Catálogo de publicaciones - libros

The paper introduces communication modes in tissue P systems that are based on the applicability of the rules to the objects present inside the cells. This notion of a communication mode is inspired by the concept of a derivation mode used in the area of grammar systems. Three different communication modes are identified depending on both the way the objects are moved from one cell to another one, altogether as a multiset or independently from each other, and on the moment when communication can take place, immediately after a terminal object is produced inside a cell, immediately after a cell has reached a terminal configuration or only when the system as a whole has reached a final configuration. The computational power of tissue P systems with different communication modes is compared with the power of the basic model of P systems and some classes of L systems.

During recent years stochastic algorithms have deserved much attention from the computational biology research communities. In this paper we derive a hybrid version of the formerly known Metabolic Algorithm that is enriched with stochastic features, whose impact on the dynamics of the system is especially prominent when the amount of metabolite becomes smaller. This hybrid procedure represents a first attempt to let the Metabolic Algorithm deal with low concentrations of substances according to a non-deterministic policy.

Pseudomonas aeruginosa is an opportunistic bacterium that exploits quorum sensing communication to synchronize individuals in a colony and this leads to an increase in the effectiveness of its virulence. In this paper we derived a mechanistic P systems model to describe the behavior of a single bacterium and we discuss a possible approach, based on an evolutionary algorithm, to tune its parameters that will allow a quantitative simulation of the system.

We consider the idea of controlling the evolution of a membrane system. In particular, we investigate a model of membrane systems using promoted rules, where a string of promoters (called the control string) “travels” through the regions, activating the rules of the system. This control string is present in the skin region at the beginning of the computation – one can interpret that it has been inserted in the system before starting the computation – and it is “consumed”, symbol by symbol, while traveling through the system. In this way, the inserted string drives the computation of the membrane system by controlling the activation of evolution rules. When the control string is entirely consumed and no rule can be applied anymore, then the system halts – this corresponds to a successful computation. The number of objects present in the output region is the result of such a computation. In this way, using a set of control strings (a control program), one generates a set of numbers. We also consider a more restrictive definition of a successful computation, and then study the corresponding model.

In this paper we investigate the influence of the structure of control programs on the generative power. We demonstrate that different structures yield generative powers ranging from finite to recursively enumerable number sets.

In determining the way that the control string moves through the regions, we consider two possible “strategies of traveling”, and prove that they are similar as far as the generative power is concerned.

The aim of this paper is to start an investigation and a comparison of the expressiveness of the two most relevant formalisms inspired by membranes interactions, namely, P systems and Brane Calculi. We compare the two formalisms with respect to their ability to act as generator devices. In particular, we show different ways of generating the set in P systems and in Brane Calculi.

We introduce Genetic P systems, a class of P systems with evolution rules inspired by the functioning of the genes.

The creation of new objects – representing proteins – is driven by genetic gates: a new object is produced when all the activator objects are present, and no inhibitor object is available. Activator objects are not consumed by the application of such an evolution rule. Objects disappear because of degradation: each object is equipped with a lifetime; when such a lifetime expires, the object decays.

Then, we extend the basic model with rules and rules, that simulate the action of protein channels and the action of substances which connect to other objects to block their use. We provide a universality result for such a class of systems.

In this paper we present a method to classify the states of a finite Markov chain through membrane computing. A specific P system with external output is designed for each boolean matrix associated with a finite Markov chain. The computation of the system allows us to decide the convergence of the process because it determines in the environment the classification of the states (recurrent, absorbent, and transient) as well as the periods of states. The amount of resources required in the construction is polynomial in the number of states of the Markov chain.

We propose a logic for specifying and proving properties of membrane systems. The main idea is to approach a membrane system by using the “point of view” of an external observer. Observers (as epistemic agents) accumulate their knowledge from the partial information they collect by observing subparts of the system and by applying logical reasoning to this information. We provide a formal framework to combine and interpret distributed knowledge in order to recover the complete knowledge about a membrane system. The proposed logic can be used to model biological situations where information concerning parts of the biological system is missing or incomplete.

Stochastic simulations based on the leaping method are applicable to well stirred chemical systems reacting within a single fixed volume. In this paper we propose a novel method, based on the leaping procedure, for the simulation of complex systems composed by several communicating regions. The new method is here applied to dynamical probabilistic P systems, which are characterized by several features suitable to the purpose of performing stochastic simulations distributed in many regions. Conclusive remarks and ideas for future research are finally presented.

In this paper we present P machines corresponding to membrane systems with a single membrane. We give examples of simple P machines for both P systems with promoters and P systems with priorities. For each case we get the same results for both P machines and their corresponding P systems. We present a way of connecting simple P machines, and give an example how the new resulting network corresponds to P systems with more than one membrane.