16,771 research outputs found

    Engineering simulations for cancer systems biology

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    Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

    Collaboration in the Semantic Grid: a Basis for e-Learning

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    The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning

    Using Toulminā€™s Argument Pattern in the evaluation of argumentation in school science

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    Toulmin's definition of argument has been used by researchers as a theoretical perspective on argument and as a methodological tool for analysing episodes of oral argumentation. An adaptation of Toulmin's framework used by researchers has informed a professional development programme for teachers. Research on the impact of the programme on pedagogic practice shows that Toulmin-based materials are advantageous in helping teachers to conceptualise argument and model it for students. However focus on the process of argumentation limits any consideration of the content and quality of evidence. Toulmin's framework can also be used to evaluate student outcomes when using argumentation software

    Belief Revision in Structured Probabilistic Argumentation

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    In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates
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