From in vivo to in silico biology and back

Abstract

The massive acquisition of data in molecular and cellular biology has led to the renaissance of an old topic: simulations of biological systems. Simulations, increasingly paired with experiments, are being successfully and routinely used by computational biologists to understand and predict the quantitative behaviour of complex systems, and to drive new experiments. Nevertheless, many experimentalists still consider simulations an esoteric discipline only for initiates. Suspicion towards simulations should dissipate as the limitations and advantages of their application are better appreciated, opening the door to their permanent adoption in everyday research.

Diverse metabolic model parameters generate similar methionine cycle dynamics

“…Constraints-based approaches to modeling metabolism, such as flux-balance analysis, have substantial power for predicting metabolic fluxes in certain organisms (Edwards et al, 2001;Schilling and Palsson, 1998;Steffen et al, 2002;Yuan et al, 2006). Despite this progress, there remains the need to also develop dynamic metabolic models which incorporate understanding of metabolite regulation to predict both metabolite concentrations and fluxes (Di Ventura et al, 2006;Gombert and Nielsen, 2000). The development of such models has been limited in part by the challenge of model parameter identification from laboratory data.…”

The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery

“…Studying biological subsystems, their organization and their mutual interactions, through an interplay between laboratory experiments and modelling and simulation, should lead to an understanding of biological function and to a prediction of the effects of perturbations (e.g. genetic mutations or the presence of drugs; Di Ventura et al 2006). The concept 'from genes to health' is the vision of the Physiome (Hunter et al 2006) and the ViroLab (Sloot et al 2006) projects, where multiscale modelling and simulation of the aspects of human physiology are the ultimate goal.…”

Computational modeling of brain tumors: discrete, continuum or hybrid?

“…Already, this sprawling interdisciplinary field draws increasing attention from an experimental and clinical as well as pharmaceutical perspective [14,21,31]. A better understanding of the inherent complexity of these cancer systems requires intensified interdisciplinary research in which the next iteration of innovative computational models, informed by and continuously revised with experimental data, will play an important role of guiding experimental interpretation and design in going forward [29].…”