Jörg Schaber

REGENERATION IN GAP MODELS: PRIORITY ISSUES FOR STUDYING FOREST RESPONSES TO CLIMATE CHANGE

Recruitment algorithms in forest gap models are examined withparticular regard to their suitability for simulating forestecosystem responses to a changing climate. The traditional formulation of recruitment is found limiting in three areas. First, the aggregation of different regeneration stages (seedproduction, dispersal, storage, germination and seedling establishment) is likely to result in less accurate predictionsof responses as compared to treating each stage separately. Second, the related assumptions that seeds of all species are uniformly available and that environmental conditions arehomogeneous, are likely to cause overestimates of future speciesdiversity and forest migration rates. Third, interactions between herbivores (ungulates and insect pests) and forest vegetation are a big unknown with potentially serious impactsin many regions. Possible strategies for developing better gapmodel representations for the climate-sensitive aspects of eachof these key areas are discussed. A working example of a relatively new model that addresses some of these limitations is also presented for each case. We conclude that better modelsof regeneration processes are desirable for predicting effectsof climate change, but that it is presently impossible to determine what improvements can be expected without carrying outrigorous tests for each new formulation.

Systems biology standards—the community speaks

VOLUME 25 NUMBER 4 APRIL 2007 NATURE BIOTECHNOLOGY available filters and dramatically decreases clogging and breakage. This allows undesired components to be easily eliminated during blood diagnosis. Third, microscale electrophoresis using bacterial cellulose in a low-viscosity polymer matrix results in a dramatic improvement in separation and detection of analytes compared with current techniques. And finally, the unique light amplification properties of Nata de Coco6,7 provide a much greater level of detection sensitivity compared with other light amplification systems. All these properties allow optimized detection of genetic variation by virtue of better separation resolution and higher detection intensity of biomolecules7. Our in vitro results using the methylthiazolydiphenyl-tetrazolium bromide (MTT) assay in the human leukemia cell line THP-1 also indicate that bacterial cellulose does not upregulate CD86 or CD54 expression, suggesting it is unlikely to be allergenic (Fig. 1e). Indeed, bacterial cellulose is already under clinical development as a scaffold for tissueengineered products9. Nata de Coco is just one of numerous nanomaterials found in nature that could be applied in innovative nanotechnology products and devices. Cellulose is the most abundant material in nature and, apart from bacteria, it can be derived from sources as diverse as wood, cotton, animals and plant cell walls. Other abundant natural materials that may mined for nanomaterials include silk, collagen, chitin, chitosan and polylactic acid. Recently, much of the emphasis in nanotechnology has been on synthetic materials, such as carbon nanotubes, nanoparticles (colloidal gold, quantum dots, latex and so on) and inorganic nanomaterials (ZnO, TiO2, silica). For these synthetic nanomaterials to be applied as drug carriers, medical treatments, implants, tissue engineering constructs and cosmetics, extensive investigation will be necessary to establish their biocompatibility, safety, immunogenicity and allergenicity. Although advances in these areas are exciting and should continue to be funded and supported, we should not forget that many materials are available directly from the natural world with interesting properties on the nanoscale. These materials have a proven safety record in humans, biodegradable properties that make them environmentally friendly and in certain cases economic advantages over more sophisticated and expensive products and technologies. Bacterial cellulose is just one attractive source of nanomaterials for use in medical and food applications. But the potential for naturally occurring nanomaterials is boundless. As we begin to use naturally occurring nanomaterials like Nata de Coco in applications beyond medical diagnostics, we will see that these natural nanomaterials are not just an alternative; rather, they may well be the preferred alternative.

Modelling reveals novel roles of two parallel signalling pathways and homeostatic feedbacks in yeast

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo‐adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo‐adaptation in yeast. The model suggested that (i) the main mechanism for osmo‐adaptation is a fast and transient non‐transcriptional Hog1‐mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady‐state glycerol production with low steady‐state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single‐branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

Quantitative Analysis of Glycerol Accumulation, Glycolysis and Growth under Hyper Osmotic Stress

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.

