research-article

Overcoming the lack of kinetic information in biochemical reactions networks

Authors:

Niccoló Totis,

Chiara Riganti,

Francesco Novelli,

Francesca Cordero,

Marco BeccutiAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 44, Issue 4

Pages 91 - 102

https://doi.org/10.1145/3092819.3092830

Published: 10 May 2017 Publication History

Abstract

A main aspect in computational modelling of biological systems is the determination of model structure and model parameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse engineering and parameter estimation methods have been proposed in the literature, however these methods often need an amount of experimental data not always available.

In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical biological knowledge and does not require large amounts of data. The system behaviour is described by a set of Ordinary Differential Equations (ODE). Model indetermination is resolved selecting time-varying coefficients that maximize/ minimize the objective function at each ODE integration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Finally, the approach is illustrated on a case study focused on cancer metabolism.

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Cited By

Grelck CNiewiadomska-Szynkiewicz EAldinucci MBracciali ALarsson E(2019)Why High-Performance Modelling and Simulation for Big Data Applications MattersHigh-Performance Modelling and Simulation for Big Data Applications10.1007/978-3-030-16272-6_1(1-35)Online publication date: 26-Mar-2019
https://doi.org/10.1007/978-3-030-16272-6_1
Totis NTangherloni ABeccuti MCazzaniga PNobile MBesozzi DPennisi MPappalardo F(2018)Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms CharacterizationComputational Intelligence Methods for Bioinformatics and Biostatistics10.1007/978-3-030-34585-3_17(187-202)Online publication date: 6-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-34585-3_17

Overcoming the lack of kinetic information in biochemical reactions networks
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 44, Issue 4

March 2017

101 pages

ISSN:0163-5999

DOI:10.1145/3092819

Editor:
Nidhi Hegde
Bell Labs, Nokia, Nozay, France

Issue’s Table of Contents

Copyright © 2017 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 May 2017

Published in SIGMETRICS Volume 44, Issue 4

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Cited By

Grelck CNiewiadomska-Szynkiewicz EAldinucci MBracciali ALarsson E(2019)Why High-Performance Modelling and Simulation for Big Data Applications MattersHigh-Performance Modelling and Simulation for Big Data Applications10.1007/978-3-030-16272-6_1(1-35)Online publication date: 26-Mar-2019
https://doi.org/10.1007/978-3-030-16272-6_1
Totis NTangherloni ABeccuti MCazzaniga PNobile MBesozzi DPennisi MPappalardo F(2018)Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms CharacterizationComputational Intelligence Methods for Bioinformatics and Biostatistics10.1007/978-3-030-34585-3_17(187-202)Online publication date: 6-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-34585-3_17

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