2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2014
ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applicat... more ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for different computational analyses in the field of Life Sciences. In this work we present a CUDA-powered computational tool, named coagSODA, that was purposely developed and applied for the analysis of a large model of the blood coagulation cascade defined as a system of ordinary differential equations, based on both mass-action kinetics and Hill functions. We discuss the biological results of the parameter sweep analyses of this model, and show that GPUs can boost the computational performances up to 177x speedup.
The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific appli... more The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a...
2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2014
ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applicat... more ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for different computational analyses in the field of Life Sciences. In this work we present a CUDA-powered computational tool, named coagSODA, that was purposely developed and applied for the analysis of a large model of the blood coagulation cascade defined as a system of ordinary differential equations, based on both mass-action kinetics and Hill functions. We discuss the biological results of the parameter sweep analyses of this model, and show that GPUs can boost the computational performances up to 177x speedup.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion, 2012
ABSTRACT We present a parameter estimation method, based on particle swarm optimization (PSO) and... more ABSTRACT We present a parameter estimation method, based on particle swarm optimization (PSO) and embedding the tau-leaping algorithm, for the efficient estimation of reaction constants in stochastic models of biological systems, using as target a set of discrete-time measurements of molecular amounts sampled in different experimental conditions. To account for the multiplicity of data, we consider a multi-swarm formulation of PSO. The whole method is developed for GPGPU architecture to reduce the computational costs.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012
ABSTRACT Parameter estimation (PE) of biological systems is one of the most challenging problems ... more ABSTRACT Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.
2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013
ABSTRACT The modeling of biochemical reaction networks is a fundamental but complex task in Syste... more ABSTRACT The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Because of the general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse engineering methodologies is currently underway. This problem is further complicated by the fact that a proper parameterization needs to be associated to the reaction network, in order to investigate its dynamical behavior. In this work we propose a novel computational methodology for the reverse engineering of fully parameterized kinetic networks, based on the combined use of two evolutionary programming techniques: Cartesian Genetic Programming (CGP) and Particle Swarm Optimization (PSO). In particular, CGP is used to infer the network topology, while PSO performs the parameter estimation task. To the purpose of applying our methodology in routine laboratory environments, we designed it to exploit a small set of experimental time series as target. We show that our methodology is able to reconstruct kinetic networks that perfectly fit with the target data.
Proceedings of the 2013 International Conference on High Performance Computing and Simulation, HPCS 2013, 2013
ABSTRACT The reverse engineering (RE) of biochemical reaction networks is a fundamental and very ... more ABSTRACT The reverse engineering (RE) of biochemical reaction networks is a fundamental and very complex task in Systems Biology. My PhD thesis is focused on the definition of an automatic RE methodology based on the fusion of Genetic Programming and Particle Swarm Optimization. The methodology I propose relies on the execution of a massive number of simulations, whose computational costs are relevant. To the aim of reducing the overall running time, I am implementing the methodology on a parallel architecture, namely, Nvidia's CUDA.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013
ABSTRACT The computational investigation of a biological system often requires the execution of a... more ABSTRACT The computational investigation of a biological system often requires the execution of a large number of simulations to analyze its dynamics, and to derive useful knowledge on its behavior under physiological and perturbed conditions. This analysis usually turns out into very high computational costs when simulations are run on central processing units (CPUs), therefore demanding a shift to the use of high-performance processors. In this work we present a simulator of biological systems, called cupSODA, which exploits the higher memory bandwidth and computational capability of graphics processing units (GPUs). This software allows to execute parallel simulations of the dynamics of biological systems, by first deriving a set of ordinary differential equations from reaction-based mechanistic models defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm LSODA. We show that cupSODA can achieve a 112 × speedup on GPUs with respect to equivalent executions of LSODA on CPUs.
In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wid... more In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a 86× speedup on GPUs with respect to equivalent executions of LSODA on the CPU.
The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific appli... more The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.
Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolu... more Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain ... more Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2014
ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applicat... more ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for different computational analyses in the field of Life Sciences. In this work we present a CUDA-powered computational tool, named coagSODA, that was purposely developed and applied for the analysis of a large model of the blood coagulation cascade defined as a system of ordinary differential equations, based on both mass-action kinetics and Hill functions. We discuss the biological results of the parameter sweep analyses of this model, and show that GPUs can boost the computational performances up to 177x speedup.
The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific appli... more The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a...
2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2014
ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applicat... more ABSTRACT The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for different computational analyses in the field of Life Sciences. In this work we present a CUDA-powered computational tool, named coagSODA, that was purposely developed and applied for the analysis of a large model of the blood coagulation cascade defined as a system of ordinary differential equations, based on both mass-action kinetics and Hill functions. We discuss the biological results of the parameter sweep analyses of this model, and show that GPUs can boost the computational performances up to 177x speedup.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion, 2012
ABSTRACT We present a parameter estimation method, based on particle swarm optimization (PSO) and... more ABSTRACT We present a parameter estimation method, based on particle swarm optimization (PSO) and embedding the tau-leaping algorithm, for the efficient estimation of reaction constants in stochastic models of biological systems, using as target a set of discrete-time measurements of molecular amounts sampled in different experimental conditions. To account for the multiplicity of data, we consider a multi-swarm formulation of PSO. The whole method is developed for GPGPU architecture to reduce the computational costs.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012
ABSTRACT Parameter estimation (PE) of biological systems is one of the most challenging problems ... more ABSTRACT Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.
2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013
ABSTRACT The modeling of biochemical reaction networks is a fundamental but complex task in Syste... more ABSTRACT The modeling of biochemical reaction networks is a fundamental but complex task in Systems Biology, which is traditionally performed exploiting human expertise and the available experimental data. Because of the general lack of knowledge on the molecular mechanisms occurring in living cells, an intense research activity focused on the development of reverse engineering methodologies is currently underway. This problem is further complicated by the fact that a proper parameterization needs to be associated to the reaction network, in order to investigate its dynamical behavior. In this work we propose a novel computational methodology for the reverse engineering of fully parameterized kinetic networks, based on the combined use of two evolutionary programming techniques: Cartesian Genetic Programming (CGP) and Particle Swarm Optimization (PSO). In particular, CGP is used to infer the network topology, while PSO performs the parameter estimation task. To the purpose of applying our methodology in routine laboratory environments, we designed it to exploit a small set of experimental time series as target. We show that our methodology is able to reconstruct kinetic networks that perfectly fit with the target data.
Proceedings of the 2013 International Conference on High Performance Computing and Simulation, HPCS 2013, 2013
ABSTRACT The reverse engineering (RE) of biochemical reaction networks is a fundamental and very ... more ABSTRACT The reverse engineering (RE) of biochemical reaction networks is a fundamental and very complex task in Systems Biology. My PhD thesis is focused on the definition of an automatic RE methodology based on the fusion of Genetic Programming and Particle Swarm Optimization. The methodology I propose relies on the execution of a massive number of simulations, whose computational costs are relevant. To the aim of reducing the overall running time, I am implementing the methodology on a parallel architecture, namely, Nvidia's CUDA.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013
ABSTRACT The computational investigation of a biological system often requires the execution of a... more ABSTRACT The computational investigation of a biological system often requires the execution of a large number of simulations to analyze its dynamics, and to derive useful knowledge on its behavior under physiological and perturbed conditions. This analysis usually turns out into very high computational costs when simulations are run on central processing units (CPUs), therefore demanding a shift to the use of high-performance processors. In this work we present a simulator of biological systems, called cupSODA, which exploits the higher memory bandwidth and computational capability of graphics processing units (GPUs). This software allows to execute parallel simulations of the dynamics of biological systems, by first deriving a set of ordinary differential equations from reaction-based mechanistic models defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm LSODA. We show that cupSODA can achieve a 112 × speedup on GPUs with respect to equivalent executions of LSODA on CPUs.
In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wid... more In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a 86× speedup on GPUs with respect to equivalent executions of LSODA on the CPU.
The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific appli... more The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.
Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolu... more Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain ... more Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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Papers by Marco S Nobile
Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions,
which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep
analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.
Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions,
which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep
analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.