Petri Net Recommender System to Model Metabolic Pathway of Polyhydroxyalkanoates

Petri Net Recommender System to Model Metabolic Pathway of Polyhydroxyalkanoates

Sakshi Gupta (Amity University Haryana, Gurugram, India), Gajendra Pratap Singh (School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India) and Sunita Kumawat (Amity University Haryana, Gurugram, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJKSS.2019040103

Abstract

Due to the complexity of the metabolic pathways, their modeling has been a great challenge for the researchers. Various mathematical models have been developed and are continuing to be developed to model and study metabolic pathways. In this article, the authors have described Petri nets (PNs) as a recommender system to model one of the metabolic pathways of Polyhydroxyalkanoates. Recommender systems have become an integral part of today's world. Their applications lie in the fields of e-commerce, bioinformatics and many more. Petri nets are one of the promising mathematical tools which can be treated as a recommender system to model and analyze the complex metabolic pathways of various organisms because of the representation of these pathways as graphs. The PN toolbox validates the obtained Petri net model. Polyhydroxyalkanoates, commonly known as PHAs, are biodegradable microbial polyesters and have properties quite similar to commonly used non-biodegradable plastics. Due to their biodegradability, biocompatibility, and eco-friendly nature, they are of biological significance and are used in various applications in wide range of sectors like medical sector, drug delivery, tissue engineering, and many more. The obtained PN model of Polyhydroxyalkanoates has been validated using PN toolbox.
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Introduction

Petri nets are a graphical and mathematical tool used for modelling and studying concurrent, parallel, distributed and discrete event systems (Murata, 1989). Till date they have been successfully applied to various knowledge-based systems like biological networks, communication networks, industrial systems etc. due to their ability to represent any graph-based systems (Murata, 1989; Chaouiya, 2007; Marwan, Wagler & Weismantel, 2011; Reddy, Mavrovouniotis, & Liebman, 1993).

Various approaches have been applied to model the biological networks and Petri nets have been proved to be a promising and effective tool for the modelling and analysis of biological networks. In 1962, a German mathematician Carl Adam Petri first introduced the concept of Petri nets in his Doctoral Dissertation 'Communication with Automata' as a graphical and mathematical tool (Petri, 1966). Several theories from the authors from different backgrounds in different timeframe are available in the literature. For example, most recent proposed theory for 1-safe petri net is Boolean petri net (Kansal, Acharya & Singh, 2012; Kansal, Singh & Acharya, 2010, 2011, 2015; Singh, Kansal & Acharya, 2013). Apart from theory part, modelling and study of biological networks using PNs have been and are continuing to be a growing interest among the researchers due to the power of Petri nets to model complex situations. Various extensions of Petri nets such as hybrid PNs, stochastic PNs, colored PNs and hybrid functional PNs have also been used to model different types of biological networks (Chaouiya, 2007; Hardy & Robillard, 2004).

The metabolic networks play an important part in an organism’s life since its life is dependent on its metabolism. The metabolic pathways can be regarded as subsystems of a metabolic network. A metabolic pathway is an interlinked network of biochemical reactions, catalyzed by enzymes. In these series of reactions, the output of a reaction can become the reactant for next reaction. To intuitively understand the behavior of such complex reactions, both qualitative and quantitative modelling of metabolic pathways is much needed.

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