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Summary Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Availabilityand implementation AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. Supplementary information Supplementary data are available at Bioinformatics online.

In systems and computational biology, ordinary differential equations are used for the mechanistic modelling of biochemical networks. These models can easily have hundreds of states and parameters. Typically most parameters are unknown and estimated by fitting model output to observation. During parameter estimation the model needs to be solved repeatedly, sometimes millions of times. This can then be a computational bottleneck, and limits the employment of such models. In many situations the experimental data provides information about the steady state of the biochemical reaction network. In such cases one only needs to obtain the equilibrium state for a given set of model parameters. In this paper we exploit this fact and solve the steady state problem directly rather than integrating the ODE forward in time until steady state is reached. We use Newton’s method - like some previous studies - and develop several improvements to achieve robust convergence. To address the reliance of Newtons method on good initial guesses, we propose a continuation method. We show that the method works robustly in this setting and achieves a speed up of up to 100 compared to using ODE solves.

$\mu$Dose is a novel compact analytical instrument for assessing low level $^{238}$U, $^{235}$U, $^{232}$Th decay chains and $^{40}$K radioactivity. The system is equipped with a dual $\alpha$/$\beta$ scintillator allowing to discriminate between $\alpha$ and $\beta$ particles. The unique build-in pulse analyzer measures the amplitude of each individual pulse, its shape and the time interval between subsequent pulses. This allows the detection of pulse pairs arising from subsequent decays of $^{214}$Bi/$^{214}$Po, $^{220}$Rn/$^{216}$Po, $^{212}$Bi/$^{212}$Po and $^{219}$Rn/$^{215}$Po. The obtained $\alpha$ and $\beta$ counts and four separate decay pair counts are used to asses the $^{238}$U, $^{235}$U, $^{232}$Th and $^{40}$K specific radioactivities in the measured samples through the use of radioactivity standards. The $\mu$Dose system may be equipped with various photomultipliers and counting containers to assess radionuclide concentrations of samples of masses ranging between 0.4~g and 4~g. As a result, the user can customize the system to their needs and maximize the instrument\textquotesingle s performance. The system is controlled by a dedicated software with a graphical user interface and modules for system calibration, data visualization, specific radioactivity calculations and dose rate determination using the infinite matrix assumption.

Two mathematical models are part of the foundation of Computational neurophysiology; (a) the Cable equation is used to compute the membrane potential of neurons, and, (b) volume-conductor theory describes the extracellular potential around neurons. In the standard procedure for computing extracellular potentials, the transmembrane currents are computed by means of (a) and the extracellular potentials are computed using an explicit sum over analytical point-current source solutions as prescribed by volume conductor theory. Both models are extremely useful as they allow huge simplifications of the computational efforts involved in computing extracellular potentials. However, there are more accurate, though computationally very expensive, models available where the potentials inside and outside the neurons are computed simultaneously in a self-consistent scheme. In the present work we explore the accuracy of the classical models (a) and (b) by comparing them to these more accurate schemes. The main assumption of (a) is that the ephaptic current can be ignored in the derivation of the Cable equation. We find, however, for our examples with stylized neurons, that the ephaptic current is comparable in magnitude to other currents involved in the computations, suggesting that it may be significant—at least in parts of the simulation. The magnitude of the error introduced in the membrane potential is several millivolts, and this error also translates into errors in the predicted extracellular potentials. While the error becomes negligible if we assume the extracellular conductivity to be very large, this assumption is, unfortunately, not easy to justify a priori for all situations of interest.