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The finding and researching algorithm for potentially oscillating enzymatic systems
T N Lakhova1, F V Kazantsev1, S A Lashin2
1Kurchatov Genomics Center of ICG SB RAS, Novosibirsk, Russia.
Vavilovskii Zhurnal Genetiki I Selektsii
|December 13, 2021
Summary
Biological systems exhibit oscillations from molecular to population levels. This study introduces mathematical models and computational methods to identify and analyze these oscillatory behaviors, focusing on gene networks and enzymatic systems.
Area of Science:
- Systems Biology
- Computational Biology
- Biophysics
Background:
- Biological processes across all organizational levels, from molecular-genetic to ecological, exhibit periodic oscillations.
- These oscillations are fundamental to vital functions like cell cycles, circadian rhythms, and population dynamics.
- Mathematical and computer modeling are essential tools for understanding complex oscillating biological systems.
Purpose of the Study:
- To present classical mathematical models describing oscillatory behavior in biological systems.
- To detail methods for identifying oscillatory molecular-genetic and enzymatic systems.
- To analyze the role of feedback in gene networks for generating cyclic dynamics.
Main Methods:
- Utilizing structural models (graphs) of gene networks to identify potential oscillating contours.
- Reconstructing mathematical models from structural blueprints for computational analysis.
- Performing numerical simulations to confirm stable limit cycles in identified network structures.
- Applying these methods to a large-scale metabolic network of Escherichia coli.
Main Results:
- Identified key factors influencing cyclic dynamics in biological systems, emphasizing the critical role of feedback in gene networks.
- Developed and applied a step-by-step methodology for analyzing gene networks and reconstructing mathematical models.
- Demonstrated oscillatory behavior in a specific metabolic pathway (tryptophan biosynthesis) within the Escherichia coli network.
Conclusions:
- Structural and mathematical modeling provides a robust framework for discovering and analyzing oscillatory behaviors in biological systems.
- The presented methods enable the identification of feedback loops crucial for generating cyclic dynamics at the molecular level.
- Computational analysis of biological networks, exemplified by E. coli, can reveal fundamental oscillatory mechanisms.
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