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1. Toward simple in silico experiments for drugs administration in some cancer treatments

Author

Kaouther Moussa, Mohamed W. Alsager, Seddik M. Djouadi, Cédric Join, Michel Fliess, Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), ALgèbre pour Identification & Estimation Numériques (AL.I.E.N.), Centre de Recherche en Automatique de Nancy (CRAN), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), The University of Tennessee [Knoxville], and Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, J.3, Property (programming), Computer science, In silico, Flatness (systems theory), flatness-based control, [SDV.CAN]Life Sciences [q-bio]/Cancer, 02 engineering and technology, Systems and Control (eess.SY), Nonlinear control, model-free control, 01 natural sciences, Electrical Engineering and Systems Science - Systems and Control, 020901 industrial engineering & automation, Control theory, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], cancer, Control (linguistics), Closing (morphology), Mathematics - Optimization and Control, Simple (philosophy), 92C42, 93B52, 010401 analytical chemistry, shooting method, fault accommodation, 0104 chemical sciences, 3. Good health, Control and Systems Engineering, Optimization and Control (math.OC), biomedical control, nonlinear control, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Injected drug
Abstract

We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is a model-based setting, along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents., Comment: IFAC Symposium on Biological and Medical Systems - 11th BMS 2021 -- Ghent, Belgium, 19-22 September 2021

Published
2021

2. Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy

Author

Maël Balluet, Florian Sizaire, Youssef El Habouz, Thomas Walter, Jérémy Pont, Baptiste Giroux, Otmane Bouchareb, Marc Tramier, Jacques Pecreaux, Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), and Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )

Subjects
J.3, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Image processing, 02 engineering and technology, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], USable, Quantitative Biology - Quantitative Methods, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), 68T45, 92C37, 68U10, I.4.8, C.3, Quantitative Methods (q-bio.QM), 030304 developmental biology, 0303 health sciences, Artificial neural network, business.industry, Pattern recognition, Linear discriminant analysis, Random forest, Discriminant, Feature (computer vision), FOS: Biological sciences, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher’s linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4 % accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimising these algorithms for smart microscopy.

Published
2020

3. Cross-modal registration using point clouds and graph-matching in the context of correlative microscopies

Author

Stephan Kunne, Guillaume Potier, Jean Merot, Perrine Paul-Gilloteaux, Unité de recherche de l'institut du thorax (ITX-lab), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Structure Fédérative de Recherche en Santé François Bonamy (SFR RSFB), Plateforme MicroPicell [Nantes], Université de Nantes (UN), ANR-18-CE45-0015,CROCOVAL,Recalage transmodal en microscopies corrélatives pour la caractérisation physiopathologique de la valvulopathie(2018), Paul-Gilloteaux, Perrine, APPEL À PROJETS GÉNÉRIQUE 2018 - Recalage transmodal en microscopies corrélatives pour la caractérisation physiopathologique de la valvulopathie - - CROCOVAL2018 - ANR-18-CE45-0015 - AAPG2018 - VALID, and unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX)

Subjects
FOS: Computer and information sciences, I.4, J.3, 05C60, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer Vision and Pattern Recognition (cs.CV), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Quantitative Biology - Tissues and Organs, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, FOS: Biological sciences, Tissues and Organs (q-bio.TO)
Abstract

Correlative microscopy aims at combining two or more modalities to gain more information than the one provided by one modality on the same biological structure. Registration is needed at different steps of correlative microscopies workflows. Biologists want to select the image content used for registration not to introduce bias in the correlation of unknown structures. Intensity-based methods might not allow this selection and might be too slow when the images are very large. We propose an approach based on point clouds created from selected content by the biologist. These point clouds may be prone to big differences in densities but also missing parts and outliers. In this paper we present a method of registration for point clouds based on graph building and graph matching, and compare the method to iterative closest point based methods., in Proceedings of iTWIST'20, Paper-ID: 22, Nantes, France, December, 2-4, 2020

