Joerg Stelling

A synthetic low-frequency mammalian oscillator

Circadian clocks have long been known to be essential for the maintenance of physiological and behavioral processes in a variety of organisms ranging from plants to humans. Dysfunctions that subvert gene expression of oscillatory circadian-clock components may result in severe pathologies, including tumors and metabolic disorders. While the underlying molecular mechanisms and dynamics of complex gene behavior are not fully understood, synthetic approaches have provided substantial insight into the operation of complex control circuits, including that of oscillatory networks. Using iterative cycles of mathematical model-guided design and experimental analyses, we have developed a novel low-frequency mammalian oscillator. It incorporates intronically encoded siRNA-based silencing of the tetracycline-dependent transactivator to enable the autonomous and robust expression of a fluorescent transgene with periods of 26 h, a circadian clock-like oscillatory behavior. Using fluorescence-based time-lapse microscopy of engineered CHO-K1 cells, we profiled expression dynamics of a destabilized yellow fluorescent protein variant in single cells and real time. The novel oscillator design may enable further insights into the system dynamics of natural periodic processes as well as into siRNA-mediated transcription silencing. It may foster advances in design, analysis and application of complex synthetic systems in future gene therapy initiatives.

MetaNetX.org

Summary: MetaNetX.org is a website for accessing, analysing and manipulating genome-scale metabolic networks (GSMs) as well as biochemical pathways. It consistently integrates data from various public resources and makes the data accessible in a standardized format using a common namespace. Currently, it provides access to hundreds of GSMs and pathways that can be interactively compared (two or more), analysed (e.g. detection of dead-end metabolites and reactions, flux balance analysis or simulation of reaction and gene knockouts), manipulated and exported. Users can also upload their own metabolic models, choose to automatically map them into the common namespace and subsequently make use of the website’s functionality. Availability and implementation: MetaNetX.org is available at http://metanetx.org. Contact: help@metanetx.org

Accurate cell segmentation in microscopy images using membrane patterns

MOTIVATION Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods. RESULTS We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells. AVAILABILITY AND IMPLEMENTATION Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip.

Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism.

Topological filtering identifies biological networks compatible with known data and enables quantitative analysis of regulatory mechanisms. Reducing the Options Quantitative analysis of signaling systems is challenging because limited quantitative data are available and the data can be represented by many network models. Sunnåker et al. developed a computational approach called topological filtering to systematically and automatically integrate modeling and data acquisition to infer the set of mechanistically plausible models, thus vastly reducing the number of potential models. The approach iteratively eliminates reactions from the model to identify only those topological networks that fit the data. Application of their method to an extracellular signal–regulated kinase (ERK) pathway that could be represented by 512 possible network topologies reduced the possibilities to 16 and showed that a set of feedback reactions were necessary to quantitatively represent the results. Topological filtering applied to the regulation of the localization of Msn2, a yeast transcription factor controlled by phosphorylation by PKA (protein kinase A) in response to changes in glucose abundance, identified a single model that fit the data. Comparison of model predictions with experimental data showed that the nuclear phosphorylation rate was key to controlling Msn2 nuclear abundance in response to cAMP (cyclic adenosine monophosphate), a signal produced as cells recover from glucose starvation. Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae, specifically the short-term mechanisms mediating the cells’ recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.

Inducible, tightly regulated and growth condition-independent transcription factor in Saccharomyces cerevisiae

The precise control of gene expression is essential in basic biological research as well as in biotechnological applications. Most regulated systems available in yeast enable only the overexpression of the target gene, excluding the possibility of intermediate or weak expression. Moreover, these systems are frequently toxic or depend on growth conditions. We constructed a heterologous transcription factor that overcomes these limitations. Our system is a fusion of the bacterial LexA DNA-binding protein, the human estrogen receptor (ER) and an activation domain (AD). The activity of this chimera, called LexA-ER-AD, is tightly regulated by the hormone β-estradiol. The selection of the AD proved to be crucial to avoid toxic effects and to define the range of activity that can be precisely tuned with β-estradiol. As our system is based on a heterologous DNA-binding domain, induction in different metabolic contexts is possible. Additionally, by controlling the number of LexA-binding sites in the target promoter, one can scale the expression levels up or down. Overall, our LexA-ER-AD system is a valuable tool to precisely control gene expression in different experimental contexts without toxic side effects.

Bridging the gaps in systems biology

Abstract Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Topological augmentation to infer hidden processes in biological systems

Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Model Extension and Model Selection

In this chapter we are concerned with the topic of construction , assessment, and selection of models in general, and of biochemical models in particular. Standard approaches to model construction and (automated) generation of candidate models are first discussed. We then present the most commonly used methods for model assessment, as well as the underlying concepts and ideas. In particular we focus on the information theoretic and Bayesian approaches to model selection. Information theoretic methods for model selection include the Akaike information criterion and the more recent deviance information criterion. Bayesian approaches include the computation of posterior ratios for relative model probabilities from Bayes factors as well as the approximate Bayesian information criterion. We also briefly discuss other methods such as cross-validation and bootstrapping techniques, and the theoretically appealing approach of minimum description length. We sketch how the most important results can be derived, emphasize distinctions between the methods, and discuss how model inference methods are employed in practice. We conclude that there is no generally applicable method for model assessment: a suitable choice depends on the specific inference problem, and to some extent also on the subjective preferences of the modeler.

Effect of Immunosuppression on T-Helper 2 and B-Cell Responses to Influenza Vaccination.

BACKGROUND Influenza vaccine immunogenicity is suboptimal in immunocompromised patients. However, there are limited data on the interplay of T- and B- cell responses to vaccination with simultaneous immunosuppression. METHODS We collected peripheral blood mononuclear cells from transplant recipients before and 1 month after seasonal influenza vaccination. Before and after vaccination, H1N1-specific T- and B-cell activation were quantified with flow cytometry. We also developed a mathematical model using T- and B-cell markers and mycophenolate mofetil (MMF) dosage. RESULTS In the 47 patients analyzed, seroconversion to H1N1 antigen was demonstrated in 34%. H1N1-specific interleukin 4 (IL-4)-producing CD4(+) T-cell frequencies increased significantly after vaccination in 53% of patients. Prevaccine expression of H1N1-induced HLA-DR and CD86 on B cells was high in patients who seroconverted. Seroconversion against H1N1 was strongly associated with HLA-DR expression on B cells, which was dependent on the increase between prevaccine and postvaccine H1N1-specific IL-4(+)CD4(+) T cells (R(2) = 0.35). High doses of MMF (≥ 2 g/d) led to lower seroconversion rates, smaller increase in H1N1-specific IL-4(+)CD4(+) T cells, and reduced HLA-DR expression on B cells. The mathematical model incorporating a MMF-inhibited positive feedback loop between H1N1-specific IL-4(+)CD4(+) T cells and HLA-DR expression on B cells captured seroconversion with high specificity. CONCLUSIONS Seroconversion is associated with influenza-specific T-helper 2 and B-cell activation and seems to be modulated by MMF.

Efficient Reconstruction of Predictive Consensus Metabolic Network Models.

Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.