Evolution and Oscillation in P Systems: Applications to Biological Phenomena

Manca, Vincenzo; Bianco, Luca; Fontana, Federico

doi:10.1007/978-3-540-31837-8_4

Cited by 48 publications

(28 citation statements)

References 13 publications

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“…However, very often, in very complex systems with poor Information about the causes acting in a system, it is the only possibility that can be realistically investigated. The dynamics inverse problem was investigated in the context of MP theory developed in the last ten years (see, for example, [1][2][3][4][5][6]. In this paper, when we refer to MP regression, we intend the LGSS (basic) MP regression algorithm, integrating algebraic and statistical regression methods in the MP framework [7][8][9][10], that resulted completely satisfactory in all the considered cases.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Solutions to Dynamics Inverse Problems: A Validation of MP Regression

M¹

2014

J Appl Computat Math

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The paper is focused on a new perspective in solving dynamics inverse problems, by considering recurrent equations based on Metabolic P (MP) grammars. MP grammars are a particular type of multiset rewriting grammars, introduced in 2004 for modeling metabolic systems, which express dynamics in terms of finite difference equations. Regression algorithms, based on MP grammars, were introduced since 2008 for algorithmically solving dynamics inverse problems in the context of biological phenomena. This paper shows that, when MP regression is applied to time series of circular functions (where time is replaced by rotation angle), the dynamics that is found turns out to coincide with recurrent equations derivable from classical analytical definitions of these functions. This validates the MP regression as a general methodology to discover deep logics underlying observed dynamics. At the end of the paper some applications are also discussed, which exploit the regression capabilities of the MP framework for the analysis of periodical signals and for the implementation of sequential circuits providing periodical oscillators. When an initial state X [0] is given to an MP grammar G, then, starting from it, we pass from any state to its following state by applying all the rules of the grammar, that is, by summing the decrements and increments acting on each variable, according to all the rules where it occurs. It is easy to show that the corresponding dynamics is expressed with recurrent equations, which are synthetically represented in matricial form [10]. Recurrent Solutions to DynamicsWhen variables are equipped with measurement units (related to their interpretation), and a time duration is associated to each step, the MP grammar is more properly called an MP system.

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Section: Introductionmentioning

confidence: 99%

Recurrent Solutions to Dynamics Inverse Problems: A Validation of MP Regression

M¹

2014

J Appl Computat Math

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“…This new strategy of rule application was inspired by real 'metabolic reactions', and it seems to lead multiset based computing towards interesting simulations of biological processes, such as complex oscillations [11], the mitotic cycle [3] and the non-photochemical quenching phenomenon [12]. Overall, a new way to observe the evolution rules of a system reproducing a metabolic reaction was proposed.…”

Section: Introductionmentioning

confidence: 99%

Regulation and Covering Problems in MP Systems

2010

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Summary. The study of efficient methods to deduce fluxes of biological reactions, by starting from experimental data, is necessary to understand the dynamics of a metabolic model, but it is also a central issue in systems biology. In this paper we report some partial results and related open problems regarding the efficient computation of regulation fluxes in metabolic P systems. By means of Log-gain theory the system dynamics can be linearized, in such a way to be described by a recurrence equations system, of which we point out a few algebraic properties, involving covering problems.

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“…Among these models, the Metabolic P systems [11,12], shortly MP systems, proved to be relevant in the analysis of dynamics of biochemical processes, that is, structures where matter of different type is transformed by reactions. By means of MP systems models of several interesting phenomena were provided, among which we mention: the Lotka-Volterra dynamics [2,3,15], a Susceptible-Infected-Recovered epidemic [2], the Leukocyte Selective Recruitment in the immune response [2], the Protein Kinase C Activation [3], the Mitotic Cycle [14], the Pseudomonas Quorum Sensing [4] and the Non-Photochemical Quenching phenomenon [16].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Initial Fluxes of Metabolic P Systems

Pagliarini

Franco

Manca

2009

INT J COMPUT COMMUN

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A central issue in systems biology is the study of efficient methods inferring fluxes of biological reactions by starting from experimental data. Among the different techniques proposed in the last years, the theory of Metabolic P systems, which is based on the Log-Gain principle, proved to be helpful for deducing biological fluxes from temporal series of observed dynamics. According to this approach, the algebraic systems provided by the Log-Gain principle determine the reaction fluxes underlying a system dynamics when initial fluxes are known. Here we propose a heuristic algorithm for estimating the initial fluxes, that is tested in two case studies.

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Evolution and Oscillation in P Systems: Applications to Biological Phenomena

Cited by 48 publications

References 13 publications

Recurrent Solutions to Dynamics Inverse Problems: A Validation of MP Regression

Recurrent Solutions to Dynamics Inverse Problems: A Validation of MP Regression

Regulation and Covering Problems in MP Systems

An Algorithm for Initial Fluxes of Metabolic P Systems

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