Genetic-metabolic networks can be modeled as toric varieties

TL;DR

This paper demonstrates that genetic-metabolic networks modeled as S-Systems are toric varieties, enabling the use of toric algebraic geometry tools like Hilbert bases to analyze large-scale biological networks more effectively.

Contribution

It proves S-Systems are toric varieties and introduces methods to solve them using toric algebraic geometry, expanding analysis capabilities in systems biology.

Findings

S-Systems are toric varieties.

Hilbert basis can solve S-Systems.

Identified invariant oscillatory regions.

Abstract

The mathematical modelling of genetic-metabolic networks is of out most importance in the field of systems biology. Different formalisms and a huge variety of classical mathematical tools have been used to describe and analyse such networks. Michael A. Savageau defined a formalism to model genetic-metabolic networks called S-Systems, [12], [13], [15]. There is a limit in the number of nodes that can be analysed when these systems are solved using classical numerical methods such as non-linear dynamic analysis and linear optimization algorithms. We propose to use toric algebraic geometry to solve S-systems. In this work we prove that S-systems are toric varieties and that as a consequence Hilbert basis can be used to solve them. This is achieved by applying two theorems, proved here, the theorem about Embedding of S-Systems in toric varieties and the theorem about Toric Resolution on S-Systems. In addition, we define the realization of minimal phenotypic polytopes, phenotypic toric ideals and phenotypic toric varieties as a generalization of the phenotypic polytopes described by M. Savageau, [8], [9], [10]. Also we studied a synthetic oscillatory network of two genes under diverse environmental control modes, we reproduce S. Savageau results and additionally we obtain an invariant sustained oscillatory region. The implications of the results here presented is that, in principle, they will facilitate solving large scale genetic-metabolic networks by means of toric algebraic geometry tools as well as facilitate elucidating their dynamics.