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RISS 인기검색어
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- 저자 : Wellstead, P. (검색결과 3 건)
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What is co-creation and how does it create public value?
Wellstead Adam,Howlett Michael,Chakrabarty Aritra 한국행정학회 2022 International Review of Public Administration Vol.27 No.4
Knowledge-based policy influence organizations such as think tanks or policy hubs seek to develop public value through the kinds of knowledge utilization activities in which they are engaged. But what exactly do they do, how do they do it and how does this result in public value? While historically public managers have long been assumed to be the principal purveyors of public value, now many policy actors in their various activities claim to seek to enhance public value and can do so in a number of distinct ways requiring a reassessment of the kinds of activities and efforts they are engaged in. This paper looks at key differences between longestablished organizations like think tanks and newcomers like policy labs to see how they differ in focus upon co-creation, co-design, and co-production in their search for public value.
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The dynamic systems approach to control and regulation of intracellular networks
Wolkenhauer, Olaf,Ullah, Mukhtar,Wellstead, Peter,Cho, Kwang-Hyun Elsevier 2005 FEBS letters Vol.579 No.8
<P><B>Abstract</B></P><P>Systems theory and cell biology have enjoyed a long relationship that has received renewed interest in recent years in the context of <I>systems biology</I>. The term ‘systems’ in systems biology comes from <I>systems theory</I> or <I>dynamic systems theory</I>: systems biology is defined through the application of systems- and signal-oriented approaches for an understanding of inter- and intra-cellular dynamic processes. The aim of the present text is to review the systems and control perspective of dynamic systems. The biologist’s conceptual framework for representing the variables of a biochemical reaction network, and for describing their relationships, are pathway maps. A principal goal of systems biology is to turn these static maps into dynamic models, which can provide insight into the temporal evolution of biochemical reaction networks. Towards this end, we review the case for differential equation models as a ‘natural’ representation of causal entailment in pathways. Block-diagrams, commonly used in the engineering sciences, are introduced and compared to pathway maps. The stimulus–response representation of a molecular system is a necessary condition for an understanding of dynamic interactions among the components that make up a pathway. Using simple examples, we show how biochemical reactions are modelled in the dynamic systems framework and visualized using block-diagrams.</P>
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Cho, K.H.,Choo, S.M.,Wellstead, P.,Wolkenhauer, O. North-Holland Pub ; Elsevier Science Ltd 2005 FEBS letters Vol.579 No.20
We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper. The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841-12846) and Sontag et al. (Bioinformatics 20 (2004) 1877-1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example.
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