Published
2020

4. Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data

Author

Yuliya V. Orlova, Emmanuel Barillot, Alexander N. Gorban, Evgeny M. Mirkes, Jonathan Bac, S. E. Golovenkin, A. Chervov, Andrei Zinovyev, Krasnoyarsk State Medical University (KrasSMU), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Leicester, Lobachevsky State University [Nizhni Novgorod], ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 826121,iPaediatricCurie, Zinovyev, Andrei, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING, and MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, J.2, J.3, Clinical Trajectory, Computer science, Pseudotime, I.2.6, computer.software_genre, Machine Learning (cs.LG), 0302 clinical medicine, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Cluster Analysis, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Principal trees, pseudo-time, Diabetes, Dimensionality reduction, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Dynamical diseases phenotyping, Trajectory, Clinical data, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Data mining, AcademicSubjects/SCI02254, Data analysis, Health Informatics, Feature selection, Data type, Statistics - Applications, Clustering, 03 medical and health sciences, Data visualization, Diabetes Mellitus, Humans, Applications (stat.AP), Cluster analysis, 030304 developmental biology, [SDV.IB] Life Sciences [q-bio]/Bioengineering, business.industry, Research, Data structure, Missing data, Myocardial infarction, AcademicSubjects/SCI00960, Patient disease pathway, business, computer, 030217 neurology & neurosurgery
Abstract

Background Large observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by “points of no return" and “final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations. Results Here we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs, which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection, and quantifying the geodesic distances (pseudo-time) in partially ordered sequences of observations. The methodology allows a patient to be positioned on a particular clinical trajectory (pathological scenario) and the degree of progression along it to be characterized with a qualitative estimate of the uncertainty of the prognosis. We developed a tool ClinTrajan for clinical trajectory analysis implemented in the Python programming language. We test the methodology in 2 large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data. Conclusions Our pseudo-time quantification-based approach makes it possible to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data.

Published
2020

5. On the Complexity of Exact Pattern Matching in Graphs: Binary Strings and Bounded Degree

Author

Equi, Massimo, Grossi, Roberto, M��kinen, Veli, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), The first two authors are grateful to Alessio Conte and Luca Versari for providing theircomments on the reduction. The last author wishes to thank the participants of the annualresearch group retreat on sparking the idea to studysethreductions in this context. Wethank the anonymous reviewers of an earlier version of this paper for useful suggestionsfor improving the presentation and for pointing out the connection to regular expression matching., and Sagot, Marie-France

Subjects
FOS: Computer and information sciences, J.3, F.1, string matching, E.1, F.2.2, G.2.2, H.2.3, H.2.8, H.3.3, Computational Complexity (cs.CC), [INFO] Computer Science [cs], heterogeneous networks, 2012 ACM Subject Classification Mathematics of computing → Graph algorithms Theory of computation → Problems, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, strong exponential time hypothesis, graph search, variation graphs, labeled graphs, exact pattern matching, Computer Science - Computational Complexity, string search, graph query
Abstract

Exact pattern matching in labeled graphs is the problem of searching paths of a graph $G=(V,E)$ that spell the same string as the pattern $P[1..m]$. This basic problem can be found at the heart of more complex operations on variation graphs in computational biology, of query operations in graph databases, and of analysis operations in heterogeneous networks, where the nodes of some paths must match a sequence of labels or types. We describe a simple conditional lower bound that, for any constant $\epsilon>0$, an $O(|E|^{1 - \epsilon} \, m)$-time or an $O(|E| \, m^{1 - \epsilon})$-time algorithm for exact pattern matching on graphs, with node labels and patterns drawn from a binary alphabet, cannot be achieved unless the Strong Exponential Time Hypothesis (SETH) is false. The result holds even if restricted to undirected graphs of maximum degree three or directed acyclic graphs of maximum sum of indegree and outdegree three. Although a conditional lower bound of this kind can be somehow derived from previous results (Backurs and Indyk, FOCS'16), we give a direct reduction from SETH for dissemination purposes, as the result might interest researchers from several areas, such as computational biology, graph database, and graph mining, as mentioned before. Indeed, as approximate pattern matching on graphs can be solved in $O(|E|\,m)$ time, exact and approximate matching are thus equally hard (quadratic time) on graphs under the SETH assumption. In comparison, the same problems restricted to strings have linear time vs quadratic time solutions, respectively, where the latter ones have a matching SETH lower bound on computing the edit distance of two strings (Backurs and Indyk, STOC'15)., Comment: Using Lemma 12 and Lemma 13 might to be enough to prove Lemma 14. However, the proof of Lemma 14 is correct if you assume that the graph used in the reduction is a DAG. Hence, since the problem is already quadratic for a DAG and a binary alphabet, it has to be quadratic also for a general graph and a binary alphabet

