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Methane (CH4) emissions from dairy husbandry are a hot topic in the context of active climate protection, where housing systems with slatted floors and slurry storage inside are in general expected to emit more than systems with solid floors. There are multiple factors, including climate conditions, that modulate the emission pattern. In this study, we investigated interrelations between CH4 emission patterns and climate conditions as well as differences between farm locations versus floor effects. We considered three data sets with 265, 264 and 275 hourly emission values from two housing systems (one slatted, one solid floor) in Switzerland and one system with solid floors in Germany. Each data set incorporated measurements in summer, winter and a transition season. The average CH4 emission was highest for the slatted floor system. For the solid floor systems, CH4 emissions at the Swiss location were around 30% higher compared to the German location. The shape of the distributions for the two solid floor systems was rather similar but very different from the distribution for the slatted floor system, which showed higher prevalence for extreme emissions. Rank correlations, which measure the degree of similarity between two rankings in terms of linear relation, were not able to detect dependencies at the selected significance level. In contrast, mutual information, which measures more general statistical dependencies in terms of shared information, revealed highly significant dependencies for almost all variable pairs. The weakest statistical relation was found between winds speed and CH4 emission, but the convection regime was found to play a key role. Clustering was consistent among the three data sets with five typical clusters related to high/low temperature and wind speed, respectively, as well as in some cases to morning and evening hours. Our analysis showed that despite the disparate and often insignificant correlation between environmental variables and CH4 emission, there is a strong relation between both, which shapes the emission pattern in many aspects much more in addition to differences in the floor type. Although a clear distinction of high and low emission condition clusters based on the selected environmental variables was not possible, trends were clearly visible. Further research with larger data sets is advisable to verify the detected trends and enable prognoses for husbandry systems under different climate conditions.

The air change rate (ACR) of naturally ventilated dairy buildings (NVDBs) plays an important part in the design and control of the ventilation system, as well as in the estimation of the gaseous emission rate. The objectives of this research were to model the ACR based on a quantitative investigation of the relationship between the ACR and its potential influencing factors, including the opening ratio (r), the building length to width ratio (α), the wind speed (U), and the wind direction (θ). The investigations were performed using the response surface methodology integrated with the Box-Behnken design and Computational Fluid Dynamics (CFD) simulations. Three response surface models of the ACR of NVDBs were established for three opening ratio ranges of 5%–42.5%, 42.5%–80%, and 5%–80%, respectively. It was found that the selection of the opening ratio range had almost no effect on the developed response surface models. The results showed that the ACR of NVDBs was not influenced by α, but was significantly affected by r, U, θ, and interaction effects between every two of the three factors. The highest ACR was 6.7 s−1, 6.0 s−1, and 4.0 s−1 when θ, U, and r was at their respective medium value while the rest parameters were at the highest values, indicating that the r played an important role in the value of ACR. It was concluded that in the prediction of the ACR of a building, the influences of both individual and interactional effects of θ, U, and r should be considered.

The increasing amount of photovoltaic (PV) systems leads to a voltage rise in the distribution grid. By providing reactive power, PV and battery inverters can reduce the voltage. For this purpose, most inverters have implemented a voltage-dependent reactive power curve, which is often parameterised once during the installation of the system. In most cases, the parameterisation does not take into account the position of the system in the grid. As a result, inverters closer to the distribution transformer provide less or no reactive power as the voltage at the beginning of a feeder is much smaller. This study presents a regulation tool, which parameterises the reactive power curve in dependence on the position in the grid. This leads to a coordinated behaviour of all inverters in a distribution grid and ensures that all inverters provide a similar amount of reactive power. Moreover, the regulation tool notices if the grid topology has changed and adapts the reactive power control to this change.

Gaseous emissions from livestock buildings have been a research focus for many years. In particular, quantifying emissions from naturally ventilated buildings is not trivial because it requires accurately determining the air exchange rate (AER) and representative gaseous concentration values in the exhaust and in the incoming air. Improvements are required for the method for estimating AERs and for selecting representative sampling points and locations in naturally ventilated dairy buildings (NVB). The objective of this study was to investigate the magnitude and sources of uncertainties in calculating AERs. A long-term experiment was performed in an NVB located in north-east Germany. CO 2 concentrations were continuously measured inside the barn at eight uniformly distributed points and outside the barn at four points. Sensitivity studies on the calculation of AER were performed by varying the indoor and outdoor sampling points, sampling duration and animal parameters used for the calculation. The sensitivity results were compared to the best available data approximation (BADA), which is based on the best knowledge of these parameters. All factors were evaluated and ordered in terms of their influence on AER calculation and uncertainty. The results show that the sampling duration and the number and location of indoor sampling points have the largest effects on AER uncertainty. Data on milk yield and days since insemination have the lowest influence on AER uncertainty. The information collected in this long-term study is very important for planning measurement campaigns in the future.

