Using Machine Learning Techniques for Modelling and Simulation of Metabolic Networks

M. Biba, F. Xhafa, F. Esposito, S. Ferilli
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引用次数: 2

Abstract

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an intagrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deals with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network.
使用机器学习技术建模和模拟代谢网络
代谢组学正日益成为一个重要的研究领域。该领域的基本任务是测量和解释复杂的时间和条件依赖参数,如细胞中代谢物的活性或通量,它们的浓度,组织元素和其他生物样品。对所有这些因素的仔细研究导致了对新陈代谢功能的重要见解。然而,最近人们对研究生物系统的综合方法越来越感兴趣。这是系统生物学的主要目标,在系统生物学中,对生物系统的几个组成部分的综合研究被认为可以产生对这些系统的彻底理解。代谢网络不仅结构复杂,而且行为也具有随机性。因此,有必要通过表达结构和处理不确定性来构建这些网络的完整动力学。在本文中,我们描述了如何在符号统计机器学习(ML)框架中执行随机建模和模拟。我们展示了符号机器学习处理结构和关系复杂性,而统计机器学习提供了不确定性建模的原则方法。学习用于分析生化反应的轨迹,并通过参数学习建立动态模型,而推理用于对网络进行随机模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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