Published
2019

6. Efficient FPT Algorithms for (Strict) Compatibility of Unrooted Phylogenetic Trees

Author

Celine Scornavacca, Christophe Paul, Ignasi Sau, Julien Baste, Algorithmes, Graphes et Combinatoire (ALGCO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226

Subjects
FOS: Computer and information sciences, 0301 basic medicine, J.3, Parameterized complexity, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], 01 natural sciences, FPT algorithm, Computational phylogenetics, 05C85, 92Dxx, Quantitative Biology::Populations and Evolution, Data Structures and Algorithms (cs.DS), Phylogeny, General Environmental Science, Mathematics, 0303 health sciences, Phylogenetic tree, G.2.2, General Neuroscience, Biological Evolution, Supertree, Phylogenetics, Computational Theory and Mathematics, 010201 computation theory & mathematics, Tree rearrangement, Split, General Agricultural and Biological Sciences, Unrooted phylogenetic trees, Algorithm, Algorithms, General Mathematics, Immunology, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 0102 computer and information sciences, Dynamic programming, Compatibility, General Biochemistry, Genetics and Molecular Biology, Combinatorics, 03 medical and health sciences, Computer Science - Data Structures and Algorithms, 030304 developmental biology, Pharmacology, Discrete mathematics, Base Sequence, Models, Genetic, Weight-balanced tree, Treewidth, 030104 developmental biology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

In phylogenetics, a central problem is to infer the evolutionary relationships between a set of species $X$; these relationships are often depicted via a phylogenetic tree -- a tree having its leaves univocally labeled by elements of $X$ and without degree-2 nodes -- called the "species tree". One common approach for reconstructing a species tree consists in first constructing several phylogenetic trees from primary data (e.g. DNA sequences originating from some species in $X$), and then constructing a single phylogenetic tree maximizing the "concordance" with the input trees. The so-obtained tree is our estimation of the species tree and, when the input trees are defined on overlapping -- but not identical -- sets of labels, is called "supertree". In this paper, we focus on two problems that are central when combining phylogenetic trees into a supertree: the compatibility and the strict compatibility problems for unrooted phylogenetic trees. These problems are strongly related, respectively, to the notions of "containing as a minor" and "containing as a topological minor" in the graph community. Both problems are known to be fixed-parameter tractable in the number of input trees $k$, by using their expressibility in Monadic Second Order Logic and a reduction to graphs of bounded treewidth. Motivated by the fact that the dependency on $k$ of these algorithms is prohibitively large, we give the first explicit dynamic programming algorithms for solving these problems, both running in time $2^{O(k^2)} \cdot n$, where $n$ is the total size of the input., 18 pages, 1 figure

Published
2016

7. Using cascading Bloom filters to improve the memory usage for de Brujin graphs

Author

Gustavo Sacomoto, Kamil Salikhov, Gregory Kucherov, Lomonosov Moscow State University (MSU), Baobab, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [Beer-Sheva], Ben-Gurion University of the Negev (BGU), Laboratoire d'Informatique Gaspard-Monge (LIGM), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), Kucherov, Gregory, Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), and Department of Mathematical Logic and Theory of Algorithms

Subjects
FOS: Computer and information sciences, J.3, E.2, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Sequencing data, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Bloom filter, Structural Biology, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Representation (mathematics), Molecular Biology, De Bruijn sequence, Genome assembly, Research, Applied Mathematics, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Data structure, Hash table, Computational Theory and Mathematics, Next-generation sequencing, de Brujin graph, Algorithm, Large size
Abstract

De Brujin graphs are widely used in bioinformatics for processing next-generation sequencing data. Due to a very large size of NGS datasets, it is essential to represent de Bruijn graphs compactly, and several approaches to this problem have been proposed recently. In this work, we show how to reduce the memory required by the algorithm of [3] that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to the method of [3], with insignificant impact to construction time. At the same time, our experiments showed a better query time compared to [3]. This is, to our knowledge, the best practical representation for de Bruijn graphs., Comment: 12 pages, submitted

Published
2014

8. An Efficient Biomechanical Cell Model to Simulate Large Multi-cellular Tissue Morphogenesis: Application to Cell Sorting Simulation on GPU

