The rule-based model approach. A Kappa model for hepatic stellate cells activation by TGFB1

International audienc

Computational Logic for Biomedicine and Neurosciences

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis

Computational Logic for Biomedicine and Neuroscience

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis.Nous pr{\^o}nons ici l'utilisation d'une logique calculatoire pour la biologie des systèmes, en tant que cadre \emph{unifié et sûr}, bien adapté à la fois à la modélisation du comportement dynamique des systèmes biologiques,à l'expression de leurs propriétés, et à la vérification de ces propriétés.Les logiques candidates potentielles doivent avoir un pedigree traditionnel en théorie de la preuve (y compris, soit l'induction, soit une présentation en calcul des séquents, avec l'élimination des coupures et des règles ``focales''), et doivent être accompagnées d'outils de preuves certifiés.En plus de fournir un cadre fiable, cela nous permet d'encoder de manière correcte nos systèmes biologiques. Pour la biologie des systèmes en général et la biomédecine en particulier, nous avons jusqu'à présent, pour la partie modélisation, trois logiques candidates : toutes basées sur la logique linéaire.Les propriétés étudiées et leurs preuves sont formalisées dans une logique inductive (non linéaire) très expressive : le Calcul des Constructions Inductives (CIC).Les exemples que nous avons étudiés jusqu'à présent sont relativement simples. Cependant, ils sont tous accompagnés de preuves formelles semi-automatiques dans le système Coq, qui implémente CIC. En neurosciences, nous utilisons directement CIC et Coq pour modéliser les neurones et certains circuits neuronaux simples et prouver certaines de leurs propriétés dynamiques.En biomédecine, l'étude des interactions entre des voies multiomiques,ainsi que les études cliniques et les données des dossiers médicaux électroniques devraient aider à la découverte de médicaments et au diagnostic des maladies.Les travaux futurs portent notamment sur l'utilisation de systèmes de preuves plus automatiques.Cela devrait nous permettre de modéliser et d'étudier des exemples plus réalistes,et à terme de fournir un système pour le diagnostic des maladies et le pronostic thérapeutique

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications