A detailed molecular network map and model of the NLRP3 inflammasome.

The NLRP3 inflammasome is a key regulator of inflammation that responds to a broad range of stimuli. The exact mechanism of activation has not been determined, but there is a consensus on cellular potassium efflux as a major common denominator. Once NLRP3 is activated, it forms high-order complexes together with NEK7 that trigger aggregation of ASC into specks.

kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.

Background: Computational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon ("reaction-contingency") formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called "combinatorial explosion" of system size.

A yeast cell cycle model integrating stress, signaling, and physiology.

The cell division cycle in eukaryotic cells is a series of highly coordinated molecular interactions that ensure that cell growth, duplication of genetic material, and actual cell division are precisely orchestrated to give rise to two viable progeny cells. Moreover, the cell cycle machinery is responsible for incorporating information about external cues or internal processes that the cell must keep track of to ensure a coordinated, timely progression of all related processes. This is most pronounced in multicellular organisms, but also a cardinal feature in model organisms such as baker's yeast.

Identification of periodic attractors in Boolean networks using a priori information.

Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method.

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models.

The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters.

Using rxncon to Develop Rule-Based Models.

We present a protocol for building, validating, and simulating models of signal transduction networks. These networks are challenging modeling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalize the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics.

A comprehensive, mechanistically detailed, and executable model of the cell division cycle in Saccharomyces cerevisiae.

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible.

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.

The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales.

Systems biology holds the promise to integrate multiple sources of information in order to build ever more complete models of cellular function. To do this, the field must overcome two significant challenges. First, the current strategy to model average cells must be replaced with population based models accounting for cell-to-cell variability.

Network reconstruction and validation of the Snf1/AMPK pathway in baker's yeast based on a comprehensive literature review.

Background/objectives: The SNF1/AMPK protein kinase has a central role in energy homeostasis in eukaryotic cells. It is activated by energy depletion and stimulates processes leading to the production of ATP while it downregulates ATP-consuming processes. The yeast SNF1 complex is best known for its role in glucose derepression.

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

Background: Cellular 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.

Bud-Localization of CLB2 mRNA Can Constitute a Growth Rate Dependent Daughter Sizer.

Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation. This is achieved via the cell division cycle, which is driven by the sequential accumulation and destruction of cyclins. The regulatory network around these cyclins, particularly in G1, has been interpreted as a size control network in budding yeast, and cell size as being decisive for the START transition.

A Monte Carlo evaluation of beam characteristics for total body irradiation at extended treatment distances.

The aim is to study beam characteristics at large distances when focusing on the electron component. In particular, to investigate the utility of spoilers with various thicknesses as an electron source, as well as the effect of different spoiler-to-surface distances (STSD) on the beam characteristics and, consequently, on the dose in the superficial region. A MC model of a 15 MV Varian accelerator, validated earlier by experimental data at isocenter and extended distances used in large-field total body irradiation, is applied to evaluate beam characteristics at distances larger than 400 cm.

Bridging the gaps in systems biology.

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.

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.

Reaction-contingency based bipartite Boolean modelling.

Background: Intracellular 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.

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.

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.

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.

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

Background: Spore 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.

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.

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.

The Ashbya gossypii EF-1α promoter of the ubiquitously used MX cassettes is toxic to Saccharomyces cerevisiae.

Protein overexpression based on introduction of multiple gene copies is well established. To improve purification or quantification, proteins are typically fused to peptide tags. In Saccharomyces cerevisiae, this has been hampered by multicopy toxicity of the TAP and GFP cassettes used in the global strain collections.