Paper Info
Title
Global Sensitivity Analysis Of Constraint-Based Metabolic Models
Abstract
In the latter years, detailed genome-wide metabolic models have been proposed, paving the way to thorough investigations of the connection between genotype and phenotype in human cells. Nevertheless, classic modeling and dynamic simulation approaches-based either on differential equations integration, Markov chains or hybrid methods-are still unfeasible on genome-wide models due to the lack of detailed information about kinetic parameters and initial molecular amounts. By relying on a steady-state assumption and constraints on extracellular fluxes, constraint-based modeling provides an alternative means-computationally less expensive than dynamic simulation-for the investigation of genome-wide biochemical models. Still, the predictions provided by constraint-based analysis methods (e.g., flux balance analysis) are strongly dependent on the choice of flux boundaries. To contain possible errors induced by erroneous boundary choices, a rational approach suggests to focus on the pivotal ones. In this work we propose a novel methodology for the automatic identification of the key fluxes in large-scale constraint-based models, exploiting variance-based sensitivity analysis and distributing the computation on massively multi-core architectures. We show a proof-of-concept of our approach on core models of relatively small size (up to 314 reactions and 256 chemical species), highlighting the computational challenges.
Year
DOI
Venue
2018
10.1007/978-3-030-34585-3_16
COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2018
Keywords
Field
DocType
Flux Balance Analysis, Constraint-Based Modeling, Global sensitivity analysis, MPI, Linear Programming
Differential equation,Mathematical optimization,Computer science,Markov chain,Artificial intelligence,Linear programming,Global sensitivity analysis,Constraint based modeling,Machine learning,Dynamic simulation,Flux balance analysis,Computation
Conference
Volume
ISSN
Citations 
11925
0302-9743
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Chiara Damiani110.69
Dario Pescini227425.92
Marco S. Nobile314323.69