Italy 2Department of Computer Science, University of Pisa, Pisa, Italy Correspondence Luca Marchetti, The Microsoft Research— University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto TN, Italy. Email: marchetti@cosbi.eu | Corrado Priami1,2 | Luca Marchetti1 Abstract Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypoth- eses to guide the design of new experimental tests, and ultimately assess the suitabil- ity of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to compu- tational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon.

Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods

C. Priami;
2019-01-01

Abstract

Italy 2Department of Computer Science, University of Pisa, Pisa, Italy Correspondence Luca Marchetti, The Microsoft Research— University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto TN, Italy. Email: marchetti@cosbi.eu | Corrado Priami1,2 | Luca Marchetti1 Abstract Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypoth- eses to guide the design of new experimental tests, and ultimately assess the suitabil- ity of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to compu- tational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon.
2019
Simmoni, G.; Reali, F.; Priami, C.; Marchetti, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/994817
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