The airflow in dairy barns is affected by many factors, such as the barn’s geometry, weather conditions, configurations of the openings, cows acting as heat sources, flow obstacles, etc. Computational fluids dynamics (CFD) has the advantages of providing detailed airflow information and allowing fully-controlled boundary conditions, and therefore is widely used in livestock building research. However, due to the limited computing power, numerous animals are difficult to be designed in detail. Consequently, there is the need to develop and use smart numerical models in order to reduce the computing power needed while at the same time keeping a comparable level of accuracy. In this work the porous medium modeling is considered to solve this problem using Ansys Fluent. A comparison between an animal occupied zone (AOZ) filled with randomly arranged 22 simplified cows’ geometry model (CM) and the porous medium model (PMM) of it, was made. Anisotropic behavior of the PMM was implemented in the porous modeling to account for turbulence influences. The velocity at the inlet of the domain has been varied from 0.1 m s−1 to 3 m s−1 and the temperature difference between the animals and the incoming air was set at 20 K. Leading to Richardson numbers Ri corresponding to the three types of heat transfer convection, i.e. natural, mixed and forced convection. It has been found that the difference between two models (the cow geometry model and the PMM) was around 2% for the pressure drop and less than 6% for the convective heat transfer. Further the usefulness of parametrized PMM with a velocity adaptive pressure drop and heat transfer coefficient is shown by velocity field validation of an on-farm measurement.

Airflow inside naturally ventilated dairy (NVD) buildings is highly variable and difficult to understand due to the lack of precious measuring techniques with the existing methods. Computational fluid dynamics (CFD) was applied to investigate the effect of different seasonal opening combinations of an NVD building on airflow patterns and airflow rate inside the NVD building as an alternative to full scale and scale model experiments. ANSYS 2019R2 was used for creating model geometry, meshing, and simulation. Eight ventilation opening combinations and 10 different reference air velocities were used for the series of simulation. The data measured in a large boundary layer wind tunnel using a 1:100 scale model of the NVD building was used for CFD model validation. The results show that CFD using standard k-&epsilon, turbulence model was capable of simulating airflow in and outside of the NVD building. Airflow patterns were different for different opening scenarios at the same external wind speed, which may affect cow comfort and gaseous emissions. Guiding inlet air by controlling openings may ensure animal comfort and minimize emissions. Non-isothermal and transient simulations of NVD buildings should be carried out for better understanding of airflow patterns.

Emissions and ventilation rates (VRs) in naturally ventilated dairy barns (NVDBs) are usually measured using indirect methods, where the choice of inside and outside sampling locations (i.e. sampling strategy) is crucial. The goal of this study was to quantify the influence of the sampling strategy on the estimation of emissions and VRs. We equipped a NVDB in northern Germany with an extensive measuring setup capable of measuring emissions under all wind conditions. Ammonia (NH3) and carbon dioxide (CO2) concentrations were measured with two Fourier-transform infrared spectrometers. Hourly values for ventilation rates and emissions for ammonia over a period of nearly a year were derived using the CO2 balance method and five different sampling strategies for the acquisition of indoor and outdoor concentrations were applied. When comparing the strategy estimating the highest emission level to the strategy estimating the lowest, the differences in NH3 emissions in winter, transition, and summer season were thorn 26%, thorn 19% and thorn 11%, respectively. For the ventilation rates, the differences were thorn 80%, thorn 94%, and 63% for the winter, transition and summer season, respectively. By accommodating inside/outside concentration measurements around the entire perimeter of the barn instead of a reduced part of the perimeter (aligned to a presumed main wind direction), the amount of available data substantially increased for around 210% for the same monitoring period.

