Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.
We report patterns of intra- and inter-individual variation in flight direction in the large white butterfly Pieris brassicae (Linnaeus, 1758). The presence of inter-individual variation in flight direction for individuals tested in the same conditions suggests that this trait is inherited in P. bra...
We present a Bayesian approach
To the problem of presence-only data based on a two levels scheme. A
Probability law and a case-control design are combined to handle the double
Source of uncertainty: one due to the censoring and one due to the sampling. We
Propose a new formalization for the logistic...
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