Biology subjects

Schenkel, T.

Publications and source records attributed to Schenkel, T..

3 recordsLinked to original sources

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↗

bioRxiv2023

EPAS1 Attenuates Atherosclerosis Initiation at Disturbed Flow Sites through Endothelial Fatty Acid Uptake

BackgroundAtherosclerotic plaques form unevenly due to disturbed blood flow, causing localized endothelial cell (EC) dysfunction. Obesity exacerbates this process, but the underlying molecular mechanisms are unclear. The transcription factor EPAS1 (HIF2A) has regulatory roles in endothelium, but its involvement in atherosclerosis remains unexplored. This study investigates the potential interplay between EPAS1, obesity, and atherosclerosis. MethodsResponses to shear stress were analysed using cultured porcine aortic EC exposed to flow in vitro coupled with metabolic and molecular analyses, and by en face immunostaining of murine aortic EC exposed to disturbed flow in vivo. Obesity and dyslipidemia were induced in mice via exposure to high-fat diet or through Leptin gene deletion. The role of Epas1 in atherosclerosis was evaluated by inducible endothelial Epas1 deletion, followed by hypercholesterolemia induction (AAV-PCSK9; high-fat diet). ResultsEn face staining revealed EPAS1 enrichment at sites of disturbed blood flow that are prone to atherosclerosis initiation. Obese mice exhibited substantial reduction in endothelial EPAS1 expression, correlating with hyperlipidaemia. Sulforaphane, a compound with known atheroprotective effects, restored EPAS1 expression and concurrently reduced plasma triglyceride levels in obese mice. Consistently, triglyceride derivatives (free fatty acids) suppressed EPAS1 in cultured EC by upregulating the negative regulator PHD3. Clinical observations revealed that reduced plasma EPAS1 correlated with increased endothelial PHD3 in obese individuals. Functionally, endothelial EPAS1 deletion increased lesion formation in hypercholesterolemic mice, indicating an atheroprotective function. Mechanistic insights revealed that EPAS1 protects arteries by maintaining endothelial proliferation by positively regulating CD36 and LIPG expression to increase fatty acid beta-oxidation. ConclusionsEndothelial EPAS1 attenuates atherosclerosis at sites of disturbed flow by maintaining EC proliferative via fatty acid uptake and metabolism. This endothelial repair pathway is inhibited in obesity, suggesting a novel triglyceride-PHD3 modulation pathway suppressing EPAS1 expression. These findings have implications for therapeutic strategies addressing vascular dysfunction in obesity.

physiology↗