Loic Paulev'e
8 Papers
Loic Paulev'e is an academic researcher. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 2, co-authored 6 publications.
Chat about Author
Papers
BioSimulators: a central registry of simulation engines and services for recommending specific tools
Bilal Shaikh,Lucian P. Smith,D. Vasilescu,Gnaneswara Marupilla,Michael Wilson,Eran Agmon,Henry Agnew,Steven S. Andrews,Azraf Anwar,Moritz Emanuel Beber,Frank Bergmann,David Brooks,Lutz Brusch,Laurence Calzone,Kiri Choi,Joshua Cooper,John Detloff,Brian Drawert,Michel Dumontier,G. Bard Ermentrout,James R. Faeder,Andrew Philip Freiburger,F. Frohlich,Akira Funahashi,Alan Garny,John H. Gennari,Padraig Gleeson,Anne Goelzer,Zachary B. Haiman,Joseph M. Hellerstein,Stefan Hoops,Jon Ison,Diego Jahn,Henry V. Jakubowski,Ryan M. Jordan,Mat'uvs Kalavs,M. Konig,Wolfram Liebermeister,Synchon Mandal,Robert A. McDougal,J. Kyle Medley,Pedro Mendes,Robert T. Muller,Chris J. Myers,Aurélien Naldi,Tung V N Nguyen,David P. Nickerson,Brett G. Olivier,Drashti Patoliya,Loic Paulev'e,Linda R. Petzold,Ankita Priya,Anand Rampadarath,Johann M. Rohwer,Ali S. Saglam,Dilawar Singh,Ankur Sinha,Jacky L. Snoep,Hugh Sorby,Ryan K. Spangler,Jörn Starruß,Payton J Thomas,D. D. van Niekerk,Daniel Weindl,Fengkai Zhang,Anna Zhukova,Arthur P. Goldberg,Michael L. Blinov,Herbert M. Sauro,Ion I. Moraru,Jonathan R. Karr +70 more
TL;DR: The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently.
Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis
TL;DR: This paper demonstrates how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on BNs, including marker properties, which specify that some components are fixed to a specific value.
Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks
TL;DR: In this paper , a counter-example guided refinement abstraction (CEGAR) is proposed to solve the satisfiability problem of quantified propositional logic formulas with three levels of quantifiers.
Attractor identification in asynchronous Boolean dynamics with network reduction
Elisa Tonello,Loic Paulev'e +1 more
TL;DR: In this article , an approach to the search for asynchronous cyclic attractors of Boolean networks that exploits, in a novel way, the established technique of elimination of components is described. But this approach is limited to Boolean networks.
mpbn: a simple tool for efficient edition and analysis of elementary properties of Boolean networks
TL;DR: mpbn is a Python tool for efficiently editing and analyzing elementary properties of Boolean networks, including fixed points, trap spaces, and reachability properties. It is scalable to large models and offers one of the best-performing tools for computing minimal and maximal trap spaces.
1