13th International Conference on Membrane Computing - MTA Sztaki

<strong>13th</strong> <strong>International</strong> <strong>Conference</strong> on Membrane Computing, CMC13,
Budapest, Hungary, August 28 - 31, 2012. Proceedings, pages 351 - 368.
An Analysis of Correlative and
Quantitative Causality in P Systems
Roberto Pagliarini 1 , Oana Agrigoroaiei 2 , Gabriel Ciobanu 2 , and Vincenzo
Manca 3
1 Telethon Institute of Genetics and Medicine, Via P. Castellino 111, Naples, Italy
r.pagliarini@tigem.it
2 Romanian Academy, Institute of Computer Science
oanaag@iit.tuiasi.ro,gabriel@info.uaic.ro
3 Verona University, Computer Science Dept., Strada Le Grazie 15, Verona, Italy
vincenzo.manca@univr.it
Abstract. In this paper we present two approaches, namely correlative
and quantitative causality, to study cause-effect relationships in reaction
models and we propose a framework which integrates them in order
to study causality by means of transition P systems. The proposed
framework is based on the fact that statistical analysis can be used to
building up a membrane model which can be used to analyze causality
relationships in terms of multisets of objects and rules in presence
of non-determinism and parallelism. We prove that the P system which
is defined by means of correlation analysis provides a correspondence
between quantitative and correlative notions of causality.
1 Introduction
Since their first introduction, membrane systems [15], also known as P systems,
have been widely investigated in the framework of formal language theory
as innovative compartmentalized parallel multiset rewriting systems, and different
variants have been analyzed along with their computational power (for a
complete list of references, see http://ppage.psystems.eu). Although they were
originally introduced as computational models, their biologically inspired structure
and functioning, together with their feasibility as models of cellular and
biomolecular processes, turned out to be a widely applicable modeling technique
in several domains.
If we see P systems as biochemical reaction models 1 , then it is possible to
apply them to study causality in living cells, that is, the ways that entities of a
reaction system influence each other. In particular, cause-effect relationships can
be analyzed by following two ways: i) a statistical approach, and ii) a quantitative
approach.
1 These models are a formal representation of interactions between biochemical reactions.
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