Hybrid dynamic/static method for large-scale simulation of metabolism

Katsuyuki Yugi, Yoichi Nakayama, Ayako Kinoshita, Masaru Tomita

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Background: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. Results: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation. Conclusion: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.

Original languageEnglish
Article number42
JournalTheoretical Biology and Medical Modelling
Volume2
DOIs
Publication statusPublished - 2005 Oct 4

Fingerprint

Metabolic Flux Analysis
Metabolism
Pathway
Dynamic Simulation
Metabolic Networks and Pathways
Fluxes
Computer simulation
Kinetics
Simulation
Hybrid Method
Dynamic models
Dynamic Model
Module
Snapshot
Hybrid Model
Kinetic Model
Assays
Simulation Methods
Dynamic Behavior
Enzymes

ASJC Scopus subject areas

  • Health Informatics
  • Medicine(all)

Cite this

Hybrid dynamic/static method for large-scale simulation of metabolism. / Yugi, Katsuyuki; Nakayama, Yoichi; Kinoshita, Ayako; Tomita, Masaru.

In: Theoretical Biology and Medical Modelling, Vol. 2, 42, 04.10.2005.

Research output: Contribution to journalArticle

@article{34dacb9f16ed40faae76e3d980bc8d6a,
title = "Hybrid dynamic/static method for large-scale simulation of metabolism",
abstract = "Background: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. Results: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation. Conclusion: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.",
author = "Katsuyuki Yugi and Yoichi Nakayama and Ayako Kinoshita and Masaru Tomita",
year = "2005",
month = "10",
day = "4",
doi = "10.1186/1742-4682-2-42",
language = "English",
volume = "2",
journal = "Theoretical Biology and Medical Modelling",
issn = "1742-4682",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Hybrid dynamic/static method for large-scale simulation of metabolism

AU - Yugi, Katsuyuki

AU - Nakayama, Yoichi

AU - Kinoshita, Ayako

AU - Tomita, Masaru

PY - 2005/10/4

Y1 - 2005/10/4

N2 - Background: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. Results: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation. Conclusion: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.

AB - Background: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. Results: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation. Conclusion: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.

UR - http://www.scopus.com/inward/record.url?scp=26844531259&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=26844531259&partnerID=8YFLogxK

U2 - 10.1186/1742-4682-2-42

DO - 10.1186/1742-4682-2-42

M3 - Article

C2 - 16202166

AN - SCOPUS:26844531259

VL - 2

JO - Theoretical Biology and Medical Modelling

JF - Theoretical Biology and Medical Modelling

SN - 1742-4682

M1 - 42

ER -