Evolution–Communication P Systems

Cavaliere, Matteo

doi:10.1007/3-540-36490-0_10

Cited by 52 publications

(26 citation statements)

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“…We have presented a comparison between the mathematical model (and the software realized) and the real world, trying to establish a link between the mathematical framework, the simulator realized (Cavaliere, 2003), and the biological reality. We introduced new concepts in the P system area such as the availability of a chemical reaction, the activity rate of a catalyst, and the possibility for a catalyst to be at same time active or not active.…”

Section: Membrane Proteins: Terminal Respiratory Enzymes and Photosysmentioning

confidence: 99%

Biological Roots and Applications of P Systems: Further Suggestions

Ardelean

2006

Membrane Computing

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Abstract. P systems offer the possibility to appropriately describe discrete processes performed by: i) single objects: catalytic molecules (= enzymes), supramolecular structures (MscL, porins, ionic channels etc.), single cells, and ii) a small number of objects occurring in the sample, e.g., several mechanosensitive channels occurring within a membrane patch. Thus P systems could offer the possibility to capture and model the plethora of experimental data obtained in the emerging and rapidly growing field of single cell or single molecule or atom studies.Furthermore, it is suggested that in vitro implementation of P systems could be done by the use of artificial membranes, a step forward computations with artificial membranes.

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Section: Membrane Proteins: Terminal Respiratory Enzymes and Photosysmentioning

confidence: 99%

Biological Roots and Applications of P Systems: Further Suggestions

Ardelean

2006

Membrane Computing

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“…Simple evolution rules are rules with all targets fixed as "here" (see [3]). Signaling rules are simple evolution rules, promoted by some signal-promoter p. When a signaling rule a → v| ptar (or ca → cv| ptar ) is applied then the object a is transformed into the objects specified by v and the promoter p is sent into the (adjacent) region specified by tar.…”

Section: Introductionmentioning

confidence: 99%

Computing using signals: from cells to P systems

Ardelean

Cavaliere²,

Sburlan

2004

Soft Comput

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Abstract. In cell biology one of the fundamental topic is the study of how biological signals are managed by cells. Signals can arise from inside the cell or from the external environment and the correct answer to certain signals is essential for bacteria to survive in a certain environment. Starting from these biological motivations we consider a model of P systems where the computation is controlled by signals which move across the regions. In particular, we consider Signals-Based P systems where the symbol-objects cannot be moved and the rules can be activated/inactivated using a finite number of signals (signal-promoters) moved across the membranes; differently from standard P systems using promoters, in our case promoters cannot be created during the computation. After discussing the biological motivations we show how this model becomes universal when it uses one catalyst, and a bounded number of signal-promoters.

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“…As well as this, bond making rules are considered that are used to modify the links between the existing cells (i.e., the set of edges in the graph) at the end of each step of evolution performed by means of the aforementioned rules. In other words, a population P-system in [4] is basically defined as an evolution-communication P-system [7] but with the important difference that the structure of the system is not rigid and it is represented as an arbitrary graph. In particular, bond making rules are able to influence cell capability of moving objects from a place to another one by varying the set of edges in the underlying graph.…”

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confidence: 99%

Membrane Computing — Current Results and Future Problems

Bernardini

Gheorghe

Krasnogor

et al. 2005

New Computational Paradigms

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In the last decade and especially after Adleman's experiment [1] a number of computational paradigms, inspired or gleaned from biochemical phenomena, are becoming of growing interest building a wealth of models, called generically Molecular Computing. New advances in, on the one hand, molecular and theoretical biology, and on the other hand, mathematical and computational sciences promise to make it possible in the near future to have accurate systemic models of complex biological phenomena. Recent advances in cellular Biology led to new models, hierarchically organised, defining a new emergent research area called Cellular Computing.P-systems represent a class of distributed and parallel computing devices of a biological type that was introduced in [14] which are included in the wider field of cellular computing. Several variants of this model have been investigated and the literature on the subject is now rapidly growing. The main results in this area show that P-systems are a very powerful and efficient computational model [15], [16], [13]. There are variants that might be classified according to different criteria. They may be regarded as language generators or acceptors, working with strings or multisets, developing synchronous or asynchronous computation. Two main classes of P-systems can be identified in the area of membrane computing [15]: cell-like P-systems and tissue-like P-systems. The former type is inspired by the internal organization of living cells with different compartments and membranes hierarchically arranged; formally this structure is associated with a tree. Tissue P-systems have been motivated by the structure and behaviour of multicellular organisms where they form a multitude of different tissues performing various functions [2]; the structure of the system is instead represented as a graph where nodes are associated with the cells which are allowed to communicate alongside the edges of the graph.More recently, a notion of population P-systems has been introduced [3], [4] as a model for tissue P-systems where the structure of the underlying graph can be modified during a computation by varying the set of nodes and the set of edges in the graph. Specifically, nodes are associated with cells, each of them representing a basic functional unit of the system, and edges model bonds among these cells that are dynamically created and destroyed. Although mainly inspired by the cell behaviour in living tissues, population P-systems may be also regarded as an abstraction of a population of bio-entities aggregated together in

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Evolution–Communication P Systems

Cited by 52 publications

References 1 publication

Biological Roots and Applications of P Systems: Further Suggestions

Biological Roots and Applications of P Systems: Further Suggestions

Computing using signals: from cells to P systems

Membrane Computing — Current Results and Future Problems

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