Uncertainty quantification and management in multidisciplinary design optimisation.

We analyse the uncertainty present at the structural-sizing stage of aircraft design due to interactions between aeroelastic loading and incomplete structural definition. In particular, we look at critical load case identification: the process of identifying the flight conditions at which the maximum loading conditions occur from sparse, expensive to obtain data. To address this challenge, we investigate the construction of robust emulators: probabilistic models of computer code outputs, which explicitly and reliably model their predictive uncertainty. Using Gaussian process regression, we show how such models can be derived from simple and intuitive considerations about the interactions between parameter inference and data, and via state-of-the- art statistical software, develop a generally applicable and easy to use method for constructing them. The effectiveness of these models is demonstrated on a range of synthetic and engineering test functions. We then use them to approach two facets of critical load case identification: sample efficient searching for the critical cases via Bayesian optimisation, and probabilistic assessment of possible locations for the critical cases from a given sample; the latter facilitating quantitative downselection of candidate load cases by ruling out regions of the search space with a low probability of containing the critical cases, potentially saving a designer many hours of simulation time. Finally, we show how the presence of design variability in the loads analysis implies a stochastic process, and attempt to construct a model for this by parametrisation of its marginal distributions.PhD in Aerospac

Pseudohypoxic HIF pathway activation dysregulates collagen structure-function in human lung fibrosis

Extracellular matrix (ECM) stiffening with downstream activation of mechanosensitive pathways is strongly implicated in fibrosis. We previously reported that altered collagen nanoarchitecture is a key determinant of pathogenetic ECM structure-function in human fibrosis (Jones et al., 2018). Here, through human tissue, bioinformatic and ex vivo studies we provide evidence that hypoxia-inducible factor (HIF) pathway activation is a critical pathway for this process regardless of the oxygen status (pseudohypoxia). Whilst TGFβ increased rate of fibrillar collagen synthesis, HIF pathway activation was required to dysregulate post-translational modification of fibrillar collagen, promoting pyridinoline cross-linking, altering collagen nanostructure, and increasing tissue stiffness. In vitro, knockdown of Factor Inhibiting HIF (FIH), which modulates HIF activity, or oxidative stress caused pseudohypoxic HIF activation in normal fibroblasts. By contrast, endogenous FIH activity was reduced in fibroblasts from patients with lung fibrosis in association with significantly increased normoxic HIF pathway activation. In human lung fibrosis tissue, HIF mediated signalling was increased at sites of active fibrogenesis whilst subpopulations of human lung fibrosis mesenchymal cells had increases in both HIF and oxidative stress scores. Our data demonstrate that oxidative stress can drive pseudohypoxic HIF pathway activation which is a critical regulator of pathogenetic collagen structure-function in fibrosis

Towards an artificial human lung: modelling organ-like complexity to aid mechanistic understanding

Respiratory diseases account for over 5 million deaths yearly and are a huge burden to health-care systems worldwide. Murine models have been of paramount importance to decode human lung biology in vivo, but their genetic, anatomical, physiological and immunological differences with humans significantly hamper successful translation of research into clinical practice. Thus, to clearly understand human lung physiology, development, homeostasis and mechanistic dysregulation that may lead to disease, it is essential to develop models that accurately recreate the extraordinary complexity of the human pulmonary architecture and biology. Recent advances in micro-engineering technology and tissue engineering have allowed the development of more sophisticated models intending to bridge the gap between the native lung and its replicates in vitro Alongside advanced culture techniques, remarkable technological growth in downstream analyses has significantly increased the predictive power of human biology-based in vitro models by allowing capture and quantification of complex signals. Refined integrated multi-omics readouts could lead to an acceleration of the translational pipeline from in vitro experimental settings to drug development and clinical testing in the future. This review highlights the range and complexity of state-of-the-art lung models for different areas of the respiratory system, from nasal to large airways, small airways, and alveoli, with consideration of various aspects of disease states and their potential applications, including pre-clinical drug testing. We explore how development of optimised physiologically relevant in vitro human lung models could accelerate the identification of novel therapeutics with increased potential to translate successfully from the bench to the patient's bedside.</p