1,572 research outputs found
Computing with cells: membrane systems - some complexity issues.
Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism
New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems
We propose two uniform solutions to an open question: the Firing Squad
Synchronization Problem (FSSP), for hyperdag and symmetric neural P systems,
with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively,
where e_c is the eccentricity of the commander cell of the dag or digraph
underlying these P systems. The first and fast solution is based on a novel
proposal, which dynamically extends P systems with mobile channels. The second
solution is substantially longer, but is solely based on classical rules and
static channels. In contrast to the previous solutions, which work for
tree-based P systems, our solutions synchronize to any subset of the underlying
digraph; and do not require membrane polarizations or conditional rules, but
require states, as typically used in hyperdag and neural P systems
A new perspective on computational complexity theory in Membrane Computing
A single Turing machine can solve decision problems with an in nite number
of instances. On the other hand, in the framework of membrane computing, a \solution"
to an abstract decision problem consists of a family of membrane systems (where each
system of the family is associated with a nite set of instances of the problem to be
solved). An interesting question is to analyze the possibility to nd a single membrane
system able to deal with the in nitely many instances of a decision problem.
In this context, it is fundamental to de ne precisely how the instances of the problem
are introduced into the system. In this paper, two different methods are considered:
pre-computed (in polynomial time) resources and non-treated resources.
An extended version of this work will be presented in the 20th International Conference
on Membrane Computing.Ministerio de Economía, Industria y Competitividad TIN2017-89842-
(Tissue) P Systems with Anti-Membranes
The concept of a matter object being annihilated when meeting its corresponding
anti-matter object is taken over for membranes as objects and anti-membranes
as the corresponding annihilation counterpart in P systems. Natural numbers can be
represented by the corresponding number of membranes with a speci c label. Computational
completeness in this setting then can be obtained with using only elementary
membrane division rules, without using objects. A similar result can be obtained for tissue
P systems with cell division rules and cell / anti-cell annihilation rules. In both cases,
as derivation modes we may take the standard maximally parallel derivation modes as
well as any of the maximally parallel set derivation modes (non-extendable (multi)sets of
rules, (multi)sets with maximal number of rules, (multi)sets of rules a ecting the maximal
number of objects)
A Framework for Complexity Classes in Membrane Computing
The purpose of the present work is to give a general idea about the existing results and open problems
concerning the study of complexity classes within the membrane computing framework. To this aim,
membrane systems (seen as computing devices) are briefly introduced, providing the basic definition and
summarizing the key ideas, trying to cover the various approaches that are under investigation in this area
– of course, special attention is paid to the study of complexity classes. The paper concludes with some
final remarks that hint the reasons why this field (as well as other unconventional models of computation)
is attracting the attention of a growing community.Ministerio de Educación y Ciencia TIN2005-09345-C04-01Junta de Andalucía TIC-58
A Short Note on Reversibility in P Systems
Membrane computing is a formal framework of distributed parallel comput-
ing. In this paper we study the reversibility and maximal parallelism of P systems from
the computability point of view. The notions of reversible and strongly reversible systems
are considered. The universality is shown for one class and a negative conjecture is stated
for a more restricted class of reversible P systems. For one class of strongly reversible P
systems, a very strong limitation is found, and it is shown that this limitation does not
hold for a less restricted class
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
- …