Fiete Haack

Adapting rule-based model descriptions for simulating in continuous and hybrid space

Space plays an ever increasing role in cell biological modeling and simulation. This ranges from compartmental dynamics, via mesh-based approaches, to individuals moving in continuous space. An attributed, multi-level, rule-based language, ML-Space, is presented that allows to integrate these different types of spatial dynamics within one model. The associated simulator combines Gillespies method, the Next Subvolume method, and Brownian dynamics. This allows the simulation of reaction diffusion systems as well as taking excluded volume effects into account. A small example illuminates the potential of the approach in dealing with complex spatial dynamics like those involved in studying the dynamics of lipid rafts and their role in receptor co-localization.

Spatio-temporal Model of Endogenous ROS and Raft-Dependent WNT/Beta-Catenin Signaling Driving Cell Fate Commitment in Human Neural Progenitor Cells

Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model’s predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the model. Thus, our results provide both new insights and means to further our understanding of canonical WNT/β-catenin signaling and the role of ROS as intracellular signaling mediator.

Special Section on Uncertainty and Parameter Space Analysis in Visualization: Supporting the integrated visual analysis of input parameters and simulation trajectories

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides of the same coin; since the input parameters influence the simulation outcome, it is desirable to visualize and explore both in a combined manner. The main challenge posed by such an integrated visualization is the combinatorial explosion of possible parameter combinations. It leads to insurmountably high simulation runtimes and screen space requirements for their visualization. The Visual Analytics approach presented in this paper targets this issue by providing a visualization of a coarsely sampled subspace of the parameter space and its corresponding simulation outcome. In this visual representation, the analyst can identify regions for further drill-down and thus finer subsampling. We aid this identification by providing visual cues based on heterogeneity metrics. These indicate in which regions of the parameter space deviating behavior occurs at a more fine-grained scale and thus warrants further investigation and possible re-computation. We demonstrate our approach in the domain of systems biology by a visual analysis of a rule-based model of the canonical Wnt signaling pathway that plays a major role in embryonic development. In this case, the aim of the domain experts was to systematically explore the parameter space to determine those parameter configurations that match experimental data sufficiently well.

Studying the Role of Lipid Rafts on Protein Receptor Bindings with Cellular Automata

It is widely accepted that lipid rafts promote receptor clustering and thereby facilitate signaling transduction. The role of lipid rafts in inducing and promoting receptor accumulation within the cell membrane has been explored by several computational and experimental studies. However, it remains unclear whether lipid rafts influence the recruitment and binding of proteins from the cytosol as well. To provide an answer to this question a spatial membrane model has been developed based on cellular automata. Our results indicate that lipid rafts indeed influence protein receptor bindings. In particular processes with slow dissociation and binding kinetics are promoted by lipid rafts, whereas fast binding processes are slightly hampered. However, the impact depends on a variety of parameters, such as the size and mobility of the lipid rafts, the induced slow down of receptors within rafts, and also the dissociation and binding kinetics of the cytosolic proteins. Thus, for any individual signaling pathway the influence of lipid rafts on protein binding might be different. To facilitate analyzing this influence given a specific pathway, our approach has been generalized into LiRaM, a modeling and simulation tool for lipid rafts models.

WorMS- a framework to support workflows in M&S

Workflows are a promising mean to increase the quality of modeling and simulation (M&S) products such as studies and models. In exploiting workflows for M&S, requirements arise that need to be reflected in the structure and components of a workflow supporting framework, such as WORMS (WORkflows for Modeling and Simulation). In WORMS, we adapt concepts of business process modeling and scientific workflows. Particular attention is given to extensibility and flexibility which is supported by a plug-in based design and by selecting workflow nets as intermediate representation for workflows. The first application of WORMS has been realized for the modeling and simulation framework JAMES II. A small case-study illuminates the role of components and their interplay during evaluating a cell biological model.

Multi-level modeling and simulation of cell biological systems with ML-rules: a tutorial

Multi-level modeling is concerned with describing a system at different levels of organization and relating their dynamics. ML-Rules is a rule-based language developed for supporting the modeling of cell biological systems. It supports nested rule schemata, the hierarchical dynamic nesting of species, the assignment of attributes and solutions to species at each level, and a flexible definition of reaction rate kinetics. As ML-Rules allows the compact description of rather complex models, means for an efficient execution were developed, e.g., approximate and adaptive algorithms. Experimentation with ML-Rules models is further supported by domain-specific languages for instrumentation and experimentation which have been developed in the context of the modeling and simulation framework JAMES II. A signaling pathway example will illustrate modeling and simulation with ML-Rules.

