Abstract
Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.
Highlights
Chronic obstructive lung diseases, the most prominent being that of asthma and chronic obstructive pulmonary disease (COPD), are common and exert a large burden on society [1]
It has been realized that the emergent function of the lung cannot necessarily be predicted using a reductionist approach [3]: that is, resulting function may not be predicted realistically by considering the component parts in isolation. It is the nonlinear interaction between the parts that leads to unexpected properties; the need for the development of multiscale models
Multiscale models of asthma and COPD: the AirPROM approach we will discuss a large European-wide project called AirPROM (Airway disease PRedicting Outcomes through patient-specific computational Modeling; http://www.airprom.eu) [46]. This is an excellent exemplar of the development of multiscale models of the lung applied to obstructive lung disease that relates well to the Synergy-COPD study
Summary
The most prominent being that of asthma and chronic obstructive pulmonary disease (COPD), are common and exert a large burden on society [1]. Multiscale models of asthma and COPD: the AirPROM approach we will discuss a large European-wide project called AirPROM (Airway disease PRedicting Outcomes through patient-specific computational Modeling; http://www.airprom.eu) [46] This is an excellent exemplar of the development of multiscale models of the lung applied to obstructive lung disease that relates well to the Synergy-COPD study (discussed in this supplement [47]). The clinical data consists of both asthmatic and COPD patients from aligned consortia EvA [48], UBIOPRED [49] and BTS Severe Asthma network [50] These data include extensive genomic, transcriptomic and proteomic profiles, detailed lung function with novel small airway physiological measures [51], bronchial challenge studies, CT [52] and HP MRI imaging data, and patient-reported outcomes. This technique provides a methodical way of quantifying the accuracy (and the uncertainty) in a model and will be vital in the clinical acceptance and usage of models
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