Supply chain modelling and its analytical evaluation

A model-based analysis of supply chains (SCs) is proposed as a means of estimating performance measures like lead times and resource utilisations. SCs are regarded as discrete event dynamic systems (DEDS), described as process chains in an application-specific formalism, and analysed by state-of-the-art techniques developed for queueing networks (QNs) and Petri nets (PNs). The theoretical approach is accompanied by a toolset that provides automatic translations between a process-chain-specification front end and analysis engines for QN and PN models. In addition, the analysis technique also supports a hybrid approach, in which dedicated sub-models are mapped into a PN model, numerically analysed, and replaced with an aggregate. Finally, the resulting QN models may be analysed using analytical as well as simulative techniques. As an example application, we investigate the impact of an additional SC channel between a manufacturer and web-consumers on the overall performance of an SC. The example was inspired by the trend towards retail trade via the internet and illustrates the new challenges of electronic commerce for SC management.     

Some contributions with Petri nets for the modelling, analysis and control of HDS

Petri nets (PN) are useful for the modelling, analysis and control of hybrid dynamical systems (HDS) because PN combine in a comprehensive way discrete events and continuous behaviours. On one hand, PN are suitable for modelling the discrete part of HDS and for providing a discrete abstraction of continuous behaviours. On the other hand, continuous PN are suitable for modelling the continuous part of HDS and for working out a continuous approximation of the discrete part in order to avoid the complexity associated with the exponential growth of discrete states. This paper focuses on the advantages of PN as a modelling tool for HDS. Investigations of such models for diagnosis and control issues are detailed.

Taking inspiration from the discrete event approach, sensor selection for diagnosis is discussed according to the structural analysis of the PN models. Faults are represented with fault transitions and a faulty behaviour occurs when a sequence of transitions is fired that contains at least one fault transition. Minimal sets of observable places are defined for detecting and isolating faulty behaviours.

Taking inspiration from the continuous time approach, flow control of HDS modelled with continuous PN is also investigated. Gradient-based controllers are introduced in order to adapt the firing speeds of some controllable transitions according to a desired trajectory of the marking. The equilibria and stability of the controlled system are studied with Lyapunov functions.     

Identification of predictors of Boolean networks from observed attractor states

Predictors of Boolean networks are of significance for biologists to target their research on gene regulation and control. This paper aims to investigate how to determine predictors of Boolean networks from observed attractor states by solving logical equations. The proposed method consists of four steps. First, all possible cycles formed by known attractor states are constructed. Then, for each possible cycle, all data‐permitted predictors of each node are identified according to the known attractor states. Subsequently, the data‐permitted predictors are incorporated with some common biological constraints to generate logical equations that describe whether such possible predictors can ultimately be chosen as valid ones by the biological constraints. Finally, solve the logical equations; the solutions determine a family of predictors satisfying the known attractor states. The approach is quite different from others such as computer algorithm‐based and provides a new angle and means to understand and analyze the structures of Boolean networks.     

Limit cycles and update digraphs in Boolean networks

Boolean networks have been used as models of gene regulation and other biological networks, as well as for other kinds of distributed dynamical systems. One key element in these models is the update schedule, which indicates the order in which states have to be updated. In Salinas (2008) [22] and Aracena et al. (2009) [1], equivalence classes of deterministic update schedules according to the labeled digraph associated to a Boolean network (update digraph) were defined and it was proved that two schedules in the same class yield the same dynamical behavior. In this paper, we study the relations between the update digraphs and the preservation of limit cycles of Boolean networks iterated under non-equivalent update schedules. We show that the related problems lie in the class of NP-hard problems and we prove that the information provided by the update digraphs is not sufficient to determine whether two Boolean networks share limit cycles or not. Besides, we exhibit a polynomial algorithm that works as a necessary condition for two Boolean networks to share limit cycles. Finally, we construct some update schedule classes whose elements share a given limit cycle under certain conditions on the frozen nodes of it.     

