|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|424504||1440651||2016||14 صفحه PDF||سفارش دهید||دانلود کنید|
• This paper presents MultiGrain/MAPPER—a novel concept, framework and tool for modeling and simulation based on a multiscale computing paradigm.
• MultiGrain/MAPPER has been designed to tackle the computational challenges of large-scale gene-regulatory networks (GRN) model modeling and simulation tasks.
• In particular, MultiGrain/MAPPER realizes a distributed computing solution to reverse-engineering of GRN models from gene-expression data.
• The solution is based on a distributed multi-swarm (multi-island) particle swarm optimization algorithm we implemented, where PSO islands are mapped to CPU cores.
• A detailed evaluation of MultiGrain/MAPPER’s concepts and performance is provided in the paper, with a particular emphasis on the tool’s computational aspects.
Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using both real and artificial gene expression data.
Journal: Future Generation Computer Systems - Volume 63, October 2016, Pages 1–14