Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters

The modeling of dynamic systems is frequently hampered by a limited knowledge of the system to be modeled and by the difficulty of acquiring accurate data. This often results in a number of uncertain system parameters that are hard to incorporate into a mathematical model. Thus, there is a need for modeling formalisms that can accommodate all available data, even if uncertain, in order to employ them and build useful models. This paper shows how the Flexible Nets (FNs) formalism can be exploited to handle uncertain parameters while offering attractive analysis possibilities. FNs are composed of two nets, an event net and an intensity net, that model the relation between the state and the processes of the system. While the event net captures how the state of the system is updated by the processes in the system, the intensity net models how the speed of such processes is determined by the state of the system. Uncertain parameters are accounted for by sets of inequalities associated with both the event net and the intensity net. FNs are not only demonstrated to be a valuable formalism to cope with system uncertainties, but also to be capable of modeling different system features, such as resource allocation and control actions, in a facile manner.

[1]  Donald A. Drew,et al.  Differential Equation Models.@@@Modules in Applied Mathematics. , 1984 .

[2]  Diego Latella,et al.  Continuous approximation of collective system behaviour: A tutorial , 2013, Perform. Evaluation.

[3]  Stefano Di Cairano,et al.  Stochastic Model Predictive Control , 2018, Handbook of Model Predictive Control.

[4]  Carl G. Looney,et al.  Fuzzy Petri nets for rule-based decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[5]  Donald A. Drew,et al.  Differential equation models , 1983 .

[6]  Allan Clark,et al.  Stochastic Process Algebras , 2007, SFM.

[7]  Alessandro Giua,et al.  Fault detection for discrete event systems using Petri nets with unobservable transitions , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[8]  Duygu Dikicioglu,et al.  Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease. , 2018 .

[9]  Robert Clarke,et al.  Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells , 2011, Nature Reviews Cancer.

[10]  P. Merlin,et al.  Recoverability of Communication Protocols - Implications of a Theoretical Study , 1976, IEEE Transactions on Communications.

[11]  Rui-Sheng Wang,et al.  Boolean modeling in systems biology: an overview of methodology and applications , 2012, Physical biology.

[12]  Basil Kouvaritakis,et al.  Model Predictive Control: Classical, Robust and Stochastic , 2015 .

[13]  Andrew J. Bulpitt,et al.  A Primer on Learning in Bayesian Networks for Computational Biology , 2007, PLoS Comput. Biol..

[14]  Antonio Ramírez-Treviño,et al.  Observability of discrete event systems modeled by interpreted Petri nets , 2003, IEEE Trans. Robotics Autom..

[15]  Süleyman Cenk Sahinalp,et al.  Not All Scale-Free Networks Are Born Equal: The Role of the Seed Graph in PPI Network Evolution , 2006, Systems Biology and Computational Proteomics.

[16]  Duygu Dikicioglu,et al.  Handling variability and incompleteness of biological data by flexible nets: a case study for Wilson disease , 2018, npj Systems Biology and Applications.

[17]  Ross D. Shachter,et al.  Influence Diagrams for Team Decision Analysis , 2005, Decis. Anal..

[18]  David L. Woodruff,et al.  Pyomo: modeling and solving mathematical programs in Python , 2011, Math. Program. Comput..

[19]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.

[20]  Jane Hillston,et al.  Bio-PEPA: An Extension of the Process Algebra PEPA for Biochemical Networks , 2007, FBTC@CONCUR.

[21]  Manuel Silva Suárez,et al.  Linear Algebraic and Linear Programming Techniques for the Analysis of Place or Transition Net Systems , 1996, Petri Nets.

[22]  Christel Baier,et al.  Stochastic Timed Automata , 2014, Log. Methods Comput. Sci..

[23]  Wolfgang Borutzky,et al.  Bond Graph Methodology: Development and Analysis of Multidisciplinary Dynamic System Models , 2009 .

[24]  Janette Cardoso,et al.  Possibilistic Petri nets , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Rajeev Alur,et al.  A Theory of Timed Automata , 1994, Theor. Comput. Sci..

[26]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[27]  Corrado Priami,et al.  Application of a stochastic name-passing calculus to representation and simulation of molecular processes , 2001, Inf. Process. Lett..

[28]  Cristian Mahulea,et al.  On fluidization of discrete event models: observation and control of continuous Petri nets , 2011, Discret. Event Dyn. Syst..

[29]  J. E. Rooda,et al.  Modeling and Control of a Manufacturing Flow Line Using Partial Differential Equations , 2008, IEEE Transactions on Control Systems Technology.

[30]  Manuel Silva Suárez,et al.  Relaxed continuous views of discrete event systems: considerations on Forrester diagrams and Petri nets , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[31]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[32]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[33]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.