Time-dependent quantitative multicomponent control of the G1-S network by the stress-activated protein kinase Hog1 upon osmostress

The stress-activated protein kinase Hog1 delays bud morphogenesis and DNA replication through different cyclin proteins. Assigning Roles to the Arrest Team To avoid replicating under suboptimal conditions, cells have elaborate mechanisms to sense and respond to stressful conditions and halt progression through the cell cycle. In budding yeast, the stress-activated protein kinase Hog1 prevents progression through the cell cycle by arresting the cells in the G1 phase when yeast are exposed to hyperosmotic stress. Using a combination of in vivo experiments, modeling, and simulation, Adrover et al. quantitatively investigated the temporal dynamics of this cell cycle arrest. They defined the specific roles of components downstream of Hog1 in preventing the G1-to-S phase transition and budding in response to hyperosmotic stress. Their analyses suggested that Hog1-mediated inhibition of the expression of the gene encoding the S-phase cyclin Clb5 was a key determinant of osmotic stress–induced G1 arrest. Control of cell cycle progression by stress-activated protein kinases (SAPKs) is essential for cell adaptation to extracellular stimuli. Exposure of yeast to hyperosmotic stress activates the SAPK Hog1, which delays cell cycle progression through G1 by direct phosphorylation of the cyclin-dependent kinase (CDK) inhibitor Sic1 and by inhibition of the transcription of the genes encoding the G1 cyclins Cln1 and 2. Additional targets of Hog1 may also play a role in this response. We used mathematical modeling and quantitative in vivo experiments to define the contributions of individual components of the G1-S network downstream of Hog1 to this stress-induced delay in the cell cycle. The length of the arrest depended on the degree of stress and the temporal proximity of the onset of the stress to the commitment to cell division, called “Start.” Hog1-induced inhibition of the transcription of the gene encoding cyclin Clb5, rather than that of the gene encoding Cln2, prevented entry into S phase upon osmostress. By controlling the accumulation of specific cyclins, Hog1 delayed bud morphogenesis (through Clns) and delayed DNA replication (through Clb5). Hog1-mediated phosphorylation and degradation of Sic1 at Start prevented residual activity of the cyclin/CDK complex Clb5/Cdc28 from initiating DNA replication before adaptation to the stress. Thus, our work defines distinct temporal roles for the actions of Hog1 on Sic1 and cyclins in mediating G1 arrest upon hyperosmotic stress.

A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast

Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.

Model-based inference of biochemical parameters and dynamic properties of microbial signal transduction networks.

Because of the inherent uncertainty about quantitative aspects of signalling networks it is of substantial interest to use computational methods that allow inferring non-measurable quantities such as rate constants, from measurable quantities such as changes in protein abundances. We argue that true biochemical parameters like rate constants can generally not be inferred using models due to their non-identifiability. Recent advances, however, facilitate the analysis of parameter identifiability of a given model and automated discrimination of candidate models, both being important techniques to still extract quantitative biological information from experimental data.

Nested uncertainties in biochemical models

Dynamic modelling of biochemical reaction networks has to cope with the inherent uncertainty about biological processes, concerning not only data and parameters but also kinetics and structure. These different types of uncertainty are nested within each other: uncertain network structures contain uncertain reaction kinetics, which in turn are governed by uncertain parameters. Here, the authors review some issues arising from such uncertainties and sketch methods, solutions and future directions to deal with them.

Tempo and mode of early gene loss in endosymbiotic bacteria from insects

BackgroundUnderstanding evolutionary processes that drive genome reduction requires determining the tempo (rate) and the mode (size and types of deletions) of gene losses. In this study, we analysed five endosymbiotic genome sequences of the gamma-proteobacteria (three different Buchnera aphidicola strains, Wigglesworthia glossinidia, Blochmannia floridanus) to test if gene loss could be driven by the selective importance of genes. We used a parsimony method to reconstruct a minimal ancestral genome of insect endosymbionts and quantified gene loss along the branches of the phylogenetic tree. To evaluate the selective or functional importance of genes, we used a parameter that measures the level of adaptive codon bias in E. coli (i.e. codon adaptive index, or CAI), and also estimates of evolutionary rates (Ka) between pairs of orthologs either in free-living bacteria or in pairs of symbionts.ResultsOur results demonstrate that genes lost in the early stages of symbiosis were on average less selectively constrained than genes conserved in any of the extant symbiotic strains studied. These results also extend to more recent events of gene losses (i.e. among Buchnera strains) that still tend to concentrate on genes with low adaptive bias in E. coli and high evolutionary rates both in free-living and in symbiotic lineages. In addition, we analyzed the physical organization of gene losses for early steps of symbiosis acquisition under the hypothesis of a common origin of different symbioses. In contrast with previous findings we show that gene losses mostly occurred through loss of rather small blocks and mostly in syntenic regions between at least one of the symbionts and present-day E. coli.ConclusionAt both ancient and recent stages of symbiosis evolution, gene loss was at least partially influenced by selection, highly conserved genes being retained more readily than lowly conserved genes: although losses might result from drift due to the bottlenecking of endosymbiontic populations, we demonstrated that purifying selection also acted by retaining genes of greater selective importance.

Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway.

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback. modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from http://modelmage.org.