Biology subjects

Halliday, I.

Publications and source records attributed to Halliday, I..

3 recordsLinked to original sources

bioRxiv2026

Iliac vein morphology and wall shear stress: a statistical shape modelling and CFD analysis of patient-specific geometries

Deep vein thrombosis (DVT) is a prevalent vascular condition in which venous anatomy and flow disturbances contribute to the risk of thrombosis, but the mechanistic links between vessel shape and haemodynamics remain poorly quantified. Although computational fluid dynamics (CFD) can estimate flow-related risk metrics such as low wall shear stress (WSS), the influence of anatomical fidelity on these predictions is not well understood. Statistical shape modelling (SSM) offers a principled framework for characterising geometric variability, but its integration with CFD in venous applications is still emerging. This study investigates how different levels of anatomical representation--2D projections, simplified 3D extrusions, and full 3D reconstructions of the common iliac veins--influence both the statistical structure of venous shape variability and the haemodynamic metrics derived from CFD. Using patient-specific MRI/CT data from twelve cases, we constructed SSMs in Deformetrica and performed steady-state CFD simulations in ANSYS Fluent under standardised inflow conditions. We compared the variance structure of the 2D and 3D latent spaces and quantified correlations between principal shape modes and low-WSS burden across three thresholds ([≤] 0.05, 0.10, 0.15 [Pa]). Idealised 3D geometries consistently produced larger low-WSS areas than patient-specific shapes, with average increases of 118-136% across thresholds. The 2D SSM exhibited a strongly hierarchical variance spectrum with one dominant mode that correlated significantly with WSS, whereas the 3D SSM showed a flatter spectrum with weaker univariate associations. These findings demonstrate that geometric fidelity and alignment strategy critically influence shape-flow relationships, highlighting the need for careful model selection when using CFD-based haemodynamic indicators in DVT research. Author summaryDeep vein thrombosis (DVT) is a common condition in which blood clots form in the deep veins of the leg and can lead to serious long-term complications. Although medical imaging captures important anatomical differences between patients, it remains unclear how these variations in vein shape influence local blood flow and the associated risk of clot formation. To address this challenge, we developed a computational framework that combines statistical shape modelling (SSM) with computational fluid dynamics (CFD) to analyse the relationship between venous geometry and haemodynamic risk factors. We examined the common iliac veins at three levels of anatomical detail: simplified two-dimensional projections, intermediate three-dimensional extrusions, and full three-dimensional reconstructions derived from MRI/CT data. By comparing these representations, we show that geometric fidelity strongly affects both the detected modes of anatomical variation and the resulting flow predictions. Simplified geometries consistently overestimated regions of low wall shear stress, a flow feature associated with thrombosis, compared to full 3D models. We also found that shape-flow associations depend heavily on how shapes are aligned and represented. Our findings highlight the importance of anatomical detail in computational venous modelling and provide a foundation for more personalised, simulation-based tools to support DVT treatment.

bioengineering↗

bioRxiv2024

THE IMPACT OF EXPERIMENTAL DESIGNS & SYSTEM SLOPPINESS ON THE PERSONALISATION PROCESS: A CARDIOVASCULAR PERSPECTIVE

To employ a reduced-order cardiovascular model as a digital twin for personalised medicine, it is essential to understand how uncertainties in the models input parameters affect its outputs. The aim is to identify a set of input parameters that can serve as clinical biomarkers, providing insight into a patients physiological state. Given the challenge of finding useful clinical data, careful consideration must be given to the experimental design used to acquire patient-specific input parameters. In this paper, we conduct the first quantification of a cardiovascular systems sloppiness to elucidate the structure of the input parameter space. By utilising Sobol indices and examining various synthetic cardiovascular measures with increasing invasiveness, we uncover how the personalisation process and the cardiovascular systems sloppiness are contingent upon the chosen experimental design. Our findings reveal that continuous clinical measures induce system sloppiness and increase the number of personalisable biomarkers, whereas discrete clinical measurements produce a non-sloppy system with a reduced number of biomarkers. This study underscores the necessity for careful consideration of available clinical data as differing measurement sets can significantly impact model personalisation. Author SummaryIn personalised medicine, computational models that replicate physical systems -- are becoming vital tools for understanding and predicting individual health. Our study explores cardiovascular models, which simulate heart and circulatory functions from which clinical metrics may be derived. These models aim to provide personalised insights into heart health and treatment planning. A key challenge in building these models is addressing "sloppiness," a property which provides vital insight into the response surface structure for which one calibrates a model searching for a global minimum point, a position in parameter space which best represents a patients cardiovascular health. In order to personalise a model different types of clinical metrics must be available for a model response to be compared to. We examined how different types of clinical data -- ranging from simple discrete blood pressure readings to detailed invasive continuous waveform data -- impact model sloppiness and the number of personalisable biomarkers. Our results show that continuous measurements increase the number of personalisable biomarkers but make the personalisation process more complex through increased sloppiness. In contrast, simpler discrete measurements reduce model sloppiness simplifying the personalisation process but yield fewer personalisable biomarkers. By analysing the impact of experimental designs on the personalisation process, our work offers practical insights into improving the reliability of cardiovascular digital twins, supporting their adoption in personalised medicine.

systems biology↗

bioRxiv2024

CONVERGENCE, SAMPLING AND TOTAL ORDER ESTIMATOR EFFECTS ON PARAMETER ORTHOGONALITY IN GLOBAL SENSITIVITY ANALYSIS

AO_SCPLOWBSTRACTC_SCPLOWDynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences. Research HighlightsO_LIWe conduct a heuristic investigation utilising 2 physiologically intuitive, highly nonlinear and stiff, lumped parameter models. C_LIO_LIThe Janon and Jansen estimators emerge as optimal choices for calculating parameter orthogonality, as they are insensitive to sampling methodologies and measurement types. C_LIO_LIThe Janon and Jansen estimators prove to have the most efficient convergence rates in calculating total order indices. C_LIO_LIThe convergence rate of an estimator appears to be decisive in its ability to truthfully and uniformly recover true indices and orthogonality. C_LIO_LIOur methods provide putative best practice for practical identifiability investigations. C_LI Author SummaryIn order to gain a new insight into biological systems one often uses a mathematical model to predict possible responses from the system of interest. One vital step when using such models is knowledge of the uncertainty associated with a model response given a change in the inputs provided to the model. Utilising two non-linear and stiff cardiovascular models as test cases we investigate the effects of different choices made when quantifying the uncertainty in a mathematical model. Leveraging efficient solving of the mathematical model we are able to show that in order to truly quantify the effects of inputs on a set of outputs one must ensure converged estimates of the inputs influence. Without this, identifying inputs of a model become uncertain, or clinically, non patient specific. Our detailed study provides a workflow and advice for mathematical models of biological systems thus ensuring a true interpretation of the uncertainty associated with model inputs.

systems biology↗