Author

Vincent Rodin, Anne Jeannin-Girardon, Pascal Ballet, Lab-STICC_TB_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Région Bretagne, Springer, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), and Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS )

Subjects
J.3, Scale (ratio), Computer science, Spring system, Context (language use), Parallel computing, High-performance simulation, 01 natural sciences, Multi-Agent Systems, 03 medical and health sciences, 0103 physical sciences, Synchronization (computer science), Tissue morphogenesis, Graphics, 010306 general physics, Simulation, 030304 developmental biology, ComputingMethodologies_COMPUTERGRAPHICS, 0303 health sciences, OpenCL, Multi-agent system, Cell model, Virtual cell, Cell sorting, Virtual biology, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [ INFO.INFO-MA ] Computer Science [cs]/Multiagent Systems [cs.MA]
Abstract

International audience; In the context of tissue morphogenesis study, in silico simulations can be seen as experiments in a virtual lab bench. Such simulations can facilitate the comprehension of a system, the test of hypotheses or the incremental refining of a model and its parameters. In silico simulations must be efficient and provide the possibility to simulate large tissues, containing thousands of cells. We propose to study tissue morphogenesis at the cellular level using our virtual biomechanical cell model. This model is based on a mass/spring system and coupled to a multi-agent system. We validated the relevance of our model through a case study: a cell sorting. Moreover, we took advantage of the large parallelism offered by graphics processing units (GPU), which contain up to thousands of cores: we implemented our model with the OpenCL framework. We ran large scale simulations, with up to 106 of our virtual cells. We studied the performance of our system on a CPU Intel Core i7 860, and two GPUs: a NVidia GeForce GT440 and a Nvidia GeForce GTX 690. The absence of synchronization in our implementation allowed the full benefits of the parallelism of these hardwares.

Published
2013

9. In silico study of mechanical stresses at the cellular level during tissue development

Author

Vincent Rodin, Pascal Ballet, Anne Jeannin-Girardon, Lab-STICC_UBO_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), and Région Bretagne

Subjects
0303 health sciences, J.3, In silico, Cell, Morphogenesis, Motility, Biology, Cellular level, Cell biology, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], medicine, Biological cell, Mechanotransduction, Mitosis, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

4 pages; International audience; Mechanical contraints play a key role in tissue morphogenesis. We propose to study these mechanisms at the cellular level, thanks to our virtual biomechanical cell model. This model defines biological cell behaviors, such as cell motility, mitosis and adhesion as well as methods to evaluate cell compression/stretching and shearing. The evaluation of these constraints allows the virtual cells to respond by changing their color during simulation and lead to the observation of emerging patterns in cell differentiation during tissue growth: the main purpose of this evaluation is to give the cells the ability to respond to mechanical constraints by differentiating. This approach allows to study the influence of mechanotransduction during tissue morphogenesis.

Published
2013

10. A Software Architecture for Multi-Cellular System Simulations on Graphics Processing Units

Author

Anne Jeannin-Girardon, Pascal Ballet, Vincent Rodin, Lab-STICC_UBO_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), and Région Bretagne

Subjects
J.3, Computer science, 0206 medical engineering, GPU, 02 engineering and technology, computer.software_genre, Models, Biological, General Biochemistry, Genetics and Molecular Biology, Database-centric architecture, Computational science, 03 medical and health sciences, CUDA, Computer Graphics, Reference architecture, Graphics, 030304 developmental biology, General Environmental Science, 0303 health sciences, OpenCL, business.industry, Applied Mathematics, General Medicine, Modular design, Virtual biology, Multi-cellular systems, Parallel architecture, Philosophy, Computer architecture, Virtual machine, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], General-purpose computing on graphics processing units, General Agricultural and Biological Sciences, Software architecture, business, computer, Software, 020602 bioinformatics, Simulation
Abstract

10 pages; International audience; The first aim of simulation in virtual environment is to help biologists to have a better understanding of the simulated system. The cost of such simulation is significantly reduced compared to that of in vivo simulation. However, the inherent complexity of biological system makes it hard to simulate these systems on non-parallel architectures: models might be made of sub-models and take several scales into account; the number of simulated entities may be quite large. Today, graphics cards are used for general purpose computing which has been made easier thanks to frameworks like CUDA or OpenCL. Parallelization of models may however not be easy: parallel computer programing skills are often required; several hardware architectures may be used to execute models. In this paper, we present the software architecture we built in order to implement various models able to simulate multi-cellular system. This architecture is modular and it implements data structures adapted for graphics processing units architectures. It allows efficient simulation of biological mechanisms.

