Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

Modelling fungal colonies and communities:challenges and opportunities

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning

Biomass increment and carbon sequestration in hedgerow-grown trees

The global role of tree-based climate change mitigation is widely recognized; trees sequester large amounts of atmospheric carbon, and woody biomass has an important role in the future biobased economy. In national carbon and biomass budgets, trees growing in hedgerows and tree rows are often allocated the same biomass increment data as forest-grown trees. However, the growing conditions in these linear habitats are different from forests given that the trees receive more solar radiation, potentially benefit from fertilization residuals from adjacent fields and have more physical growing space. Tree biomass increment and carbon storage in linear woody elements should therefore be quantified and correctly accounted for. We examined four different hedgerow systems with combinations of pedunculate oak, black alder and silver birch in northern Belgium. We used X-ray CT scans of pith-to-bark cores of 73 trees to model long-term (tree life span) and short-term (last five years) trends in basal area increment and increment in aboveground stem biomass. The studied hedgerows and tree rows showed high densities (168–985 trees km-1) and basal areas (22.1–44.9 m2 km-1). In all four hedgerow systems, we found a strong and persistent increase in stem biomass and thus carbon accumulation with diameter (long-term trend). The current growth performance (short-term trend) also increased with tree diameter and was not related to hedgerow tree density or basal area, which indicates that competition for light does not (yet) limit tree growth in these ecosystems. The total stem volume was 82.0–339.7 m3 km-1 (corresponding to 18.8–100.7 Mg aboveground carbon km-1) and the stem volume increment was 3.1–14.5 m3 km-1 year-1 (aboveground carbon sequestration 0.7–4.3 Mg km-1 year-1). The high tree densities and the persistent increase in growth of trees growing in hedgerow systems resulted in substantial wood production and carbon sequestration rates at the landscape scale. Our findings show that trees growing in hedgerow systems should be included when biomass and carbon budgets are drafted. The biomass production rates of hedgerow trees we provide can help refine the IPCC Guidelines for National Greenhouse Gas Inventories

Use of pJANUS™-02-001 as a calibrator plasmid for Roundup Ready soybean event GTS-40-3-2 detection: an interlaboratory trial assessment

Owing to the labelling requirements of food and feed products containing materials derived from genetically modified organisms, quantitative detection methods have to be developed for this purpose, including the necessary certified reference materials and calibrator standards. To date, for most genetically modified organisms authorized in the European Union, certified reference materials derived from seed powders are being developed. Here, an assessment has been made on the feasibility of using plasmid DNA as an alternative calibrator for the quantitative detection of genetically modified organisms. For this, a dual-target plasmid, designated as pJANUS™-02-001, comprising part of a junction region of genetically modified soybean event GTS-40-3-2 and the endogenous soybean-specific lectin gene was constructed. The dynamic range, efficiency and limit of detection for the soybean event GTS-40-3-2 real-time quantitative polymerase chain reaction (Q-PCR) system described by Terry et al. (J AOAC Int 85(4):938–944, 2002) were shown to be similar for in house produced homozygous genomic DNA from leaf tissue of soybean event GTS-40-3-2 and for plasmid pJANUS™-02-001 DNA backgrounds. The performance of this real-time Q-PCR system using both types of DNA templates as calibrator standards in quantitative DNA analysis was further assessed in an interlaboratory trial. Statistical analysis and fuzzy-logic-based interpretation were performed on critical method parameters (as defined by the European Network of GMO Laboratories and the Community Reference Laboratory for GM Food and Feed guidelines) and demonstrated that the plasmid pJANUS™-02-001 DNA represents a valuable alternative to genomic DNA as a calibrator for the quantification of soybean event GTS-40-3-2 in food and feed products

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

Abstract. We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated. Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.

Modified Gellan Gum hydrogels with tunable physical and mechanical properties

Gellan Gum (GG) has been recently proposed for tissue engineering applications. GG hydrogels are produced by physical crosslinking methods induced by temperature variation or by the presence of divalent cations. However, physical crosslinking methods may yield hydrogels that become weaker in physiological conditions due to the exchange of divalent cations by monovalent ones. Hence, this work presents a new class of GG hydrogels crosslinkable by both physical and chemical mechanisms. Methacrylate groups were incorporated in the GG chain, leading to the production of a methacrylated Gellan Gum (MeGG) hydrogel with highly tunable physical and mechanical properties. The chemical modification was confirmed by proton nuclear magnetic resonance (1H NMR) and Fourier transform infrared spectroscopy (FTIR-ATR). The mechanical properties of the developed hydrogel networks, with Young's modulus values between 0.15 and 148 kPa, showed to be tuned by the different crosslinking mechanisms used. The in vitro swelling kinetics and hydrolytic degradation rate were dependent on the crosslinking mechanisms used to form the hydrogels. Three-dimensional (3D) encapsulation of NIH-3T3 fibroblast cells in MeGG networks demonstrated in vitro biocompatibility confirmed by high cell survival. Given the highly tunable mechanical and degradation properties of MeGG, it may be applicable for a wide range of tissue engineering approaches.This research was funded by the US Army Engineer Research and Development Center, the Institute for Soldier Nanotechnology, the NIH (HL092836, DE019024, EB007249), and the National Science Foundation CAREER award (AK). This work was partially supported by FCT, through funds from the POCTI and/or FEDER programs and from the European Union under the project NoE EXPERTISSUES (NMP3-CT-2004-500283). DFC acknowledges the Foundation for Science and Technology (FCT), Portugal and the MIT-Portugal Program for personal grant SFRH/BD/37156/2007. HS was supported by a Samsung Scholarship. SS acknowledges the postdoctoral fellowship awarded by Fonds de Recherche sur la Nature et les Technologies (FQRNT), Quebec, Canada. We would like to thank Dr. Che Hutson for scientific discussions

Scientific merits and analytical challenges of tree-ring densitometry

R.W. was supported by NERC grant NE/K003097/1.X-ray microdensitometry on annually-resolved tree-ring samples has gained an exceptional position in last-millennium paleoclimatology through the maximum latewood density parameter (MXD), but also increasingly through other density parameters. For fifty years, X-ray based measurement techniques have been the de facto standard. However, studies report offsets in the mean levels for MXD measurements derived from different laboratories, indicating challenges of accuracy and precision. Moreover, reflected visible light-based techniques are becoming increasingly popular and wood anatomical techniques are emerging as a potentially powerful pathway to extract density information at the highest resolution. Here we review the current understanding and merits of wood density for tree-ring research, associated microdensitometric techniques, and analytical measurement challenges. The review is further complemented with a careful comparison of new measurements derived at 17 laboratories, using several different techniques. The new experiment allowed us to corroborate and refresh ?long-standing wisdom?, but also provide new insights. Key outcomes include; i) a demonstration of the need for mass/volume based re-calibration to accurately estimate average ring density; ii) a substantiation of systematic differences in MXD measurements that cautions for great care when combining density datasets for climate reconstructions; and iii) insights into the relevance of analytical measurement resolution in signals derived from tree-ring density data. Finally, we provide recommendations expected to facilitate future inter-comparability and interpretations for global change research.Publisher PDFPeer reviewe