A Process Calculus for Expressing Finite Place/Transition Petri Nets

We introduce the process calculus Multi-CCS, which extends conservatively CCS with an operator of strong prefixing able to model atomic sequences of actions as well as multiparty synchronization. Multi-CCS is equipped with a labeled transition system semantics, which makes use of a minimal structural congruence. Multi-CCS is also equipped with an unsafe P/T Petri net semantics by means of a novel technique. This is the first rich process calculus, including CCS as a subcalculus, which receives a semantics in terms of unsafe, labeled P/T nets. The main result of the paper is that a class of Multi-CCS processes, called finite-net processes, is able to represent all finite (reduced) P/T nets.Comment: In Proceedings EXPRESS'10, arXiv:1011.601

Computing Difference Abstractions of Metabolic Networks Under Kinetic Constraints

International audienceAlgorithms based on abstract interpretation were proposed recently for predicting changes of reaction networks with partial kinetic information. Their prediction precision, however, depends heavily on which heuristics are applied in order to add linear consequences of the steady state equations of the metabolic network. In this paper we ask the question whether such heuristics can be avoided while obtaining the highest possible precision. This leads us to the first algorithm for computing the difference abstractions of a linear equation system exactly without any approximation. This algorithm relies on the usage of elementary flux modes in a nontrivial manner, first-order definitions of the abstractions, and finite domain constraint solving

Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation

Background: Accurate segmentation of microscopic structures such as bio-artificial capsules in microscopy imaging is a prerequisite to the computer-aided understanding of important biomechanical phenomenons. State-of-the-art segmentation performances are achieved by deep neural networks and related data-driven approaches. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Method: Recently, self-supervision, i.e. designing a neural pipeline providing synthetic or indirect supervision, has proved to significantly increase generalization performances of models trained on few shots. The objective of this paper is to introduce one such neural pipeline in the context of micro-capsule image segmentation. Our method leverages the rather simple content of these images so that a trainee network can be mentored by a referee network which has been previously trained on synthetically generated pairs of corrupted/correct region masks. Results: Challenging experimental setups are investigated. They involve from only 3 to 10 annotated images along with moderately large amounts of unannotated images. In a bio-artificial capsule dataset, our approach consistently and drastically improves accuracy. We also show that the learnt referee network is transferable to another Glioblastoma cell dataset and that it can be efficiently coupled with data augmentation strategies. Conclusions: Experimental results show that very significant accuracy increments are obtained by the proposed pipeline, leading to the conclusion that the self-supervision mechanism introduced in this paper has the potential to replace human annotations

Exact Boolean Abstraction of Linear Equation Systems

International audienceWe study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system φ exact for the boolean abstraction if the abstract interpretation of φ over the structure of booleans is equal to the boolean abstraction of the solution set of φ over the positive reals. Abstract interpretation over the booleans is thus complete for the boolean abstraction when restricted to exact linear equation systems, while it is not complete more generally. We present a new rewriting algorithm that makes 6 linear equation systems exact for the boolean abstraction while preserving the solutions over the positive reals. The rewriting algorithm is based on the elementary modes of the linear equation system. The computation of the elementary modes may require exponential time in the worst case, but is often feasible in practice with freely available tools. For exact linear equation systems we can compute the boolean abstraction by finite domain constraint programming. This yields a solution of the initial problem that is often feasible in practice. Our exact rewriting algorithm has two further applications. First, it can be used to compute the sign abstraction of linear equation systems over the reals, as needed for analysing functional programs with linear arithmetics. And second it can be applied to compute the difference abstraction of a linear equation system as used in change prediction algorithms for flux networks in systems biology

Réseaux de Pétri stochastiques comportant une seule place 1ere partie: régime stationnaire

Le formalisme des systèmes de réactions chimiques (de manière équivalente des réseaux de Pétri stochastiques) est très souvent utilisé en bilogie, en sûreté de fonctionnement etc. La série génératrice associée à la master-equation est solution d'une équation d'évolution du type équation de Schrödinger. On adopte ici l'approche classique par le calcul des fonctions propres en se concentrant, dans cette première partie, sur le calcul de la distribution stationnaire pour un système comportant une seule espèce chimique. On montre que, génériquement, la série génératrice stationnaire est une fonction holomorphe dans tout le plan complexe. Des exemples de calcul (symbolique-numérique) sur ordinateur sont développés

