Abstract: The mechanisms underlying complex biological systems are routinely represented as networks, and their kinetics is widely studied. It turns out that relationships between network structures can reveal similarity of mechanism. We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm. Thus, structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks In recent joint work, we established a correspondence between network emulation and a notion of backward bisimulation for continuous systems. An emulation morphism establishes a bisimulation relation over the union of two networks, and a bisimulation relation over a network can be seen as an emulation morphism from the full network to the reduced network of its equivalence classes. Along this correspondence, we obtain minimization algorithms for chemical reaction networks, which are of interest for model execution, and algorithms to discover morphisms between networks, which are of interest for model understanding.
Bio: Luca Cardelli has a Ph.D. in computer science from the University of Edinburgh. He worked at Bell Labs, Murray Hill, from 1982 to 1985, and at Digital Equipment Corporation, Systems Research Center in Palo Alto, from 1985 to 1997, before assuming a position at Microsoft Research, in Cambridge UK, where he was head of the Programming Principles and Tools and Security groups until 2012. Since 2014 he is also a Royal Society Research Professor at the University of Oxford. His main interests are in programming languages and concurrency, and more recently in programmable biology and nanotechnology. He is a Fellow of the Royal Society, a Fellow of the Association for Computing Machinery, an Elected Member of the Academia Europaea, and an Elected Member of the Association Internationale pour les Technologies Objets.
Abstract: We advocate here the use of (mathematical) logic for systems biology, as a unified 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 a sequent calculus presentation enjoying cut-elimination and focusing), and should come with (certified) proof tools. Beyond providing a reliable framework, this allows the adequate encodings of our biological systems. We shall present two candidate logics (two modal extensions of linear logic, called HyLL and SELL), along with biological examples. The examples we have considered so far are very simple ones - coming with completely formal (interactive) proofs in Coq. Future works includes using automatic provers, which would extend existing automatic provers for linear logic. This should enable us to specify and study more realistic examples in systems biology, biomedicine (diagnosis and prognosis), and eventually neuroscience. This talk is based on joint works with Kaustuv Chaudhuri (INRIA Saclay), Amy Felty (Univ. of Ottawa), Pietro Lio (Cambridge Univ.), and Carlos Olarte and Elaine Pimentel (Universidade Federal do Rio Grande do Norte, Brazil).
Bio: Joëlle Despeyroux is a researcher at INRIA-Sophia-Antipolis. She first worked in the semantics of programming languages area, proposing a pure logical view of Gordon Plotkin's Structural Operational Semantics (called "Natural Semantics"), well suited, for example, for proofs of correctness of compilers. She then worked in (mathematical) logic, at several levels: design of new type theories (unifying CCind and LF), proofs on the computer (mainly in Coq), and design and development of (a prototype of) a proof assistant (called "Theo"). More recently, in joint work with Kaustuv Chaudhuri at INRIA-Saclay, she proposed a new (modal linear) logic as a Logical Framework for both specifying and analysing models of biological systems, viewed as transition systems. She is now working in this area, with several co-workers from Cambridge, Ottawa, and Brazil, using this new framework in various areas, from molecular biology to biomedicine. Joëlle Despeyroux has a PhD in Mathematics from Paris University. She worked at INRIA-Roquencourt before moving to INRIA-Sophia-Antipolis.
Abstract: In this talk I will present our journey during the past few years within the very exciting area of neural networks. I will first start with artificial neurones and neural networks and explain why we got interested in such networks. I will then turn to the mathematical modelling of biological neurones and neural networks. We will distinguish between non-spiking neurones, such as the ones found in C. Elegans, and spiking neurones, as found in most of the other larger organisms. We will discuss our work in analysing the behaviour of such networks, the use in control, and the challenges and opportunities in this area.
Bio: Radu Grosu is a full Professor, and the Head of the Institute of Computer Engineering, at the Faculty of Informatics, of the Vienna University of Technology. Grosu is also the Head of the Cyber-Physical-Systems Group within the Institute of Computer-Engineering, and a Research Professor at the Department of Computer Science, of the State University of New York at Stony Brook, USA. The research interests of Radu Grosu include the modeling, the analysis and the control of cyber-physical systems and of biological systems. The applications focus of Radu Grosu includes distributed automotive and avionic systems, IoT, autonomous mobility, green operating systems, mobile ad-hoc networks, cardiac and neural networks, and genetic regulatory networks. Radu Grosu is the recipient of the National Science Foundation Career Award, the State University of New York Research Foundation Promising Inventor Award, the Association for Computing Machinery Service Award, and is an elected member of the International Federation for Information Processing, Working Group 2.2. Before receiving his appointment at the Vienna University of Technology, Radu Grosu was an Associate Professor in the Department of Computer Science, of the State University of New York at Stony Brook, where he co- directed the Concurrent-Systems Laboratory and co-founded the Systems-Biology Laboratory. Radu Grosu earned his doctorate (Dr.rer.nat.) in Computer Science from the Faculty of Informatics of the Technical University München, Germany. He was subsequently a Research Associate in the Department of Computer and Information Science, of the University of Pennsylvania, an Assistant, and an Associate Professor in the Department of Computer Science, of the State University of New York at Stony Brook, USA.
Abstract: Formal modelling languages such as process algebras are effective tools in computational biological modelling. However, handling data and uncertainty in these representations in a statistically meaningful way is an open problem, severely hampering the usefulness of these elegant tools in many real biological applications. I will present ProPPA, a process algebra which incorporates uncertainty in the model description, supporting the use of Machine Learning techniques to integrate observational data in the modelling. I will explain how this is given a semantics in terms of a generalisation of Constraint Markov Chains, and demonstrate how this can be used to perform inference over biological models.
Bio: Jane Hillston is Professor of Quantitative Modelling in the School of Informatics at the University of Edinburgh. Her principal research interests are in the design of formal modelling languages, particularly stochastic process algebras, to model and analyse dynamic systems and the development of efficient solution techniques for such models. These models capture both engineered computer systems and naturally occurring systems such as biochemical pathways and the spread of disease within a population. Prof Hillston received the BA and MS degrees in Mathematics from the University of York (UK) and Lehigh University (USA), respectively. She received the PhD degree in Computer Science from the University of Edinburgh in 1994. Her work on the stochastic process algebra PEPA was recognised by the British Computer Society in 2004 who awarded her the first Roger Needham Award. She was elected to fellowship of the Royal Society of Edinburgh in 2007. She is also a fellow of the British Computer Society.