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Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability. Author summary: Understanding the causes of heterogeneity and the means by which it can be controlled is crucial for manipulating cellular populations and treating diseases. To this end, single-cell time-lapse microscopy data is often combined with dynamic modelling. However, the construction of mechanistic models requires the ability to infer unknown model quantities from data, while simultaneously accounting for intrinsic and extrinsic noise. Here we propose a Bayesian inference framework which enabled us to elucidate sources of cell-to-cell variability in yeast signalling and provides deeper insights into the causes and consequences of heterogeneity. Our approach is versatile and can for example further be applied in pharmacokinetic and pharmacodynamic studies, epidemic studies, as well when modelling the behaviour of cancer cell populations. [ABSTRACT FROM AUTHOR]

Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in various processes, such as metabolism, cell cycle and autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarizes the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling. [ABSTRACT FROM AUTHOR]

The aim of the study was to investigate whether a) the option to make video-review appeals influences the match outcome; b) the number of video reviews differs depending on the time period during a match; and the rejected-to-approved-appeal distribution differs depending on the c) time period during the match, d) match status, and e) match category in karate kumite. Video recordings from 555 matches in a Premier League competition were analysed. Point(s) awarded after the video review(s) changed the match outcome for a total of 47 matches, which corresponded to 13.1% of the matches with video review(s). The number of appealed video reviews increased for each 30-s time period in the 3-min matches (P < 0.001). The rejected-to-approved-appeal distribution differed depending on the time period during the match (P = 0.0025) but not depending on match status and match category (both P > 0.05). For karateka who had fewer points than the opponent, the rejected-to-approved-appeal distribution differed between time periods (P = 0.017). The option for coaches to make video-review appeals for techniques that were not awarded point(s) by the judges increased the chance for a fair match outcome in elite karate kumite. [ABSTRACT FROM AUTHOR]

Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae , the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism. [ABSTRACT FROM AUTHOR]

In large two-stroke marine diesel engines, bearings are designed to last the lifetime of the engine. The design has shown very good service experiences. The design parameters of the main bearings are, among others, based on the average maximum specific load which the bearing should operate under. In general, the frictional loss is less than 1% of the nominal power of the engine but is still a target for optimization. Fatigue mechanisms of bearing lining material are not fully understood and the design limits with regards to minimum oil film thickness, max oil film pressure and oil film pressure gradient are not established. Large two-stroke journal bearings are not suitable for fatigue test due to the size, the low rotational speed and the complexity of such a test-rig. The disc fatigue test rig was designed with the purpose to test white metal coatings under realistic bearing conditions, in a confined time-frame. The test-rig simulates a scale model of a thrust bearing, in contrary to standard design, the bearing lining material is applied to the rotating collar. Parameters, such as bearing load, rotational speed, oil temperature, oil contamination is controlled/monitored in order to achieve repeatability and a systematic approach to the experiments. Test performed on the test-rig shows good correlation on the fatigue cracks with those experienced on large two-stroke journal bearings. [ABSTRACT FROM AUTHOR]

The AMPK/SNF1 pathway governs energy balance in eukaryotic cells, notably influencing glucose de-repression. In S. cerevisiae , Snf1 is phosphorylated and hence activated upon glucose depletion. This activation is required but is not sufficient for mediating glucose de-repression, indicating further glucose-dependent regulation mechanisms. Employing fluorescence recovery after photobleaching (FRAP) in conjunction with non-linear mixed effects modelling, we explore the spatial dynamics of Snf1 as well as the relationship between Snf1 phosphorylation and its target Mig1 controlled by hexose sugars. Our results suggest that inactivation of Snf1 modulates Mig1 localization and that the kinetic of Snf1 localization to the nucleus is modulated by the presence of non-fermentable carbon sources. Our data offer insight into the true complexity of regulation of this central signaling pathway in orchestrating cellular responses to fluctuating environmental cues. These insights not only expand our understanding of glucose homeostasis but also pave the way for further studies evaluating the importance of Snf1 localization in relation to its phosphorylation state and regulation of downstream targets., Competing Interests: None declared.