Modelling the complexities of chronic disease
The advent of genomics and proteomics has meant an explosion in data that now needs large-scale integration to maximise the translation into biomedical technologies. A major obstacle, particularly for small and medium-sized enterprises (SMEs), is the lack of appropriate tools for the generation of dynamic models. Aware of this shortcoming, the 'Integrative genomics and chronic disease phenotypes: modelling and simulation tools for clinicians' (Biobridge) project aimed to integrate databases containing 'omic' information. The aim was to incorporate genomic, proteomic and metabolomic data into appropriate metabolic pathways and then enter these into a simulation environment to improve understanding of complex metabolic processes. Biobridge selected the nitrosoredox imbalance in the cardiovascular system as a test case. It is believed that disequilibrium in nitrosoredox underpins three chronic disorders – chronic heart failure, chronic obstructive pulmonary disease (COPD) and diabetes. Not only do these disorders share common metabolic mechanisms but as they govern oxygen utilisation, they all present poor prognoses and use up a high proportion of healthcare resources. Integrative translational research is a very appropriate approach for these disorders. The genetics underlying susceptibility together with environmental components such as diet and smoking create an extremely complex web to unravel. Researchers focused on the effects of skeletal muscle endurance training in healthy subjects as compared with COPD patients. Different levels of the system such as blood and skeletal muscle were analysed for multidimensional factors including the 'omics', as well as physiological measurements. Two groups of COPD patients in two exercise regimes, with and without muscle wasting, were compared against healthy control subjects. Other approaches filled gaps left in analyses. For example, animal studies and cell culture helped to solve questions posed using the data analysis bioinformatics approach. Other studies in the course of the project included one on diabetes type II through data mining and another on links between body mass composition and exercise capacity in COPD patients. Biobridge successfully generated the desired end results through simulation using multilevel integrated data sources. Milestone achievements include preparation of tools for probabilistic modelling, development of deterministic modelling of oxygen transport and reactive oxygen species production and integration of the simulation environment to the Biobridge web portal. The simulation tools developed may help to identify appropriate biomarkers for non-invasive monitoring of nitrosoredox imbalance and offer new means for clinicians to improve efficiency of healthcare resources. The systems may be applicable to other chronic conditions and may well lead to personalised health strategies at early disease stages.