A Process Calculus for Molecular Interaction Maps

We present the MIM calculus, a modeling formalism with a strong biological basis, which provides biologically-meaningful operators for representing the interaction capabilities of molecular species. The operators of the calculus are inspired by the reaction symbols used in Molecular Interaction Maps (MIMs), a diagrammatic notation used by biologists. Models of the calculus can be easily derived from MIM diagrams, for which an unambiguous and executable interpretation is thus obtained. We give a formal definition of the syntax and semantics of the MIM calculus, and we study properties of the formalism. A case study is also presented to show the use of the calculus for modeling biomolecular networks.Comment: 15 pages; 8 figures; To be published on EPTCS, proceedings of MeCBIC 200

Multivariate prediction of Saliva Precipitation Index for relating selected chemical parameters of red wines to the sensory perception of astringency

Astringency is an essential sensory attribute of red wine closely related to the saliva precipitation upon contact with the wine. In this study a data matrix of 52 physico-chemical parameters was used to predict the Saliva Precipitation Index (SPI) in 110 Italian mono-varietal red wines using partial least squares regression (PLSr) with variable selection by Variable Importance for Projection (VIP) and the significance of regression coefficients. The final PLSr model, evaluated using a test data set, had 3 components and yielded an R2test of 0.630 and an RMSEtest of 0.994, with 19 independent variables whose regression coefficients were all significant at p < 0.05. Variables selected in the final model according to the decreasing magnitude of their absolute regression coefficient include the following: Procyanidin B1, Epicatechin terminal unit, Total aldehydes, Protein content, Vanillin assay, 520 nm, Polysaccharide content, Epigallocatechin PHL, Tartaric acid, Volatile acidity, Titratable acidity, Catechin terminal unit, Proanthocyanidin assay, pH, Tannin-Fe/Anthocyanin, Buffer capacity, Epigallocatechin PHL gallate, Catechin + epicatechin PHL, and Tannin-Fe. These results can be used to better understand the physico-chemical relationship underlying astringency in red win

Stochastic modelling of cellular growth and division by means of the pi@ calculus

The application of Concurrency Theory to Systems Biology is in its earliest stage of progress. The metaphor of cells as computing systems by Regev and Shapiro opened the employment of concurrent languages for the modelling of biological systems. Their peculiar characteristics led to the design of many bio-inspired formalisms which achieve higher faithfulness and specificity. In this paper we discuss the application to the biological modelling of pi@, a core calculus for the representation of biological systems. The pi@ language represents a keystone in this respect, thanks to its expressiveness capabilities which allow the modelling of a wide variety of phenomena (e.g. simple chemical reactions, but also formation of molecular or protein complexes, organisation of complex system in dynamical compartment hierarchies) despite of its simplicity and conservativeness. Here we analyse a biological case study involving cellular growth and division, modelled in the stochastic variant of pi@: the case study is formalised and stochastically simulated according to a multi-compartment extension of Gillespie\u27s stochastic simulation algorithm. The results underline the usefulness of the modelling approach adopted in pi@ for the correct handling of systems with variable volume

Abstract

We give an overview of recent work on the rule-based modeling of nano devices. In particular, our experience in the modeling of a nanoscale elevator suggested us to enhance rule-based modeling with complex functional rates that can be used to express rates that depend on the current state of the entire complexes in which the reacting molecules reside. 1 The CompReNDe project In this overview we briefly describe CompReNDe (Compositional and executable Representations of Nano Devices), an interdisciplinary project of the Chemistry and Computer Science departments of the University of Bologna aimed at combining the expertises of two groups, one specialized in the design and construction of devices and machines of molecular size [3,2] and the other one qualified in formal models, based on the theory of process calculi, for describing and analyzing molecular systems [14]. Such expertises have been joined together in order to deliver a programming model for describing molecular machines that is also amenable to automated simulations an

Encoding Catalytic P Systems in π@

AbstractP systems are theoretical computing devices abstracted away from the biological architecture of the cell, introduced some years ago by Gheorghe Păun and now intensely studied. In the area of concurrent systems, process calculi have recently been applied and extended with similar aim, to simulate (and formalise) the behaviour of the cell. Although many common points can be found between the two approaches, no formal and exhaustive comparison has been carried out yet.π@ is a new calculus, strongly π-Calculus based, well-suited to easily encode biologically inspired process calculi. In this paper the encoding in π@ of one variant of P systems is proposed, thus allowing a better understanding of similarities between P systems and bio-inspired process calculi