Published
2013

11. Une approche multi-agent pour la simulation en biologie cellulaire

Author

Ballet, Pascal, Pothet, Alain, Misevic, Gradimir, Jeannin-Girardon, Anne, Fronville, Alexandra, Rodin, Vincent, Ballet, Pascal, Matériologiques, Lab-STICC_UBO_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche Pédagogique (INRP), MNESR, Assemblages moléculaires : modélisation et imagerie SIMS (AMMIS), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO), Centre Européen de Réalité Virtuelle (CERV), and École Nationale d'Ingénieurs de Brest (ENIB)

Subjects
J.3, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], biologie cellulaire, Modélisation individu-centré, [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], biologie moléculaire, Simulation
Abstract

40 pages; National audience; La modélisation informatique individu-centré permet de créer toutes sortes de simulations en biologie cellulaire et moléculaire. Avec un logiciel approprié, la création de maquettes numériques est relativement simple et permet de bien appréhender le système réel modélisé. Ce document présente les objectifs et les enjeux de cette recherche ainsi que les intérêts pour le chercheur et l'étudiant. Les limites sont également précisées. Un bref historique est ensuite abordé afin de placer cet article dans son contexte. Enfin, une approche originale est présentée avec deux simulations à réaliser sur le logiciel NetBioDyn. Ces expériences virtuelles sont réalisables sans connaissance particulière en programmation. Mots-clés: Simulation, Modélisation individu-centré, biologie cellulaire, biologie moléculaire.

Published
2013

12. ANR Tecsan 07 - Epimouv Livrable 1-2-3 Partie CHU Caractérisation de la sémiologie motrice des crises épileptiques dans une unité d'épileptologie. Second système d'acquisition en clinique Logiciel de caractérisation des crises et Rapport d'évaluation

Author

Becq, Guillaume, Kahane, Philippe, Minotti, Lorella, GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de neurologie, Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble, Gipsa-Lab, and ANR-07-TECS-0004,EPIMOUV,Caractérisation et détection des mouvements durant les crises d'épilepsie à des fins d'alarme, pour un usage en institution et en famille(2007)

Subjects
J.3, analyse discriminante, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, magnétomètre, roc, [SDV.IB]Life Sciences [q-bio]/Bioengineering, épilepsie, accéléromètre, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, apprentissage supervisé, détection
Abstract

Ce rapport décrits les 3 livrables réalisés par le CHU au terme du projet Epimouv. Il décrit le travail réalisé par le CHU et l'analyse des données réalisée pour classer les manifestations motrices au cours des crises. A partir de ces classifications, un algorithme pour détecter les crises les plus fréquentes et les plus problématiques, les crises tonico-cloniques secondairement généralisées est proposé et évalué. Une représentation de l'évolution stéréotypiques des classes de manifestations motrices au cours du temps est aussi proposée en fonction des pathologies.

Published
2011

13. Parking functions, labeled trees and DCJ sorting scenarios

Author

Anne Bergeron, Aïda Ouangraoua, Algorithms for large scale sequence analysis (SEQUOIA2), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Département d'informatique [Montréal], and Université du Québec à Montréal = University of Québec in Montréal (UQAM)

Subjects
FOS: Computer and information sciences, J.3, Discrete Mathematics (cs.DM), [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Sorting, Construct (python library), Genome, Quantitative Biology::Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Set (abstract data type), Combinatorics, Exact formula, Enumeration, sort, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Bijection, injection and surjection, Computer Science - Discrete Mathematics, Mathematics
Abstract

In genome rearrangement theory, one of the elusive questions raised in recent years is the enumeration of rearrangement scenarios between two genomes. This problem is related to the uniform generation of rearrangement scenarios, and the derivation of tests of statistical significance of the properties of these scenarios. Here we give an exact formula for the number of double-cut-and-join (DCJ) rearrangement scenarios of co-tailed genomes. We also construct effective bijections between the set of scenarios that sort a cycle and well studied combinatorial objects such as parking functions and labeled trees., 12 pages, 3 figures

Published
2009

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