Dynamic information handling in continuous time Boolean Network model of gene interactions

Growing information and knowledge on gene regulatory networks, which are typical hybrid systems, has led a significant interest in modeling those networks. An important direction of gene network modeling is studying the abstract network models to understand the behavior of a class of systems. Boolean Networks has emerged as an important model class on this direction. Limitations of traditional Boolean Networks led the researchers to propose several generalizations. In this work, one such class, the Continuous Time Boolean Networks (CTBN’s), is studied. CTBN’s are constructed by allowing the Boolean variables evolve in continuous time and involve a biologically-motivated refractory period. In particular, we analyze the basic circuits and subsystems of the class of CTBN’s. We demonstrate the existence of various qualitative dynamic behavior including stable, multistable, neutrally stable, quasiperiodic and chaotic behaviors. We show that those models are capable of demonstrating highly adjustable features like maintenance of continuous protein concentrations. Finally, we discuss the relation between qualitative dynamic features and information handling.     

Modeling and analysis using hybrid Petri nets

This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS attract ever more of researchers’ attention and several works have been devoted to these topics. We consider in this paper the extensions of the PN formalism (initially conceived for modeling and analysis of discrete event systems) in the direction of hybrid modeling. We present, first, the continuous PN models. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Then, we present two hybrid PN models, which differ in the class of HDS they can deal with. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior.     

Stability analysis of activation-inhibition Boolean networks with stochastic function structures

This paper analyzes the stability of activation-inhibition Boolean networks with stochastic function structures. First, the activation-inhibition Boolean networks with stochastic function structures are converted to the form of logical networks by the method of semitensor product of matrices. Second, based on the obtained algebraic forms, we use matrices to denote the index set of possible logical operators and transition probabilities for activation-inhibition Boolean networks. Third, equivalence criterions are presented for the stabilities analysis of activation-inhibition Boolean networks with stochastic function structures. Finally, an example is given to verify the validity of the results.     

What is a universal computing machine?

A computer is classically formalised as a universal Turing machine or a similar device. However over the years a lot of research has focused on the computational properties of dynamical systems other than Turing machines, such cellular automata, artificial neural networks, mirrors systems, etc.In this paper we propose a unifying formalism derived from a generalisation of Turing’s arguments. Then we review some of universal systems proposed in the literature and show that are particular case of this formalism. Finally, we review some of the attempts to understand the relation between dynamical and computational properties of a system.     

Necessary conditions for multistationarity in discrete dynamical systems

R. Thomas conjectured, 20 years ago, that the presence of a positive circuit in the interaction graph of a dynamical system is a necessary condition for the presence of several stable states. Recently, E. Remy et al. stated and proved the conjecture for Boolean dynamical systems. Using a similar approach, we generalize the result to discrete dynamical systems, and by focusing on the asynchronous dynamics that R. Thomas used in the course of his analysis of genetic networks, we obtain a more general variant of R. Thomas’ conjecture. In this way, we get a necessary condition for genetic networks to lead to differentiation.     

The emergence of dynamical complexity: An exploration using elementary cellular automata

This work concerns the interaction between two classical problems: the forecasting of the dynamical behaviors of elementary cellular automata (ECA) from its intrinsic mathematical laws and the conditions that determine the emergence of complex dynamics. To approach these problems, and inspired by the theory of reversible logical gates, we decompose the ECA laws in a “spectrum” of dyadic Boolean gates. Emergent properties due to interactions are captured generating another spectrum of logical gates. The combined analysis of both spectra shows the existence of characteristic bias in the distribution of Boolean gates for ECA belonging to different dynamical classes. These results suggest the existence of signatures capable to indicate the propensity to develop complex dynamics. Logical gates “exclusive‐or” and “equivalence” are among these signatures of complexity. An important conclusion is that within ECA space, interactions are not capable to generate signatures of complexity in the case these signatures are absent in the intrinsic law of the automaton. © 2004 Wiley Periodicals, Inc. Complexity 9: 33–42, 2004     

Memory Effects and Nonequilibrium Correlations in the Dynamics of Open Quantum Systems

We propose a systematic approach to the dynamics of open quantum systems in the framework of Zubarev’s nonequilibrium statistical operator method. The approach is based on the relation between ensemble means of the Hubbard operators and the matrix elements of the reduced statistical operator of an open quantum system. This key relation allows deriving master equations for open systems following a scheme conceptually identical to the scheme used to derive kinetic equations for distribution functions. The advantage of the proposed formalism is that some relevant dynamical correlations between an open system and its environment can be taken into account. To illustrate the method, we derive a non-Markovian master equation containing the contribution of nonequilibrium correlations associated with energy conservation.     

