Syntactic Markovian Bisimulation for Chemical Reaction Networks

In chemical reaction networks (CRNs) with stochastic semantics based on continuous-time Markov chains (CTMCs), the typically large populations of species cause combinatorially large state spaces. This makes the analysis very difficult in practice and represents the major bottleneck for the applicability of minimization techniques based, for instance, on lumpability. In this paper we present syntactic Markovian bisimulation (SMB), a notion of bisimulation developed in the Larsen-Skou style of probabilistic bisimulation, defined over the structure of a CRN rather than over its underlying CTMC. SMB identifies a lumpable partition of the CTMC state space a priori, in the sense that it is an equivalence relation over species implying that two CTMC states are lumpable when they are invariant with respect to the total population of species within the same equivalence class. We develop an efficient partition-refinement algorithm which computes the largest SMB of a CRN in polynomial time in the number of species and reactions. We also provide an algorithm for obtaining a quotient network from an SMB that induces the lumped CTMC directly, thus avoiding the generation of the state space of the original CRN altogether. In practice, we show that SMB allows significant reductions in a number of models from the literature. Finally, we study SMB with respect to the deterministic semantics of CRNs based on ordinary differential equations (ODEs), where each equation gives the time-course evolution of the concentration of a species. SMB implies forward CRN bisimulation, a recently developed behavioral notion of equivalence for the ODE semantics, in an analogous sense: it yields a smaller ODE system that keeps track of the sums of the solutions for equivalent species.Comment: Extended version (with proofs), of the corresponding paper published at KimFest 2017 (http://kimfest.cs.aau.dk/

Distinct spatial characteristics of industrial and public research collaborations: Evidence from the 5th EU Framework Programme

This study compares the spatial characteristics of industrial R&D networks to those of public research R&D networks (i.e. universities and research organisations). The objective is to measure the impact of geographical separation effects on the constitution of cross-region R&D collaborations for both types of collaboration. We use data on joint research projects funded by the 5th European Framework Programme (FP) to proxy cross-region collaborative activities. The study area is composed of 255 NUTS-2 regions that cover the EU-25 member states (excluding Malta and Cyprus) as well as Norway and Switzerland. We adopt spatial interaction models to analyse how the variation of cross-region industry and public research networks is affected by geography. The results of the spatial analysis provide evidence that geographical factors significantly affect patterns of industrial R&D collaboration, while in the public research sector effects of geography are much smaller. However, the results show that technological distance is the most important factor for both industry and public research cooperative activities.Comment: 28 page

Regional environments and sector developments: the biotech sector in Oxfordshire

This paper explores the interdependence between national policy, the local characteristics of the UK’s biotechnology sectoral system of innovation and the growth of Oxfordshire’s biotech sector. It considers on the one hand the county’s research capacity and on the other its innovation performance. The latter is captured by a series of indicators from a recently completed study of the sector, recording the sector’s evolution both in the number of firms and their employment size, their status (independent, merged/acquired), product group and contribution to local employment and wealth creation. It considers the implications of the relative weaknesses in the system of innovation in this sector which relate to an underperformance of its firms in relation to the strength of the science base

Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology

This paper studies multilateral cooperation networks among organizations and work on a two-mode representation to study the decision to participate in a consortium. Our objective is to explain the underlying processes that give rise to multilateral collaboration networks. Particularly, we are interested in how heterogeneity in organizations' attributes plays a part and in the geographical dimension of this formation process. We use the data on project proposals submitted to the 7th Framework Program (FP) in the area of Life sciences, Biotechnology and Biochemistry for Sustainable Non-Food. We employ exponential random graph models (p* models) (Frank and Strauss, 1986 ; Wasserman and Pattison, 1996) with node attributes (Agneessens et al., 2004), and we make use of extensions for affiliation networks (Wang et al., 2009). These models do not only enable handling variability in consortium sizes but also relax the assumption on tie/triad independence. We obtained some preliminary results indicating institutional types as a source of heterogeneity affecting participation decisions. Also, these initial results point out that organizations take their potential partners' participations in other projects into account in giving their decision ; organizations located in the core European countries tend to participate in the same project ; the tendency to preserve the composition of a consortium across projects and the tendency of organizations with the same institutional type to co-participate are not significant

Innovation capacity in the healthcare sector and historical anchors: examples from the UK, Switzerland and the US

Innovation is an integral part of economic development in developed economies. In the post 2008 period, a key policy agenda is that of sustainable development, which calls for innovation in all aspects of value-chains. In this paper, we focus on innovation from the biotech—pharma perspective to see whether or not this will lead to a sustainable future for the regions where there are clusters of firms in this sector. We examine data from a recently completed European Union study of innovation in the Healthcare sector from the UK and Switzerland, countries with an historical base in pharma, to understand how innovation pathways vary at the regional level in the broader life sciences, which incorporate biotech and more. Innovation in the healthcare sector in two regions, Oxfordshire in the UK and Zurich in Switzerland are compared. We contextualize our discussion by drawing on studies that focus on the sector in the US, specifically Boston. The analytical framework comprises three elements: innovation systems and national and regional economic development theories are the first two, followed by approaches which consider organizational or institutional activity. This framework is used to help explain and understand the complexity of how innovation is organized at the sub-national level. The overall context is that it is increasing becoming a condition for government financing of research that it has more immediate application in industry or have the possibility of commercialisation (e.g., translational research)

The Sixth Framework Program as an Affiliation Network: Representation and Analysis

In this paper, we compare two different representations of Framework Programs as affiliation network: 'One-mode networks' and 'Two-mode networks'. The aim of this article is to show that the choice of the representation has an impact on the analysis of the networks and on the results of the analysis. In order to support our proposals, we present two forms of representation and different indicators used in the analysis. We study the network of the 6th Framework Program using the two forms of representation. In particular, we show that the identification of the central nodes is sensitive to the chosen representation. Furthermore, the nodes forming the core of the network vary according to the representation. These differences of results are important as they can influence innovation policies