Advective heat and mass transfer, due to pressure differences created by wind or buoyancy, dominates the exchange processes in naturally ventilated structures. The air exchange rate (AER) quantifies this transfer. The airflow patterns including air velocities and turbulences govern the indoor environmental parameters such as temperature, gases and humidity. These patterns form the essential link between the outdoor environment and the buildings microclimate; thus, an understanding of the principles of air motion is necessary in order to provide the correct quantities of air and the proper distribution patterns to meet the needs of the application. Computational fluid dynamics (CFD) have been applied in very limited studies for naturally ventilated animal houses considering wind directions and surrounding buildings. This paper presents isothermal CFD simulations with a naturally ventilated dairy (NVD) barn model to assess the influence of wind direction and surroundings on the AER and indoor and outdoor airflow distributions. A typical NVD building and its surroundings located in Northeast Germany were selected for model development and simulation. ANSYS Workbench 2020R1 platform (ANSYS Inc) was used for creating model geometry, meshing and simulation. Simulations were performed for four wind directions and for a computation model with and without surrounding buildings. The standard kinetic energy (k)-dissipation (e) turbulence model was used for all simulations. In order to improve the quality of CFD calculations, i.e. to make calculations with sufficient accuracy, several important issues (e.g. grid independency and convergence criteria etc) were considered carefully for both the governing equations and the computation. CFD validation was performed with the measured data from a boundary layer wind tunnel under strictly controlled laboratory conditions taking into account full scale measurement. The results showed that the simulated differences in AER between wind directions can go up to 52.2% (without surroundings) and 65.1% (with surroundings). Furthermore, comparing the simulations with and without surrounding buildings showed that neglecting the surroundings can lead to overestimation of the AER with up to 52 %. Further investigations are required to understand the airflow pattern and estimate AER in unsteady conditions considering wind directions and surroundings.

Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.

The discharge coefficient (Cd) is a characteristic parameter of an opening, which represents the effect of flow contraction and frictional losses when the flow goes across the opening. It is an important parameter for the ventilation rate determination when using the orifice equation method. The objectives of this study were to investigate the relationship between Cd and its potential influencing factors, i.e. opening ratio (r), building length to width ratio (α), wind speed (U), and wind direction (θ), and to evaluate the sensitivity of each factor on the Cd for naturally ventilated dairy buildings (NVDBs). The investigations were performed using the response surface methodology together with Box-Behnken Design and Computational Fluid Dynamics simulation methods. A second-order polynomial response surface model for the estimation of the discharge coefficient was developed and verified. The results showed that the Cd was significantly affected by the individual factors of r and θ, with more sensitivity to r when r > 42.5%. By contrast, the effects from the individual factors of α and U on Cd were insignificant. In addition, the interaction effects between r and θ, and between α and θ on Cd were also identified. It was concluded that the effects from both individual factors and interactions are required to be considered in the estimation of the opening discharge coefficient for NVDBs.

It is important to estimate the ventilation rate of animal houses accurately for the purpose of reducing emissions and providing comfortable indoor climate. The orifice equation is widely applied for estimating the airflow rate of naturally ventilated dairy barns. However, to regard openings’ discharge coefficient (Cd) as a constant maybe not true for large openings. To gain basic knowledge of the opening size effects on Cd values, experiments with a scaled model were carried out in a large boundary layer wind tunnel. Seven cases with different sidewall opening ratios, varying from 18.6% to 81.4%, were investigated under 8 m/s wind speed conditions. The airflow rates, Cd values of windward openings and airflow patterns were analysed. It was found that the Cd value was considerably dependent on the opening size and varied from 0.67 to 0.94 when the opening ratio increased from 18.6% to 62.7%. This is due to the assumptions of pure pressure-driven flow and uniform velocity distributions in the opening were not satisfied for large opening cases. Our study suggests that Cd should be regarded as a variable under different opening size conditions when using the orifice equation to determine the airflow rate of large openings.

