Marcus Krantz

A framework for mapping, visualisation and automatic model creation of signal-transduction networks

Intracellular signalling systems are highly complex. This complexity makes handling, analysis and visualisation of available knowledge a major challenge in current signalling research. Here, we present a novel framework for mapping signal‐transduction networks that avoids the combinatorial explosion by breaking down the network in reaction and contingency information. It provides two new visualisation methods and automatic export to mathematical models. We use this framework to compile the presently most comprehensive map of the yeast MAP kinase network. Our method improves previous strategies by combining (I) more concise mapping adapted to empirical data, (II) individual referencing for each piece of information, (III) visualisation without simplifications or added uncertainty, (IV) automatic visualisation in multiple formats, (V) automatic export to mathematical models and (VI) compatibility with established formats. The framework is supported by an open source software tool that facilitates integration of the three levels of network analysis: definition, visualisation and mathematical modelling. The framework is species independent and we expect that it will have wider impact in signalling research on any system.

Toward Community Standards and Software for Whole-Cell Modeling

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance: We anticipate that these new standards and software will enable more comprehensive models.

Glucose de-repression by yeast AMP-activated protein kinase SNF1 is controlled via at least two independent steps

The AMP‐activated protein kinase, AMPK, controls energy homeostasis in eukaryotic cells but little is known about the mechanisms governing the dynamics of its activation/deactivation. The yeast AMPK, SNF1, is activated in response to glucose depletion and mediates glucose de‐repression by inactivating the transcriptional repressor Mig1. Here we show that overexpression of the Snf1‐activating kinase Sak1 results, in the presence of glucose, in constitutive Snf1 activation without alleviating glucose repression. Co‐overexpression of the regulatory subunit Reg1 of the Glc‐Reg1 phosphatase complex partly restores glucose regulation of Snf1. We generated a set of 24 kinetic mathematical models based on dynamic data of Snf1 pathway activation and deactivation. The models that reproduced our experimental observations best featured (a) glucose regulation of both Snf1 phosphorylation and dephosphorylation, (b) determination of the Mig1 phosphorylation status in the absence of glucose by Snf1 activity only and (c) a regulatory step directing active Snf1 to Mig1 under glucose limitation. Hence it appears that glucose de‐repression via Snf1‐Mig1 is regulated by glucose via at least two independent steps: the control of activation of the Snf1 kinase and directing active Snf1 to inactivating its target Mig1.

Biographer: web-based editing and rendering of SBGN compliant biochemical networks.

Motivation: The rapid accumulation of knowledge in the field of Systems Biology during the past years requires advanced, but simple-to-use, methods for the visualization of information in a structured and easily comprehensible manner. Results: We have developed biographer, a web-based renderer and editor for reaction networks, which can be integrated as a library into tools dealing with network-related information. Our software enables visualizations based on the emerging standard Systems Biology Graphical Notation. It is able to import networks encoded in various formats such as SBML, SBGN-ML and jSBGN, a custom lightweight exchange format. The core package is implemented in HTML5, CSS and JavaScript and can be used within any kind of web-based project. It features interactive graph-editing tools and automatic graph layout algorithms. In addition, we provide a standalone graph editor and a web server, which contains enhanced features like web services for the import and export of models and visualizations in different formats. Availability: The biographer tool can be used at and downloaded from the web page http://biographer.biologie.hu-berlin.de/. The different software packages, including a server-indepenent version as well as a web server for Windows and Linux based systems, are available at http://code.google.com/p/biographer/ under the open-source license LGPL. Contact: edda.klipp@biologie.hu-berlin.de or handorf@physik.hu-berlin.de

Time course gene expression profiling of yeast spore germination reveals a network of transcription factors orchestrating the global response

BackgroundSpore germination of the yeast Saccharomyces cerevisiae is a multi-step developmental path on which dormant spores re-enter the mitotic cell cycle and resume vegetative growth. Upon addition of a fermentable carbon source and nutrients, the outer layers of the protective spore wall are locally degraded, the tightly packed spore gains volume and an elongated shape, and eventually the germinating spore re-enters the cell cycle. The regulatory pathways driving this process are still largely unknown. Here we characterize the global gene expression profiles of germinating spores and identify potential transcriptional regulators of this process with the aim to increase our understanding of the mechanisms that control the transition from cellular dormancy to proliferation.ResultsEmploying detailed gene expression time course data we have analysed the reprogramming of dormant spores during the transition to proliferation stimulated by a rich growth medium or pure glucose. Exit from dormancy results in rapid and global changes consisting of different sequential gene expression subprograms. The regulated genes reflect the transition towards glucose metabolism, the resumption of growth and the release of stress, similar to cells exiting a stationary growth phase. High resolution time course analysis during the onset of germination allowed us to identify a transient up-regulation of genes involved in protein folding and transport. We also identified a network of transcription factors that may be regulating the global response. While the expression outputs following stimulation by rich glucose medium or by glucose alone are qualitatively similar, the response to rich medium is stronger. Moreover, spores sense and react to amino acid starvation within the first 30 min after germination initiation, and this response can be linked to specific transcription factors.ConclusionsResumption of growth in germinating spores is characterized by a highly synchronized temporal organisation of up- and down-regulated genes which reflects the metabolic reshaping of the quickening spores.

Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks

BackgroundCellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner.ResultsHere, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes.ConclusionThe probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.

Information content and scalability in signal transduction network reconstruction formats

One of the first steps towards holistic understanding of cellular networks is the integration of the available information in a human and machine readable format. This network reconstruction process is well established for metabolic networks, and numerous genome wide metabolic reconstructions are already available. Extending these strategies to signalling networks has proven difficult, primarily due to the combinatorial nature of regulatory modifications. The combinatorial nature of possible protein-protein interactions and post translational modifications affects both network size and the correspondence between the reconstructed network and the underlying empirical data. Here, we discuss different approaches to reconstruction of signal transduction networks. We divide the current approaches into topological, specific state based and reaction-contingency based, and discuss their different information content and scalability. The discussion focusses on graphical formats but the points are in general applicable also to mathematical models and databases. While the formats have complementary strengths especially for small networks, reaction-contingency based formats have a number of advantages in the light of global network reconstruction. In particular, they minimise the need for assumptions, maximise the congruence with empirical data, and scale efficiently with network size.

Size homeostasis can be intrinsic to growing cell populations and explained without size sensing or signalling

The cell division cycle orchestrates cellular growth and division. The machinery underpinning the cell division cycle is well characterized, but the actual cue(s) driving the cell division cycle remains unknown. In rapidly growing and dividing yeast cells, this cue has been proposed to be cell size. Presumably, a mechanism communicating cell size acts as gatekeeper for the cell division cycle via the G1 network, which triggers G1 exit only when a critical size has been reached. Here, we evaluate this hypothesis with a minimal core model linking metabolism, growth and the cell division cycle. Using this model, we (a) present support for coordinated regulation of G1/S and G2/M transition in Saccharomyces cerevisiae in response to altered growth conditions, (b) illustrate the intrinsic antagonism between G1 progression and cell size and (c) provide evidence that the coupling of growth and division is sufficient to allow for size homeostasis without directly communicating or measuring cell size. We show that even with a rudimentary version of the G1 network consisting of a single unregulated cyclin, size homeostasis is maintained in populations during autocatalytic growth when the geometric constraint on nutrient supply is considered. Taken together, our results support the notion that cell size is a consequence rather than a regulator of growth and division.

Initiation of the transcriptional response to hyperosmotic shock correlates with the potential for volume recovery

The control of activity and localization of transcription factors is critical for appropriate transcriptional responses. In eukaryotes, signal transduction components such as mitogen‐activated protein kinase (MAPK) shuttle into the nucleus to activate transcription. It is not known in detail how different amounts of nuclear MAPK over time affect the transcriptional response. In the present study, we aimed to address this issue by studying the high osmolarity glycerol (HOG) system in Saccharomyces cerevisiae. We employed a conditional osmotic system, which changes the period of the MAPK Hog1 signal independent of the initial stress level. We determined the dynamics of the Hog1 nuclear localization and cell volume by single‐cell analysis in well‐controlled microfluidics systems and compared the responses with the global transcriptional output of cell populations. We discovered that the onset of the initial transcriptional response correlates with the potential of cells for rapid adaptation; cells that are capable of recovering quickly initiate the transcriptional responses immediately, whereas cells that require longer time to adapt also respond later. This is reflected by Hog1 nuclear localization, Hog1 promoter association and the transcriptional response, but not Hog1 phosphorylation, suggesting that a presently uncharacterized rapid adaptive mechanism precedes the Hog1 nuclear response. Furthermore, we found that the period of Hog1 nuclear residence affects the amplitude of the transcriptional response rather than the spectrum of responsive genes.

Reaction-contingency based bipartite Boolean modelling

BackgroundIntracellular signalling systems are highly complex, rendering mathematical modelling of large signalling networks infeasible or impractical. Boolean modelling provides one feasible approach to whole-network modelling, but at the cost of dequantification and decontextualisation of activation. That is, these models cannot distinguish between different downstream roles played by the same component activated in different contexts.ResultsHere, we address this with a bipartite Boolean modelling approach. Briefly, we use a state oriented approach with separate update rules based on reactions and contingencies. This approach retains contextual activation information and distinguishes distinct signals passing through a single component. Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation. We benchmark this method with the previously mapped MAP kinase network in yeast, showing that minor adjustments suffice to produce a functional network description.ConclusionsTaken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.