Mathematical modeling and computational analyses are essential tools to understand and gain novel insights on the functioning of complex biochemical systems. In the specific case of metabolic reaction networks, which are regulated by many other intracellular processes, various challenging problems hinder the definition of compact and fully calibrated mathematical models, as well as the execution of computationally efficient analyses of their emergent dynamics. These problems especially occur when the model explicitly takes into account the presence and the effect of different isoforms of metabolic enzymes. Since the kinetic characterization of the different isoforms is most of the times unavailable, Parameter Estimation (PE) procedures are typically required to properly calibrate the model. To address these issues, in this work we combine the descriptive power of Stochastic Symmetric Nets, a parametric and compact extension of the Petri Net formalism, with FST-PSO, an efficient and settings-free meta-heuristics for global optimization that is suitable for the PE problem. To prove the effectiveness of our modeling and calibration approach, we investigate here a large-scale kinetic model of human intracellular metabolism. To efficiently execute the large number of simulations required by PE, we exploit LASSIE, a deterministic simulator that offloads the calculations onto the cores of Graphics Processing Units, thus allowing a drastic reduction of the running time. Our results attest that estimating isoform-specific kinetic parameters allows to predict how the knock-down of specific enzyme isoforms affects the dynamic behavior of the metabolic network. Moreover, we show that, thanks to LASSIE, we achieved a speed-up of ~30× with respect to the same analysis carried out on Central Processing Units.

Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization

Totis N.
;
Beccuti M.
;
2020-01-01

Abstract

Mathematical modeling and computational analyses are essential tools to understand and gain novel insights on the functioning of complex biochemical systems. In the specific case of metabolic reaction networks, which are regulated by many other intracellular processes, various challenging problems hinder the definition of compact and fully calibrated mathematical models, as well as the execution of computationally efficient analyses of their emergent dynamics. These problems especially occur when the model explicitly takes into account the presence and the effect of different isoforms of metabolic enzymes. Since the kinetic characterization of the different isoforms is most of the times unavailable, Parameter Estimation (PE) procedures are typically required to properly calibrate the model. To address these issues, in this work we combine the descriptive power of Stochastic Symmetric Nets, a parametric and compact extension of the Petri Net formalism, with FST-PSO, an efficient and settings-free meta-heuristics for global optimization that is suitable for the PE problem. To prove the effectiveness of our modeling and calibration approach, we investigate here a large-scale kinetic model of human intracellular metabolism. To efficiently execute the large number of simulations required by PE, we exploit LASSIE, a deterministic simulator that offloads the calculations onto the cores of Graphics Processing Units, thus allowing a drastic reduction of the running time. Our results attest that estimating isoform-specific kinetic parameters allows to predict how the knock-down of specific enzyme isoforms affects the dynamic behavior of the metabolic network. Moreover, we show that, thanks to LASSIE, we achieved a speed-up of ~30× with respect to the same analysis carried out on Central Processing Units.
15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018
prt
2018
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Science and Business Media Deutschland GmbH
11925
187
202
978-3-030-34584-6
978-3-030-34585-3
GPU-powered simulations; Metabolic reaction networks; Parameter Estimation
Totis N.; Tangherloni A.; Beccuti M.; Cazzaniga P.; Nobile M.S.; Besozzi D.; Pennisi M.; Pappalardo F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1881130
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