Biology subjects

Clayton, R. H.

Publications and source records attributed to Clayton, R. H..

5 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

Implementing a framework of carbon and nitrogen feedback responses into a plant resource allocation model

O_LIThe allocation of resources to roots and shoots can greatly alter total plant mass. Allocation is thought to be the consequence of growth processes (i.e uptake rates, transport rates, growth rates) and the communication between them via signalling mechanisms. Feedbacks that alter growth processes are induced in nature by changes in the internal pools of carbon and nitrogen, but how these function together to define allocation remains unclear. C_LIO_LIWe introduce a framework model of internal feedback responses to changes in plant carbon and nitrogen concentrations. We evaluate how well the model responds to changes in carbon and nitrogen availability by simulating external environmental perturbations that influence the uptake of resources. C_LIO_LIThe model reflects experimental results when looking at the effect of atmospheric CO2 and soil nitrogen concentrations on total plant mass and replicates observed responses to leaf defoliation events. Overall this shows that a combination of known signalling mechanisms are sufficient to reproduce experimentally observed responses to external resource availability. C_LIO_LIModel simulations highlight key areas of uncertainty where more empirical data is needed. In particular, quantitative data is needed to establish the strengths and rates at which feedback responses to carbon and nitrogen substrate concentrations alter growth and uptake rates. C_LI

plant biology↗

bioRxiv2023

Spatial autocorrelation dimension as a potential determinant for the temporal persistence of human atrial and ventricular fibrillation.

Background: Despite being central to atrial fibrillation (AF) and ventricular fibrillation (VF) mechanisms and therapy, the factors governing AF and VF termination are poorly understood. It has been noted that ratio of system size (L) and the two-point spatial correlation length ({xi}2) are associated with time until termination in transient spatiotemporally chaotic systems, but the relationship between these characteristics and termination has not been systematically studied in human AF and VF. Objective: We aimed assess whether the time to cardiac fibrillation termination can be predicted using a novel estimator, the spatial autocorrelation dimension (Di), defined as the ratio of L and {xi}2, in human AF and VF. Methods: Di was computed and compared in a multi-centre, multi-system study with data for sustained versus spontaneously terminating human AF/VF. VF data was collected during coronary-bypass surgery; and AF data during clinically indicated AF ablation. We analyzed: i) VF mapped using a 256-electrode epicardial sock (n=12pts); ii) AF mapped using a 64-electrode constellation basket-catheter (n=15pts); iii) AF mapped using a 16-electrode HD-grid catheter (n=42pts). To investigate temporal fibrillation persistence, the response of AF-episodes to flecainide (n=7pts) was also studied. Results: Spontaneously terminating fibrillation demonstrated a lower Di (P<0.001 all systems). Lower Di was also seen in paroxysmal compared to persistent AF (P=0.002). Post-flecainide, Di decreased over time (P<0.001). Lower Di was also associated with longer-lasting episodes of AF/VF (R2>0.90, P<0.05 in all cases). Using k-means clustering, two distinct clusters and their centroids were identified i) a cluster of spontaneously terminating episodes, and ii) a cluster of sustained epochs. Conclusion: Di predicts the temporal persistence of cardiac fibrillation. This finding provides potentially important insights into a possible common pathway to termination and therapeutic approaches.

physiology↗

bioRxiv2019

Sensitivity and uncertainty analysis of two human atrial cardiac cell models using Gaussian process emulators

Cardiac cell models reconstruct the action potential and calcium dynamics of cardiac myocytes, and are becoming widely used research tools. These models are highly detailed, with many parameters in the equations that describe current flow through ion channels, pumps, and exchangers in the cell membrane, and so it is difficult to link changes in model inputs to model behaviours. The aim of the present study was to undertake sensitivity and uncertainty analysis of two models of the human atrial action potential. We used Gaussian processes to emulate the way that 11 features of the action potential and calcium transient produced by each model depended on a set of. The emulators were trained by maximising likelihood conditional on a set of design data, obtained from 300 model evaluations. For each model evaluation, the set of inputs was obtained from uniform distributions centred on the default values for each parameter, using latin-hypercube sampling. First order and total effect sensitivity indices were calculated for each combination of input and output. First order indices were well correlated with the square root of sensitivity indices obtained by partial least squares regression of the design data. The sensitivity indices highlighted a difference in the balance of inward and outward currents during the plateau phase of the action potential in each model, with the consequence that changes to one parameter can have opposite effects in the two models. Overall the interactions among inputs were not as important as the first order effects, indicating that model parameters tend to have independent effects on the model outputs. This study has shown that Gaussian process emulators are an effective tool for sensitivity and uncertainty analysis of cardiac cell models.\n\nAuthor summaryThe time course of the cardiac action potential is determined by the balance of inward and outward currents across the cell membrane, and these in turn depend on dynamic behaviour of ion channels, pumps and exchangers in the cell membrane. Cardiac cell models reconstruct the action potential by representing transmembrane current as a set of stiff and nonlinear ordinary differential equations. These models capture biophysical detail, but are complex and have large numbers of parameters, so cause and effect relationships are difficult to identify. In recent years there has been an increasing interest in uncertainty and variability in computational models, and a number of tools have been developed. In this study we have used one of these tools, Gaussian process emulators, to compare and contrast two models of the human atrial action potential. We obtained sensitivity indices based on the proportion of variance in a model output that is accounted for by variance in each of the model parameters. These sensitivity indices highlighted the model parameters that had the most influence on the model outputs, and provided a means to make a quantitative comparison between the models.

cell biology↗