Investigating dynamic causalities in reaction systems

Reaction systems are a qualitative formalism for modeling systems of biochemical reactions characterized by the non-permanency of the elements: molecules disappear if not produced by any enabled reaction. Moreover, reaction systems execute in an environment that provides new molecules at each step. Brijder, Ehrenfeucht and Rozenberg investigated dynamic causalities in reaction systems by introducing the idea of predictors. A predictor of a molecule s, for a given n, is the set of molecules to be observed in the environment in order to determine whether s is produced or not by the system at step n. In this paper, we continue the investigation on dynamic causalities by defining an abstract interpretation framework containing three different notions of predictor: Formula based predictors, that is a propositional logic formula that precisely characterizes environments that lead to the production of s after n steps; Multi-step based predictors, that consist of n sets of molecules to be observed in the environment, one for each step; and Set based predictors, that are those proposed by Brijder, Ehrenfeucht and Rozenberg, and consist of a unique set of molecules to be observed in all steps. For each kind of predictor we define an effective operator that allows predictors to be computed for any molecule s and number of steps n. The abstract interpretation framework allows us to compare the three notions of predictor in terms of precision, to relate the three defined operators and to compute minimal predictors. We also discuss a generalization of this approach that allows predictors to be defined independently of the value of n, and a tabling approach for the practical use of predictors on reaction systems models. As an application, we use predictors, generalization and tabling to give theoretical grounds to previously obtained results on a model of gene regulation

Methodologies & formalisms for modeling macroscopic biological problems

This work presents a new computational approach, based on the formalism of P systems, for modelling and running simulations of animal population dynamics phenomena. The three formalisms proposed are: MPP systems (Minimal Probabilistic P systems), APP systems (Attributed Probabilistic P systems) and MAPP systems (Multilevel Attributed Probabilistic P systems). All of them are formally defined by providing their syntax notations and formal semantics as inference rules. Case study are provided with examples for all three formalism

Formal Modelling and Simulation of Biological Systems with Spatiality

In Systems Biology, spatial modelling allows an accurate description of phenomena whose behaviour is influenced by the spatial arrangement of the elements. In this thesis, we present various modelling formalisms with spatial features, each using a different abstraction level of the real space. From the formalisms with the most abstract notion of space, to the most concrete, we formally define the MIM Calculus with compartments, the Spatial P systems, and the Spatial CLS. Each formalism is suitable for the description of different kinds of systems, which call for the use of different space modelling abstractions. We present models of various real-world systems which benefit from the ability to precisely describe space-dependent behaviours. We define the MIM Calculus, inspired by Molecular Interaction Maps, a graphical notation for bioregulatory networks. The MIM Calculus provides high-level operators with a direct biological meaning, which are used to describe the interaction capabilities of the elements of such systems. Its spatial extension includes the most abstract notion of space, namely it only allows the modelling of compartments. Such a feature allows distinguishing only the abstract position where an element is, identified by the name of the compartment. Subsequently, we propose a spatial extension to the membrane computing formalism P systems. In this case, we follow a more precise approach to spatial modelling, by embedding membranes and objects in a two-dimensional discrete space. Some objects of a Spatial P system can be declared as mutually exclusive objects, with the constraint that each position can accommodate at most one of them. The distinction between ordinary and mutually exclusive objects can be thought of as an abstraction on the size of the objects. We study the computational complexity of the formalism and the problem of efficient simulation of some kinds of models. Finally, we present the Spatial Calculus of Looping Sequences (Spatial CLS), which is an extension of the Calculus of Looping Sequences (CLS), a formalism geared towards the modelling of cellular systems. In this case, models are based on two/three dimensional continuous space, and allow an accurate description of the motion of the elements, and of their size. In particular, Spatial CLS allows the description of the space occupied by elements and membranes, which can change their sizes dynamically as the system evolves. Space conflicts which may occur can be resolved by performing a rearrangement of elements and membranes. As example applications of the calculus we present a model of cell proliferation, and a model of the quorum sensing process in Pseudomonas aeruginosa

Simulation of Spatial P system models

Spatial P systems are an extension of the P systems formalism in which objects and membranes are embedded into a two-dimensional discrete space. Spatial P systems are characterised by the distinction between ordinary objects and mutually exclusive objects, with the constraint that any position can accommodate any number of ordinary objects, and at most one mutually exclusive object. The presence of mutually exclusive objects makes the simulation of Spatial P system models more complex than that of standard P systems. In this paper, we present a polynomial-time algorithm for the simulation of a restricted version of Spatial P systems where the restriction consists in considering only mutually exclusive objects and rules having exactly one reactant and one product. This version of Spatial P systems, although very restricted, is expressive enough to model interesting biological systems. In particular, we show how it can be used to simulate two models describing different dynamics of fish populations, namely the dynamics of territorial fish and the formation and movement of herring schools. In addition, the simulation methodology we propose can be adapted to simulate richer versions of Spatial P systems