The metabolic algorithm for P systems: Principles and applications

Manca, Vincenzo

doi:10.1016/j.tcs.2008.04.015

Cited by 40 publications

(36 citation statements)

References 21 publications

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“…Therefore, dynamics is given at discrete steps, and in each step, it is ruled by a partition of matter among the reactions transforming it. The principle underlying the partitioning is called mass partition principle, and it defines the transformations of object populations, rather than single objects, according to a suitable generalization of chemical laws [11].…”

Section: Metabolic P Systemsmentioning

confidence: 99%

“…The starting point of the Log-Gain theory [10,11,12] for MP systems is the Allometry Law [1,7], which has many possible formulations [10], but, in the case here discussed, it can be expressed in a simple way. Namely, a proportion can be assumed, at each step, between the relative variations of the flux of a reaction and the sum of relative variations of its reactants, with a possible gap, called offset.…”

Section: Log-gain Theory: a Brief Recallmentioning

confidence: 99%

“…For these reasons, many computational models inspired from biology have been proposed. 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%

“…In fact, in the framework of MP systems, a theory called Log-Gain [10,11,12] has been introduced, specifically devoted to the deduction of reaction fluxes, that is, the amount of reactants transformed by the reactions at any step of the system.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Metabolic P Systemsmentioning

confidence: 99%

Section: Log-gain Theory: a Brief Recallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Algorithm for Initial Fluxes of Metabolic P Systems

Pagliarini

Franco

Manca

2009

INT J COMPUT COMMUN

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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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Systems Toxicology from Genes to Organs

Jack

Wambaugh

Shah

2012

Methods in Molecular Biology

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This unique overview of systems toxicology methods and techniques begins with a brief account of systems thinking in biology over the last century. We discuss how systems biology and toxicology continue to leverage advances in computational modeling, informatics, large-scale computing, and biotechnology. Next, we chart the genesis of systems toxicology from previous work in physiologically based models, models of early development, and more recently, molecular systems biology. For readers interested in further details this background provides useful linkages to the relevant literature. It also lays the foundations for new ideas in systems toxicology that could translate laboratory measurements of molecular responses from xenobiotic perturbations to adverse organ level effects in humans. By providing innovative solutions across disciplinary boundaries and highlighting key scientific gaps, we believe this chapter provides useful information about the current state, and valuable insight about future directions in systems toxicity.

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The metabolic algorithm for P systems: Principles and applications

Cited by 40 publications

References 21 publications

An Algorithm for Initial Fluxes of Metabolic P Systems

An Algorithm for Initial Fluxes of Metabolic P Systems

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

Systems Toxicology from Genes to Organs

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