Examination of Bayesian belief network for safety assessment of nuclear computer-based systems

We report here on a continuation of work on the Bayesian Belief Network (BBN)model described in [Fenton, Littlewood et al. 1998]. As explained in the previous deliverable, our model concerns one part of the safety assessment task for computer and software based nuclear systems. We have produced a first complete, functioning version of our BBN model by eliciting a large numerical node probability table (NPT) required for our ‘Design Process Performance’ variable. The requirement for such large numerical NPTs poses some difficult questions about how, in general, large NPTs should be elicited from domain experts. We report about the methods we have devised to support the expert in building and validating a BBN. On the one hand, we have proceeded by eliciting approximate descriptions of the expert’s probabilistic beliefs, in terms of properties like stochastic orderings among distributions; on the other hand, we have explored ways of presenting to the expert visual and algebraic descriptions of relations among variables in the BBN, to assist the expert in an ongoing assessment of the validity of the BBN

Hard, soft or lean? Planning on medium size construction projects

In a paper presented to the 11th Annual ARCOM Conference, Johansen examined the way that managers and planners in medium sized construction projects plan in a flexible manner. This was termed "soft planning" and contrasted with the textbook approach which was termed "hard" planning. The fundamental components of hard planning are firm dates and critical activities. The reality was found to be quite different from the textbook approach. (Johansen, 1996a) The conclusion then, was that methods of soft planning methodologies should be developed to support what was actually happening. Here this conclusion is revised in the light of lean production concepts. After defining these concepts, the authors consider how they can affect the development of planning theories in construction; in particular, how concepts such as “shielding”, “lookahead planning” and “last planner” can allow managers to overcome the barriers to hard planning

Engineering simulations for cancer systems biology

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