Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments

Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood

Grey-box modelling and forecasting of stormwater flow in sewer systems

The goal of this thesis is to wrap up my endeavours in the realm of modelling and probabilistic forecasting. The work is anchored in my two quite different papers and is therefore essentially two-legged. The first leg deals with the development and estimation of a grey-box model suited for forecasting, and this is the primary subject of the thesis. In this case, I study the modelling and forecasting of rainfall-response in a Danish stormwater tunnel, but the focus is really on the modelling process and its inherent challenges rather than the specific case. This exploration would therefore be relevant for practical grey-box modellers within many fields, e.g. wind power forecasting and financial forecasting. The second leg deals with forecast evaluation, and is exemplified by applying some evaluation metrics to the stormwater forecasting model developed in the first leg followed by a discussion of what kind of value is gained from such an evaluation effort. Again, the relevance of the demonstrated work on forecast evaluation is not limited to the specific case, but can be equivalently applied to probabilistic forecasts in other areas

An introduction to multivariate probabilistic forecast evaluation

Probabilistic forecasting is becoming increasingly important for a wide range of applications, especially for energy systems such as forecasting wind power production. A need for proper evaluation of probabilistic forecasts follows naturally with this, because evaluation is the key to improving the forecasts. Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist, we find that there is a need for an introduction with some practical application. In particular, many forecast scenarios in energy systems are inherently multivariate, and while univariate evaluation methods are well understood and documented, only limited and scattered work has been done on their multivariate counterparts. This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods, primarily scoring rules, as well as practical sections that explain how these methods can be calculated and estimated. In three case studies featuring simple autoregressive models, stochastic differential equations and real wind power data, we implement, apply and discuss the logarithmic score, the continuous ranked probability score and the variogram score for forecasting problems of varying dimension. Finally, the advantages and disadvantages of the three scoring rules are highlighted, and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems

IEA wind recommended practice for the implementation of renewable forecasting solutions: hands-on examples for the use of the guideline

For any industry, it is important to establish standards and recommended best practices in order to ensure security of supply with a healthy competition structure. The IEAWind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions (IEA-RP) have been developed by a team of internationally active experts in wind and solar forecasting to fill this gap in the selection and implementation of optimal renewable energy forecasting solutions. The IEA-RP comprises four parts. The first part, Forecast Solution Selection Process, addresses the design of a customised process to select an optimal forecast solution for users specific situations. The second part, Design and Execution of Benchmarks and Trials, addresses the design, execution and analysis of customised forecasting benchmarks and trials. The third part, Forecast Solution Evaluation, describes methods and guidelines for meaningful evaluation of renewable energy forecasts and entire forecast solutions. The fourth part, Meteorological and Power Data Requirements for real-time Forecasting Applications, is a guideline for the selection, deployment and maintenance of meteorological sensors, power measurements and associated data quality control relevant to real-time forecasting. To assist in the practical application of the guideline, we provide three hands-on examples on how to use the guideline to design or improve forecast evaluation (Part 3) and measurement data quality (Part 4) in an efficient and impactful way. In the three use cases we demonstrate (1) evaluation of meteorological parameter forecasts (that could be used as input to a power prediction procedure) at a Danish coastal location, (2) verification of wind power predictions for a substation in the Northwest of Ireland and (3) quality control of meteorological measurements at an offshore location in the North Sea