Biological networks in metabolic P systems

Manca, Vincenzo; Bianco, Luca

doi:10.1016/j.biosystems.2006.11.009

Cited by 57 publications

(31 citation statements)

References 6 publications

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“…To this end, we chose a set of d primitive functions g 1 ,…, g d (at most of n variables), called regressors, by means of which regulators are expressed, as linear combinations of some of them. The selection of the regressors corresponding to each regulator is realized by means of suitable statistical tests based on Fisher's F-distribution within a strategy of Stepwise regression [25,26]. The determination of the coefficients of the chosen regressors is computed by combining the method of Least Square Evaluation with a procedure of Stoichiometric Expansion realized by means of suitable algebraic operations on matrices.…”

Section: The Lgss Algorithm Of Mp Regressionmentioning

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: The Lgss Algorithm Of Mp Regressionmentioning

confidence: 99%

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

M¹

2014

J Appl Computat Math

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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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“…A special class of MP systems was proved to be stongly related to differential models [4]. The notion of MP system we consider here is based on those given in [8,10,11].…”

Section: Metabolic P Systemsmentioning

confidence: 99%

“…In [7] a discrete perspective was introduced in the analysis of metabolic processes, which was then developed in papers [2,9,4,8,10,11], and which is focused on the notion of Metabolic P systems, shortly MP systems. Here, we outline the possibility of deducing an MP model, for a given metabolic process, from a suitable macroscopic observation of its behavior along a certain number of steps.…”

Section: Introductionmentioning

confidence: 99%

“…MP systems are a special type of P systems [12] which were proven to effectively model the dynamics of several biochemical processes: the Belousov-Zhabotinsky reaction (Brusselator) the Lotka-Volterra dynamics, the SIR (Susceptible-InfectedRecovered epidemic) [1], the leukocyte selective recruitment in the immunological response [5,1], the Protein Kinase C activation [2], circadian rhythms, mitotic cycles [8], [6] 1 . The perspective introduced by MP systems can be synthesized by a principle which replaces the mass action principle.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Simulations of Biochemical Dynamics

Manca

DNA Computing

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Abstract. Metabolic P systems, shortly MP systems, are a special class of P systems, introduced for expressing biological metabolism. Their dynamics are computed by metabolic algorithms which transform populations of objects according to a mass partition principle, based on suitable generalizations of chemical laws. The definition of MP system is given and a new kind of regulation mechanism is outlined, for the construction of computational models from experimental data of given metabolic processes.

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Biological networks in metabolic P systems

Cited by 57 publications

References 6 publications

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

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

An Algorithm for Initial Fluxes of Metabolic P Systems

Discrete Simulations of Biochemical Dynamics

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