Petri nets for modelling metabolic pathways: a survey

Baldan, Paolo; Cocco, Nicoletta; Marin, Andrea; Simeoni, Marta

doi:10.1007/s11047-010-9180-6

Cited by 96 publications

(61 citation statements)

References 78 publications

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“…[47,16]; rule-based systems, e.g. [22,11,18]; and Petri-Net based models, a formalism born in Bio-chemistry and widely used in computer science, see the survey [5]. See, e.g., [19] and references therein for further details on such modelling frameworks, whose detailed description is here out of scope.…”

Section: Cells As Computationmentioning

confidence: 99%

From Cells as Computation to Cells as Apps

Bracciali

Cataldo

Damiano

et al. 2016

IFIP Advances in Information and Communication Technology

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Abstract. We reflect on the computational aspects that are embedded in life at the molecular and cellular level, where life machinery can be understood as a massively distributed system whose macroscopic behaviour is an emerging property of the interaction of its components. Such a relatively new perspective, clearly pursued by systems biology, is contributing to the view that biology is, in several respects, a quantitative science. The recent developments in biotechnology and synthetic biology, noticeably, are pushing the computational interpretation of biology even further, envisaging the possibility of a programmable biology. Several in-silico, in-vitro and in-vivo results make such a possibility a very concrete one. The long-term implications of such an "extended" idea of programmable living hardware, as well as the applications that we intend to develop on those "computers", pose fundamental questions.

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Section: Cells As Computationmentioning

confidence: 99%

From Cells as Computation to Cells as Apps

Bracciali

Cataldo

Damiano

et al. 2016

IFIP Advances in Information and Communication Technology

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“…To deal with such complex bio-systems, scientists may recur to different dynamic modelling techniques and simulation tools, including, for instance, ordinary differential equations (ODEs), process calculi, boolean networks, Bayesian networks, Petri nets, bipartite graphs, stochastic equations (see, e.g., Baldan et al 2010;de Jong 2002;Deville et al 2003;Mandel et al 2004;Wiekert 2002 for some surveys). These different modelling techniques may be quantitative or qualitative, discrete or continuous, deterministic or probabilistic, and are frequently put to use in the context of imprecise and often incomplete knowledge.…”

Section: When Mechanistic Explanations Become Dynamicmentioning

confidence: 99%

Are Dynamic Mechanistic Explanations Still Mechanistic?

Issad

Malaterre

2015

History, Philosophy and Theory of the Life Sciences

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Mechanistic explanations are one of the major types of explanation in biology. The explanatory force of mechanisms is apparent in such typical cases as the functioning of an ion channel or the molecular activation of a receptor: it includes the specification of a model of mechanism and the rehearsing of a causal story that tells how the explanandum phenomenon is produced by the mechanism. It is however much less clear how mechanisms explain in the case of complex and non-linear biomolecular networks such as those that underlie the action of hormones and the regulation of genes. While dynamic mechanistic explanations have been proposed as an extension of mechanistic explanations, we argue that the former depart from the latter in that they do not draw their explanatory force from a causal story but from the mathematical warrants they give that the explanandum phenomenon follows from a mathematical model. By analyzing the explanatory force of mechanistic explanation and of dynamic mechanistic explanation, we show that the two types of explanations can be construed as limit cases of a more general pattern of explanation -Causally Interpreted Model Explanations -that draws its explanatory force from a model, a causal interpretation that links the model to biological reality, and a mathematical derivation that links the model to the explanandum phenomenon.

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“…PNs are a graphical and mathematical modeling tool used to study processes characterized as parallel, distributed, concurrent, and asynchronous [14]. They are used extensively in several types of information processing, including modeling CSNs.…”

Section: Techniques Used To Develop Modelmentioning

confidence: 99%