[EN] In the last decades, a global warming trend was observed. Along with the temperature increase, modifications in the humidity and wind regime amplify the regional and local impacts on livestock husbandry. Direct impacts include the occurrence of climatic stress conditions. In Europe, cows are economically highly relevant and are mainly kept in naturally ventilated buildings that are most susceptible to climate change. The high-yielding cows are particularly vulnerable to heat stress. Modifications in housing management are the main measures taken to improve the ability of livestock to cope with these conditions. Measures are typically taken in direct reaction to uncomfortable conditions instead of in anticipation of a long-term risk for climatic stress. Measures that balance welfare, environmental and economic issues are barely investigated in the context of climate change and are thus almost not available for commercial farms. Quantitative analysis of the climate change impacts on animal welfare and linked economic and environmental factors is rare. Therefore, we used a numerical modeling approach to estimate the future heat stress risk in such dairy cattle husbandry systems. The indoor climate was monitored inside three reference barns in central Europe and the Mediterranean regions. An artificial neuronal network (ANN) was trained to relate the outdoor weather conditions provided by official meteorological weather stations to the measured indoor microclimate. Subsequently, this ANN model was driven by an ensemble of regional climate model projections with three different greenhouse gas concentration scenarios. For the evaluation of the heat stress risk, we considered the number and duration of heat stress events. Based on the changes in the heat stress events, various economic and environmental impacts were estimated. The impacts of the projected increase in heat stress risk varied among the barns due to different locations and designs as well as the anticipated climate change (considering different climate models and future greenhouse gas concentrations). There was an overall increasing trend in number and duration of heat stress events. At the end of the century, the number of annual stress events can be expected to increase by up to 2000, while the average duration of the events increases by up to 22 h compared to the end of the last century. This implies strong impacts on economics, environment and animal welfare and an urgent need for mid-term adaptation strategies. We anticipated that up to one-tenth of all hours of a year, correspondingly one-third of all days, will be classified as critical heat stress conditions. Due to heat stress, milk yield may decrease by about 2.8 % relative to the present European milk yield, and farmers may expect financial losses in the summer season of about 5.4 % of their monthly income. In addition, an increasing demand for emission reduction measures must be expected, as an emission increase of about 16 Gg of ammonia and 0.1 Gg of methane per year can be expected under the anticipated heat stress conditions. The cattle respiration rate increases by up to 60 %, and the standing time may be prolonged by 1 h. This causes health issues and increases the probability of medical treatments. The various impacts imply feedback loops in the climate system which are presently underexplored. Hence, future in-depth studies on the different impacts and adaptation options at different stress levels are highly recommended., This research has been supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) (grant nos. 2814ERA02C and 2814ERA03C), the Instituto Nacional de Investigacion Tecnologia Agraria y Alimentaria (INIA) (grant no. 618105), the Basque Government (grant no. BERC 2018-2021), the Spanish Ministry of Economy, Industry and Competitiveness MINECO (grant nos. MDM-2017-0714, FJCI-2016-30263, and RYC-2017-22143), and the Innovation Foundation Denmark (grant no. 4215-00004B).

Well-understanding indoor air movement with respect to openings in naturally-ventilated buildings is essential to provide healthy and comfortable indoor climate for livestock. This study investigated the effects of the sidewall opening configurations on internal airflow fields and air velocity characteristics within the animal occupied zone (AOZ). Eight cases with varied opening ratios and locations were tested under 8 m•s-1 free wind speed and wind direction perpendicular to the sidewalls. The ‘up-jet’ airflow pattern was observed when the opening ratio was no greater than 62.71% and the openings located beneath the eaves. Air went across the AOZ without circulating with the surrounding air when no sidewalls were installed below the AOZ height. Both air speed and turbulent kinetic energy increased when the opening was bigger with Pearson's correlation coefficients of 0.8 and 0.9 respectively, but the relationship between the opening size and the turbulence intensity was more complex. Uniform air speed distributions were observed in the AOZ with high sidewalls on bottom. By contrast, the air speed heterogeneity in the AOZ was found when sidewall heights were below the AOZ height. We conclude that care should be taken when using these kinds of opening configurations during extreme cold windy weather conditions.

[EN] The microclimatic conditions in dairy buildings affect animal welfare and gaseous emissions. Measurements are highly variable due to the inhomogeneous distribution of heat and humidity sources (related to farm management) and the turbulent inflow (associated with meteorologic boundary conditions). The selection of the measurement strategy (number and position of the sensors) and the analysis methodology adds to the uncertainty of the applied measurement technique. To assess the suitability of different sensor positions, in situations where monitoring in the direct vicinity of the animals is not possible, we collected long-term data in two naturally ventilated dairy barns in Germany between March 2015 and April 2016 (horizontal and vertical profiles with 10 to 5 min temporal resolution). Uncertainties related to the measurement setup were assessed by comparing the device outputs under lab conditions after the on-farm experiments. We found out that the uncertainty in measurements of relative humidity is of particular importance when assessing heat stress risk and resulting economic losses in terms of temperature-humidity index. Measurements at a height of approximately 3 m-3.5 m turned out to be a good approximation for the microclimatic conditions in the animal occupied zone (including the air volume close to the emission active zone). However, further investigation along this cross-section is required to reduce uncertainties related to the inhomogeneous distribution of humidity. In addition, a regular sound cleaning (and if possible recalibration after few months) of the measurement devices is crucial to reduce the instrumentation uncertainty in long-term monitoring of relative humidity in dairy barns (C) 2017 The Authors. Published by Elsevier Ltd on behalf of IAgrE., The work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2814ERA02C.

The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.

