Paper Info
Title
Integrating Petri Nets and Flux Balance Methods in Computational Biology Models: a Methodological and Computational Practice.
Abstract
Computational Biology is a fast-growing field that is enriched by different data-driven methodological approaches and by findings and applications in a broad range of biological areas. Fundamental to these approaches are the mathematical and computational models used to describe the different states at microscopic (for example a biochemical reaction), mesoscopic (the signalling effects at tissue level), and macroscopic levels (physiological and pathological effects) of biological processes. In this paper we address the problem of combining two powerful classes of methodologies: Flux Balance Analysis (FBA) methods which are now producing a revolution in biotechnology and medicine, and Petri Nets (PNs) which allow system generalisation and are central to various mathematical treatments, for example Ordinary Differential Equation (ODE) specification of the biosystem under study. While the former is limited to modelling metabolic networks, i.e. does not account for intermittent dynamical signalling events, the latter is hampered by the need for a large amount of metabolic data. A first result presented in this paper is the identification of three types of cross-talks between PNs and FBA methods and their dependencies on available data. We exemplify our insights with the analysis of a pancreatic cancer model. We discuss how our reasoning framework provides a biologically and mathematically grounded decision making setting for the integration of regulatory, signalling, and metabolic networks and greatly increases model interpretability and reusability. We discuss how the parameters of PN and FBA models can be tuned and combined together so to highlight the computational effort needed to perform this task. We conclude with speculations and suggestions on this new promising research direction.
Year
DOI
Venue
2020
10.3233/FI-2020-1888
FUNDAMENTA INFORMATICAE
Keywords
Field
DocType
Computational biology,Petri Nets and Flux Balance Analysis
Discrete mathematics,Petri net,Theoretical computer science,Flux,Mathematics
Journal
Volume
Issue
ISSN
171
SP1-4
0169-2968
Citations 
PageRank 
References 
1
0.38
0
Authors
10
Name
Order
Citations
PageRank
Simone Pernice111.40
Laura Follia210.38
Gianfranco Balbo31807243.48
Luciano Milanesi482387.40
Giulia Sartini510.38
Niccoló Totis610.38
Pietro Liò755099.98
Ivan Merelli829435.36
Francesca Cordero96313.42
Marco Beccuti1019526.04