Steinn Gudmundsson

Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms.

Reliability of quantitative EEG features

OBJECTIVE To investigate the reliability of several well-known quantitative EEG (qEEG) features in the elderly in the resting, eyes closed condition and study the effects of epoch length and channel derivations on reliability. METHODS Fifteen healthy adults, over 50 years of age, underwent 10 EEG recordings over a 2-month period. Various qEEG features derived from power spectral, coherence, entropy and complexity analysis of the EEG were computed. Reliability was quantified using an intraclass correlation coefficient. RESULTS The highest reliability was obtained with the average montage, reliability increased with epoch length up to 40s, longer epochs gave only marginal improvement. The reliability of the qEEG features was highest for power spectral parameters, followed by regularity measures based on entropy and complexity, coherence being least reliable. CONCLUSIONS Montage and epoch length had considerable effects on reliability. Several apparently unrelated regularity measures had similar stability. Reliability of coherence measures was strongly dependent on channel location and frequency bands. SIGNIFICANCE The reliability of regularity measures has until now received limited attention. Low reliability of coherence measures in general may limit their usefulness in the clinical setting.

Computationally efficient flux variability analysis

BackgroundFlux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.ResultsWe present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.ConclusionsNetworks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

Support vector machines and dynamic time warping for time series

Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example be represented by its similarity to all the examples in this set and then apply a conventional SVM to this transformed data. Dynamic time warping (DTW) is a well established distance measure for time series but has been of limited use in SVMs since it is not obvious how it can be used to derive a PSD kernel. The feasibility of the similarity based approach for DTW is investigated by applying the method to a large set of time-series classification problems.

Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates

This paper describes attempts at constructing an automatic sleep stage classifier using EEG recordings. Three different feature extraction schemes were compared together with two different pattern classifiers, the recently introduced support vector machine and the well known k-nearest neighbor classifier. Using estimates of posterior probabilities for each of the sleep stages it was possible to devise a simple post-processing rule which leads to improved accuracy. Compared to a human expert the accuracy of the best classifier is 81%

Multidimensional Analytical Approach Based on UHPLC-UV-Ion Mobility-MS for the Screening of Natural Pigments

Here, we propose a novel strategy that combines a typical ultra high performance liquid chromatography (UHPLC), data-independent mass spectrometry (MS(E)) workflow with traveling wave ion mobility (TWIM) and UV detection, to improve the characterization of carotenoids and chlorophylls in complex biological matrices. UV detection selectively highlighted pigments absorbing at specific wavelengths, while TWIM coupled to MS was used to maximize the peak capacity. We applied this approach for the analysis of pigments in different microalgae samples, including Chlorella vulgaris, Dunaliella salina, and Phaeodactylum tricornutum. Using UHPLC-UV-MS(E) information (retention time, absorbance at 450 nm, and accurate masses of precursors and product ions), we tentatively identified 26 different pigments (carotenes, chlorophylls, and xanthophylls). By adding TWIM information (collision cross sections), we further resolved 5 isobaric pigments, not resolved by UHPLC-UV-MS(E) alone. The characterization of the molecular phenotypes allowed us to differentiate the microalgae species. Our results demonstrate that a combination of TWIM and UV detection with traditional analytical approaches increases the selectivity and specificity of analysis, providing a new tool to characterize pigments in biological samples. We anticipate that such an analytical approach will be extended to other lipidomics and metabolomics applications.

Effects of abiotic stressors on lutein production in the green microalga Dunaliella salina

BackgroundRecent years have witnessed a rising trend in exploring microalgae for valuable carotenoid products as the demand for lutein and many other carotenoids in global markets has increased significantly. In green microalgae lutein is a major carotenoid protecting cellular components from damage incurred by reactive oxygen species under stress conditions. In this study, we investigated the effects of abiotic stressors on lutein accumulation in a strain of the marine microalga D. salina which had been selected for growth under stress conditions of combined blue and red lights by adaptive laboratory evolution.ResultsNitrate concentration, salinity and light quality were selected as three representative influencing factors and their impact on lutein production in batch cultures of D. salina was evaluated using response surface analysis. D. salina was found to be more tolerant to hyper-osmotic stress than to hypo-osmotic stress which caused serious cell damage and death in a high proportion of cells while hyper-osmotic stress increased the average cell size of D. salina only slightly. Two models were developed to explain how lutein productivity depends on the stress factors and for predicting the optimal conditions for lutein productivity. Among the three stress variables for lutein production, stronger interactions were found between nitrate concentration and salinity than between light quality and the other two. The predicted optimal conditions for lutein production were close to the original conditions used for adaptive evolution of D. salina. This suggests that the conditions imposed during adaptive evolution may have selected for the growth optima arrived at.ConclusionsThis study shows that systematic evaluation of the relationship between abiotic environmental stresses and lutein biosynthesis can help to decipher the key parameters in obtaining high levels of lutein productivity in D. salina. This study may benefit future stress-driven adaptive laboratory evolution experiments and a strategy of applying stress in a step-wise manner can be suggested for a rational design of experiments.

Toward systems metabolic engineering in cyanobacteria: Opportunities and bottlenecks

We recently assessed the metabolism of Synechocystis sp PCC6803 through a constraints-based reconstruction and analysis approach and identified its main metabolic properties. These include reduced metabolic robustness, in contrast to a high photosynthetic robustness driving the optimal autotrophic metabolism. Here, we address how these metabolic features affect biotechnological capabilities of this bacterium. The search for growth-coupled overproducer strains revealed that the carbon flux re-routing, but not the electron flux, is significantly more challenging under autotrophic conditions than under mixo- or heterotrophic conditions. We also found that the blocking of the light-driven metabolism was required for carbon flux re-routing under mixotrophic conditions. Overall, our analysis, which represents the first systematic evaluation of the biotechnological capabilities of a photosynthetic organism, paradoxically suggests that the light-driven metabolism itself and its unique metabolic features are the main bottlenecks in harnessing the biotechnological potential of Synechocystis.

The use of EEG in Alzheimer's disease, with and without scopolamine - a pilot study.

OBJECTIVE To use multivariate statistical analysis of EEG data in order to separate EEGs of patients with Alzheimers disease (AD) from controls. A group of individuals with mild cognitive impairment (MCI) was evaluated using the same methodology. Additionally, the effects of scopolamine on this separation are studied. METHODS Statistical pattern recognition (SPR) is used in conjunction with information extracted from EEGs before and after administration of scopolamine. RESULTS There was complete separation of the AD group and controls before administration of scopolamine. The separation increased after scopolamine had been given. Of the 10 MCI individuals, five seemed to belong to the AD group. Three of those progressed to AD within 1 year and one after 3 years. CONCLUSIONS Using SPR on EEG recordings it is possible to separate AD from controls. This separation can be increased by the use of scopolamine but the medication is not a prerequisite for classification. The results indicate that SPR is useful for predicting progress of MCI to AD. SIGNIFICANCE EEG registration is a simple and noninvasive method. If these results are confirmed in other studies, this method could be more widely applied than the highly specialized methods used today in detection of early AD.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.