Discharge coefficient of the opening is one of the most important parameters in accurately estimating ventilation rate of a building when applying orifice equation, which maybe not suitable to be taken as a constant value for wind-driven cross ventilated dairy barns. This study aims to investigate the impact of wall porosity and wind speed on discharge coefficient by means of wind tunnel tests and a 1:40 scaled building model. Air velocity at the windward opening and the pressure drop across it were measured by Laser Doppler Anemometer and differential pressure transducer respectively. The experimental result reveals that larger wall porosity and slower wind speed result in higher discharge coefficient. Its value was between 0.67 and 0.94 for wall porosity ranging from 0.22 to 1.61 under wind speed of 8 m/s, and decreased slightly from 0.75 to 0.6 for wall porosity of 0.33 when the wind speed increased from 6 m/s to 12 m/s. These findings indicate that the discharge coefficient of an opening cannot be generalized by a fixed value when predicting ventilation rate of a wind-driven cross ventilated building equipped with large openings.

The increasing share of decentralized renewable power generation represents a challenge to the current and future energy system. Providing a geographical smoothing effect, long-range power transmission plays a key role for the system integration of these fluctuating resources. However, the build-up and operation of the necessary network infrastructure incur costs which have to be allocated to the users of the system. Flow tracing techniques, which attribute the power flow on a transmission line to the geographical location of its generation and consumption, represent a valuable tool set to design fair usage and thus cost allocation schemes for transmission investments. In this article, we introduce a general formulation of the flow tracing method and apply it to a simplified model of a highly renewable European electricity system. We review a statistical usage measure which allows to integrate network usage information for longer time series, and illustrate this measure using an analytical test case.

We investigate the properties of a recently introduced asymmetric association measure, called inner composition alignment (IOTA), aimed at inferring regulatory links (couplings). We show that the measure can be used to determine the direction of coupling, detect superfluous links, and to account for autoregulation. In addition, the measure can be extended to infer the type of regulation (positive or negative). The capabilities of IOTA to correctly infer couplings together with their directionality are compared against Kendall’s rank correlation for time series of different lengths, particularly focussing on biological examples. We demonstrate that an extended version of the measure, bidirectional inner composition alignment (biIOTA), increases the accuracy of the network reconstruction for short time series. Finally, we discuss the applicability of the measure to infer couplings in chaotic systems.

Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean. Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e.g. small scale features or extremes.

The unicellular green alga Chlamydomonas reinhardtii is a long-established model organism for studies on photosynthesis and carbon metabolism-related physiology. Under conditions of air-level carbon dioxide concentration [CO2], a carbon concentrating mechanism (CCM) is induced to facilitate cellular carbon uptake. CCM increases the availability of carbon dioxide at the site of cellular carbon fixation. To improve our understanding of the transcriptional control of the CCM, we employed FAIRE-seq (formaldehyde-assisted Isolation of Regulatory Elements, followed by deep sequencing) to determine nucleosome-depleted chromatin regions of algal cells subjected to carbon deprivation. Our FAIRE data recapitulated the positions of known regulatory elements in the promoter of the periplasmic carbonic anhydrase (Cah1) gene, which is upregulated during CCM induction, and revealed new candidate regulatory elements at a genome-wide scale. In addition, time series expression patterns of 130 transcription factor (TF) and transcription regulator (TR) genes were obtained for cells cultured under photoautotrophic condition and subjected to a shift from high to low [CO2]. Groups of co-expressed genes were identified and a putative directed gene-regulatory network underlying the CCM was reconstructed from the gene expression data using the recently developed IOTA (inner composition alignment) method. Among the candidate regulatory genes, two members of the MYB-related TF family, Lcr1 (Low-CO 2 response regulator 1) and Lcr2 (Low-CO 2 response regulator 2), may play an important role in down-regulating the expression of a particular set of TF and TR genes in response to low [CO2]. The results obtained provide new insights into the transcriptional control of the CCM and revealed more than 60 new candidate regulatory genes. Deep sequencing of nucleosome-depleted genomic regions indicated the presence of new, previously unknown regulatory elements in the C. reinhardtii genome. Our work can serve as a basis for future functional studies of transcriptional regulator genes and genomic regulatory elements in Chlamydomonas.

Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

Identifying causal links (couplings) is a fundamental problem that facilitates the understanding of emerging structures in complex networks. We propose and analyze inner composition alignment-a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series, currently applied to gene expression. The measure can be used to infer the direction of couplings, detect indirect (superfluous) links, and account for autoregulation. Applications to the gene regulatory network of E. coli are presented.