanced complex that allows to efficiently determine the effects of specific multireaction dependencies on metabolic network functions in constrained-based models. Using this concept, we found that the fraction of multireaction dependencies induced by forcedly balanced complexes in genome-scale metabolic networks followed power law with exponential cut-off. We identified forcedly balanced complexes that are lethal in cancer but have little effect on growth in healthy tissue models. In addition, these forcedly balanced complexes are largely specific to models of particular cancer types. Therefore, multireaction dependencies resulting from forced balancing of complexes represent an innovative means to control cancers that, we argue, can be implemented via transporter engineering. The presented constraint-based approaches pave the way for using multireaction dependencies in metabolic engineering for diverse biotechnological applications.