Biomedical subjects

Jörg Stelling

Publications and source records attributed to Jörg Stelling.

11 recordsLinked to original sources

PubMed2006

Quantitative performance metrics for robustness in circadian rhythms.

MOTIVATION: Sensitivity analysis provides key measures that aid in unraveling the design principles responsible for the robust performance of biological networks. Such metrics allow researchers to investigate comprehensively model performance, to develop more realistic models, and to design informative experiments. However, sensitivity analysis of oscillatory systems focuses on period and amplitude characteristics, while biologically relevant effects on phase are neglected. RESULTS: Here, we introduce a novel set of phase-based sensitivity metrics for performance: period, phase, corrected phase and relative phase. Both state- and phase-based tools are applied to free-running Drosophila melanogaster and Mus musculus circadian models. Each metric produces unique sensitivity values used to rank parameters from least to most sensitive. Similarities among the resulting rank distributions strongly suggest a conservation of sensitivity with respect to parameter function and type. A consistent result, for instance, is that model performance of biological oscillators is more sensitive to global parameters than local (i.e. circadian specific) parameters. Discrepancies among these distributions highlight the individual metrics' definition of performance as specific parametric sensitivity values depend on the defined metric, or output. AVAILABILITY: An implementation of the algorithm in MATLAB (Mathworks, Inc.) is available from the authors. SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

Animals↗

PubMed2006

Systems interface biology.

The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines.

Gene Expression Regulation↗

PubMed2006

The galactose switch in Kluyveromyces lactis depends on nuclear competition between Gal4 and Gal1 for Gal80 binding.

The Gal4 protein represents a universally functional transcription activator, which in yeast is regulated by protein-protein interaction of its transcription activation domain with the inhibitor Gal80. Gal80 inhibition is relieved via galactose-mediated Gal80-Gal1-Gal3 interaction. The Gal4-Gal80-Gal1/3 regulatory module is conserved between Saccharomyces cerevisiae and Kluyveromyces lactis. Here we demonstrate that K. lactis Gal80 (KlGal80) is a nuclear protein independent of the Gal4 activity status, whereas KlGal1 is detected throughout the entire cell, which implies that KlGal80 and KlGal1 interact in the nucleus. Consistently KlGal1 accumulates in the nucleus upon KlGAL80 overexpression. Furthermore, we show that the KlGal80-KlGal1 interaction blocks the galactokinase activity of KlGal1 and is incompatible with KlGal80-KlGal4-AD interaction. Thus, we propose that dissociation of KlGal80 from the AD forms the basis of KlGal4 activation in K. lactis. Quantitation of the dissociation constants for the KlGal80 complexes gives a much lower affinity for KlGal1 as compared with Gal4. Mathematical modeling shows that with these affinities a switch based on competition between Gal1 and Gal4 for Gal80 binding is nevertheless efficient provided two monomeric Gal1 molecules interact with dimeric Gal80. Consistent with such a mechanism, analysis of the sedimentation behavior by analytical ultracentrifugation demonstrates the formation of a heterotetrameric KlGal80-KlGal1 complex of 2:2 stoichiometry.

Binding, Competitive↗

PubMed2004

Robustness of cellular functions.

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is a long-recognized key property of living systems. Owing to intimate links to cellular complexity, however, its molecular and cellular basis has only recently begun to be understood. Theoretical approaches to complex engineered systems can provide guidelines for investigating cellular robustness because biology and engineering employ a common set of basic mechanisms in different combinations. Robustness may be a key to understanding cellular complexity, elucidating design principles, and fostering closer interactions between experimentation and theory.

Animals↗

PubMed2004

Robustness properties of circadian clock architectures.

Robustness, a relative insensitivity to perturbations, is a key characteristic of living cells. However, the specific structural characteristics that are responsible for robust performance are not clear, even in genetic circuits of moderate complexity. Formal sensitivity analysis allows the investigation of robustness and fragility properties of mathematical models representing regulatory networks, but it yields only local properties with respect to a particular choice of parameter values. Here, we show that by systematically investigating the parameter space, more global properties linked to network structure can be derived. Our analysis focuses on the genetic oscillator responsible for generating circadian rhythms in Drosophila as a prototypic dynamical cellular system. Analysis of two mathematical models of moderate complexity shows that the tradeoff between robustness and fragility is largely determined by the regulatory structure. Rank-ordered sensitivities, for instance, allow the correct identification of protein phosphorylation as an influential process determining the oscillator's period. Furthermore, sensitivity analysis confirms the theoretical insight that hierarchical control might be important for achieving robustness. The complex feedback structures encountered in vivo, however, do not seem to enhance robustness per se but confer robust precision and adjustability of the clock while avoiding catastrophic failure.

Animals↗

PubMed2004

Mathematical models in microbial systems biology.

Systems biology aims at an understanding of the genotype-phenotype relations brought about by cellular networks. Mathematical models as formal representations are central for handling the associated complexity. Recently, model-based analysis of microorganisms has begun, for instance, to reveal functional modules in metabolic and transcriptional networks, to predict cellular behavior from genome-scale physicochemical constraints, and to suggest novel design principles for well-studied bacterial subsystems such as chemotaxis. Guided by common themes such as modularity, optimality and robustness, iterative model development promises further progress towards a system-level understanding.

Adaptation, Physiological↗

PubMed2004

Mathematical modeling of complex regulatory networks.

Cellular regulation comprises overwhelmingly complex interactions between genes and proteins that ultimately will only be rendered understandable by employing formal approaches. Developing large-scale mathematical models of such systems in an efficient and reliable way, however, requires careful evaluation of structuring principles for the models, of the description of the system dynamics, and of the experimental data basis for adjusting the models to reality. We discuss these three aspects of model development using the example of cell cycle regulation in yeast and suggest that capturing complex dynamic networks is feasible despite incomplete (quantitative) biological knowledge.

Cell Cycle↗

PubMed2003

FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps.

MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.

Combinatorial Chemistry Techniques↗

PubMed2003

Two approaches for metabolic pathway analysis?

Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in (reconstructed) biochemical reaction networks. Of the two most promising concepts for pathway analysis, one relies on elementary flux modes and the other on extreme pathways. These concepts are closely related because extreme pathways are a subset of elementary modes. Here, the common features, differences and applicability of these concepts are discussed. Assessing metabolic systems by the set of extreme pathways can, in general, give misleading results owing to the exclusion of possibly important routes. However, in certain network topologies, the sets of elementary modes and extreme pathways coincide. This is quite often the case in realistic applications. In our opinion, the unification of both approaches into one common framework for metabolic pathway analysis is necessary and achievable.

Cell Physiological Phenomena↗

PubMed2002

Metabolic network structure determines key aspects of functionality and regulation.

The relationship between structure, function and regulation in complex cellular networks is a still largely open question. Systems biology aims to explain this relationship by combining experimental and theoretical approaches. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness or metabolic phenotype, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.

Biomass↗

PubMed2002

Combinatorial complexity of pathway analysis in metabolic networks.

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.

Computer Simulation↗