The virtual physiological human: computer simulation for integrative biomedicine II

Multiscale computing for science and engineering in the era of exascale performance

In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.

A vision and strategy for the virtual physiological human: 2012 update

European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595-2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.

A tentative taxonomy for predictive models in relation to their falsifiability

The growing importance of predictive models in biomedical research raises some concerns on the correct methodological approach to the falsification of such models, as they are developed in interdisciplinary research contexts between physics, biology and medicine. In each of these research sectors, there are established methods to develop cause-effect explanations for observed phenomena, which can be used to predict: epidemiological models, biochemical models, biophysical models, Bayesian models, neural networks, etc. Each research sector has accepted processes to verify how correct these models are (falsification). But interdisciplinary research imposes a broader perspective, which encompasses all possible models in a general methodological framework of falsification. The present paper proposes a general definition of 'scientific model' that makes it possible to categorize predictive models into broad categories. For each of these categories, generic falsification strategies are proposed, except for the so-called 'abductive' models. For this category, which includes artificial neural networks, Bayesian models and integrative models, the definition of a generic falsification strategy requires further investigation by researchers and philosophers of science.