Practical Guide to Large Amplitude Fourier-Transformed Alternating Current Voltammetry-What, How, and Why.

Fourier-transformed alternating current voltammetry (FTacV) is a technique utilizing a combination of a periodic (frequently sinusoidal) oscillation superimposed onto a staircase or linear potential ramp. The advanced utilization of a large amplitude sine wave induces substantial nonlinear current responses. Subsequent filter processing (via Fourier-transformation, band selection, followed by inverse Fourier-transformation) generates a series of harmonics in which rapid electron transfer processes may be separated from non-Faradaic and competing electron transfer processes with slower kinetics.

Understanding the impact of numerical solvers on inference for differential equation models.

Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solvers that seem sufficiently accurate for the , i.e.

Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events.

Case of non-typhoidal spp mycotic popliteal artery aneurysm with concurrent deep vein thrombosis.

Mycotic aneurysms are a well-recognised complication of non-typhoidal bacteraemia; the risk is increased in patients with atherosclerotic disease. The infrarenal abdominal aorta is the most common site of infection; lower extremity aneurysms are uncommon.Here we present the case of a patient with cardiovascular disease and recurrent non-typhoidal bacteraemia, who developed a left-sided popliteal artery mycotic aneurysm with secondary popliteal vein thrombosis.

Model-driven optimal experimental design for calibrating cardiac electrophysiology models.

Background And Objective: Models of the cardiomyocyte action potential have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, action potential models are highly nonlinear, making them difficult to parameterise and limiting to describing 'average cell' dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type.

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling.

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.

Reduction of the rapid delayed rectifier potassium current ( ) drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes.

A Bayesian nonparametric method for detecting rapid changes in disease transmission.

Whether an outbreak of infectious disease is likely to grow or dissipate is determined through the time-varying reproduction number, R. Real-time or retrospective identification of changes in R following the imposition or relaxation of interventions can thus contribute important evidence about disease transmission dynamics which can inform policymaking. Here, we present a method for estimating shifts in R within a renewal model framework.

Models of the cardiac L-type calcium current: A quantitative review.

The L-type calcium current ( ) plays a critical role in cardiac electrophysiology, and models of are vital tools to predict arrhythmogenicity of drugs and mutations. Five decades of measuring and modeling have resulted in several competing theories (encoded in mathematical equations). However, the introduction of new models has not typically been accompanied by a data-driven critical comparison with previous work, so that it is unclear which model is best suited for any particular application.

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number.

During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks.

A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models.

Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine membrane voltage and form action potentials (APs). Although this relationship is usually expressed as a differential equation, previous studies have shown it can be rewritten in an algebraic form, allowing direct calculation of membrane voltage.

Learning transmission dynamics modelling of COVID-19 using comomodels.

The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19.

A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no "gold standard" for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines.

Using the Barthel Index and modified Rankin Scale as Outcome Measures for Stroke Rehabilitation Trials; A Comparison of Minimum Sample Size Requirements.

Objectives: Underpowered trials risk inaccurate results. Recruitment to stroke rehabilitation randomised controlled trials (RCTs) is often a challenge. Statistical simulations offer an important opportunity to explore the adequacy of sample sizes in the context of specific outcome measures.

A Voltammetric Perspective of Multi-Electron and Proton Transfer in Protein Redox Chemistry: Insights From Computational Analysis of HypD Fourier Transformed Alternating Current Voltammetry.

This paper explores the impact of pH on the mechanism of reversible disulfide bond (CysS-SCys) reductive breaking and oxidative formation in hydrogenase maturation factor HypD, a protein which forms a highly stable adsorbed film on a graphite electrode. To achieve this, low frequency (8.96 Hz) Fourier transformed alternating current voltammetric (FTACV) experimental data was used in combination with modelling approaches based on Butler-Volmer theory with a dual polynomial capacitance model, utilizing an automated two-step fitting process conducted within a Bayesian framework.

Barely sufficient practices in scientific computing.

The importance of software to modern research is well understood, as is the way in which software developed for research can support or undermine important research principles of findability, accessibility, interoperability, and reusability (FAIR). We propose a minimal subset of common software engineering principles that enable FAIRness of computational research and can be used as a baseline for software engineering in any research discipline.

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry.

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry.

A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data.

Variation is characteristic of all living systems. Laboratory techniques such as flow cytometry can probe individual cells, and, after decades of experimentation, it is clear that even members of genetically identical cell populations can exhibit differences. To understand whether variation is biologically meaningful, it is essential to discern its source.

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments.

Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study, we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters.

Four Ways to Fit an Ion Channel Model.

Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents.

Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence.

Ion channel behavior can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing, inactivating, and recovery) are commonly represented with Q coefficients or an Eyring relationship. In this article, we assess the validity of these representations by characterizing channel kinetics at multiple temperatures.