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The article offers information on how science hackathons can assist in the development of interdisciplinary collaborations and research in the U.S.

The article presents ten simple rules for a successful cross-disciplinary collaboration. Topics discussed include research in experimental biology, need of multidisciplinary collaborations, need to recognize the publication culture in the life sciences and in experimental biology, and publication of information technology research in peer-reviewed conference proceedings instead of journals.

Background: Parameter estimation for differential equation models of intracellular processes is a highly relevant bu challenging task. The available experimental data do not usually contain enough information to identify all parameters uniquely, resulting in ill-posed estimation problems with often highly correlated parameters. Sampling-based Bayesian statistical approaches are appropriate for tackling this problem. The samples are typically generated via Markov chain Monte Carlo, however such methods are computationally expensive and their convergence may be slow, especially if there are strong correlations between parameters. Monte Carlo methods based on Euclidean or Riemannian Hamiltonian dynamics have been shown to outperform other samplers by making proposal moves that take the local sensitivities of the system’s states into account and accepting these moves with high probability. However, the high computational cost involved with calculating the Hamiltonian trajectories prevents their widespread use for all but the smallest differential equation models. The further development of efficient sampling algorithms is therefore an important step towards improving the statistical analysis of predictive models of intracellular processes. Results: We show how state of the art Hamiltonian Monte Carlo methods may be significantly improved for steady state dynamical models. We present a novel approach for efficiently calculating the required geometric quantities by tracking steady states across the Hamiltonian trajectories using a Newton-Raphson method and employing local sensitivity information. Using our approach, we compare both Euclidean and Riemannian versions of Hamiltonian Monte Carlo on three models for intracellular processes with real data and demonstrate at least an order of magnitude improvement in the effective sampling speed. We further demonstrate the wider applicability of our approach to other gradient based MCMC methods, such as those based on Langevin diffusions. Conclusion: Our approach is strictly benefitial in all test cases. The Matlab sources implementing our MCMC methodology is available from https://github.com/a-kramer/ode_rmhmc. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the effect of the initial configurations of the governing equations on flows in a collapsible channel where the upper elastic wall is replaced by a pre-stretched beam. The aim is to check the existence of a “tongue” shaped neutral stability curve in the Reynolds number–tension space from a fluid-beam model [Luo XY, Cai ZX. Effects of wall stiffness on the linear stability of flow in an elastic channel. In: de Langre E, Axisa F, editors. Proceedings of the eighth international conference on flow-induced vibrations, FIV2004, vol. II. Paris, France: 2004. p. 167–70], in a properly formulated initial strain configuration. It was found that, for a given Reynolds number, as the tension is lowered to a critical value, the system becomes unstable, which is to be expected. However, a further decrease of the tension re-stabilizes the system before it becomes unstable again. It was possible that this puzzling finding was an artefact since the elastic equations used in the model were not properly derived from the zero initial stress configuration (Ogden, private communication). To check this, in this paper, a range of steady solutions are studied with both zero and non-zero initial wall tension. These are compared with the results using the finite element package Adina 8.3 using both the initial strain and initial stress configurations. As expected, the fluid-beam model agrees with Adina when using the initial stress configuration, but not when using the initial strain configuration. For cases with a small initial tension or small deformation (very large initial tension), both initial stress and initial strain configurations lead to very similar results, however, when the initial tension is comparable with the stretching induced tension, there are obvious differences in these two configurations. The “tongue” stability curve is then re-calculated with a zero initial tension, and re-plotted in the Reynolds number–effective tension space. It is interesting to see that though slightly different in shape, the “tongue” stable zone appears again when the zero initial tension is used. Thus it is highly likely that the puzzling “tongue” in the neutral stability curve is not due to the modelling approximation, but indicating a real, interesting physical phenomenon. [Copyright &y& Elsevier]

The authors discuss the factors which should be considered by computational scientists for developing and using software for effective computational scientific research based on their research experiences. Topics include developing a prototype before writing full code, using online tools to keep a track of the work, and using visualisation and graphics software and tools to effectively communicate the research findings. Also suggested is the use of an appropriate numerical method for the same.

The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available., (Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

We consider the Bayesian analysis of mechanistic models describing the dynamic behavior of ligand-gated ion channels. The opening of the transmembrane pore in an ion channel is brought about by conformational changes in the protein, which results in a flow of ions through the pore. Remarkably, given the diameter of the pore, the flow of ions from a small number of channels or indeed from a single ion channel molecule can be recorded experimentally. This produces a large time-series of high-resolution experimental data, which can be used to investigate the gating process of these channels. We give a brief overview of the achievements and limitations of alternative maximum-likelihood approaches to this type of modeling, before investigating the statistical issues associated with analyzing stochastic model reaction mechanisms from a Bayesian perspective. Finally, we compare a number of Markov chain Monte Carlo algorithms that may be used to tackle this challenging inference problem.

Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.