A survey on piecewise-linear models of regulatory dynamical systems

Recent developments in understanding the various regulatory systems, especially the developments in biology and genomics, stimulated an interest in modelling such systems. Hybrid systems, originally developed for process control applications, provide advances in modelling such systems. A particular class of hybrid systems which are relatively simpler to analyze mathematically but still capable of demonstrating the essential features of many non-linear dynamical systems is piecewise-linear systems. Implementation of piecewise-linear systems for modelling of regulatory dynamical systems requires different considerations depending on the status of the problem. In this work we considered three different cases. Firstly, we consider the inferential modelling problem based on the empirical observations and study the discrete piecewise-linear system, whose inverse problem is solvable under some assumptions. Secondly, we considered the problem of obtaining some complex regulatory systems by tractable piecewise-linear formulations and study the qualitative dynamic features of the systems and their piecewise-linear models. Finally, we considered Boolean delay equations for building abstract models of regulatory systems, which might be the simplest models demonstrating the essential qualitative features of our interest underlying adaption, learning and memorization.     

Polynomial representations of piecewise-linear differential equations arising from gene regulatory networks

We describe generic sliding modes of piecewise-linear systems of differential equations arising in the theory of gene regulatory networks with Boolean interactions. We do not make any a priori assumptions on regulatory functions in the network and try to understand what mathematical consequences this may have in regard to the limit dynamics of the system. Further, we provide a complete classification of such systems in terms of polynomial representations for the cases where the discontinuity set of the right-hand side of the system has a codimension 1 in the phase space. In particular, we prove that the multilinear representation of the underlying Boolean structure of a continuous-time gene regulatory network is only generic in the absence of sliding trajectories. Our results also explain why the Boolean structure of interactions is too coarse and usually gives rise to several non-equivalent models with smooth interactions.     

Cooperative Boolean systems with generically long attractors I

We study the class of cooperative Boolean networks whose only regulatory functions are COPY, binary AND and binary OR. We prove that for all sufficiently large N and c < 2 there exist Boolean networks in this class that have an attractor of length >c ^N whose basin of attraction comprises an arbitrarily large fraction of the state space. The existence of such networks sharply contrasts with results on continuous dynamical systems that imply non-genericity of non-steady-state attractors under the assumption of cooperativity.     

Deriving Behavior of Boolean Bioregulatory Networks from Subnetwork Dynamics

In the well-known discrete modeling framework developed by R. Thomas, the structure of a biological regulatory network is captured in an interaction graph, which, together with a set of Boolean parameters, gives rise to a state transition graph describing all possible dynamical behaviors. For complex networks the analysis of the dynamics becomes more and more difficult, and efficient methods to carry out the analysis are needed. In this paper, we focus on identifying subnetworks of the system that govern the behavior of the system as a whole. We present methods to derive trajectories and attractors of the network from the dynamics suitable subnetworks display in isolation. In addition, we use these ideas to link the existence of certain structural motifs, namely circuits, in the interaction graph to the character and number of attractors in the state transition graph, generalizing and refining results presented in [10]. Lastly, we show for a specific class of networks that all possible asymptotic behaviors of networks in that class can be derived from the dynamics of easily identifiable subnetworks.      

Mathematical programming approach to the Petri nets reachability problem

This paper focuses on the resolution of the reachability problem in Petri nets, using the mathematical programming paradigm. The proposed approach is based on an implicit traversal of the Petri net reachability graph. This is done by constructing a unique sequence of Steps that represents exactly the total behaviour of the net. We propose several formulations based on integer and/or binary linear programming, and the corresponding sets of adjustments to the particular class of problem considered. Our models are validated on a set of benchmarks and compared with standard approaches from IA and Petri nets community.