Genetically regulated hepatic transcripts and pathways orchestrate haematological, biochemical and body composition traits.

The liver is the central metabolic organ and exhibits fundamental functions in haematological traits. Hepatic expression, haematological, plasma biochemical, and body composition traits were assessed in a porcine model (n = 297) to establish tissue-specific genetic variations that influence the function of immune-metabolism-correlated expression networks. At FDR (false discovery rate) <1%, more than 3,600 transcripts were jointly correlated (r = |0.22–0.48|) with the traits. Functional enrichment analysis demonstrated common links of metabolic and immune traits. To understand how immune and metabolic traits are affected via genetic regulation of gene expression, eQTLs were assessed. 20517 significant (FDR < 5%) eQTLs for 1401 transcripts were identified, among which 443 transcripts were associated with at least one of the examined traits and had cis-eQTL (such as ACO1 (6.52 × 10−7) and SOD1 (6.41 × 10−30). The present study establishes a comprehensive view of hepatic gene activity which links together metabolic and immune traits in a porcine model for medical research.

A flexible architecture for modeling and simulation of diffusional association

Up to now, it is not possible to obtain analytical solutions for complex molecular association processes (e.g. Molecule recognition in Signaling or catalysis). Instead Brownian Dynamics (BD) simulations are commonly used to estimate the rate of diffusional association, e.g. to be later used in mesoscopic simulations. Meanwhile a portfolio of diffusional association (DA) methods have been developed that exploit BD. However, DA methods do not clearly distinguish between modeling, simulation, and experiment settings. This hampers to classify and compare the existing methods with respect to, for instance model assumptions, simulation approximations or specific optimization strategies for steering the computation of trajectories. To address this deficiency we propose FADA (Flexible Architecture for Diffusional Association) - an architecture that allows the flexible definition of the experiment comprising a formal description of the model in SpacePi, different simulators, as well as validation and analysis methods. Based on the NAM (Northrup-Allison-McCammon) method, which forms the basis of many existing DA methods, we illustrate the structure and functioning of FADA. A discussion of future validation experiments illuminates how the FADA can be exploited in order to estimate reaction rates and how validation techniques may be applied to validate additional features of the model.

Reusing simulation experiment specifications in developing models by successive composition — a case study of the Wnt/β-catenin signaling pathway

With the increasing size and complexity of models, developing models by composing existing ones becomes more important. We exploit the idea of reusing simulation experiments of individual models for composition to automatically generate experiments for the composed model. First, we illustrate the process of modeling based on composition and discuss the role simulation experiments can play in this process. Our focus is on semantic validation of the composed model. We explicitly specify simulation experiments in simulation experiment specification via a Scala layer, including the desired model behavioral properties and their required experiment set-ups. Models are annotated with experiment specifications, and upon composition, these specifications are adapted and automatically executed for the composed model. The approach is applied in a case study of developing a Wnt/β-catenin signaling pathway model by successively composing three individual models, where we exploit metric interval temporal logic to describe model behavioral properties and check averages of stochastic simulation results against these properties.

Genetic architecture and regulatory impact on hepatic microRNA expression linked to immune and metabolic traits

Regulation of microRNA (miRNA) expression contributes to a wide range of target gene expression and phenotypes. The miRNA expression in the liver, the central metabolic organ, was examined in 209 pigs, and integrated with haematological and clinical biomarkers of metabolic and overall health, mRNA-target expression levels and single-nucleotide polymorphism (SNP) genotypes. The expression levels of 426 miRNA species correlated with plasma haematological or biochemical traits (r² = |0.19–0.45|, false discovery rate < 5%). Pairs of these miRNAs and their predicted target mRNAs showing expressing levels associated with the identical traits were examined to understand how immune and metabolic traits are affected by miRNA-mediated regulatory networks derived by mapping miRNA abundance as an expression quantitative trait. In total, 221 miRNA-expression-QTL correspond to 164 SNPs and 108 miRNAs, including miR-34a, miR-30e, miR-148-3p, miR-204, miR-181-5p, miR-143-5p and let-7 g that also correlate with the biomarkers. Sixty-one SNPs were simultaneously associated with 29 miRNA and 41 mRNA species. The expression levels of 13 out of 29 miRNA were correlated with one of the biochemical or haematological traits. For example, the expression levels of miR-34a were correlated with serum phosphorus and cholesterin levels; miR-204, miR-15a and miR-16b were correlated with triglyceride. For haematological traits, the expression levels of miR-652 and miR-204 were correlated with the mean corpuscular haemoglobin concentration, and the expression of miR-143 was correlated with plateletcrit. Pleiotropic association analyses revealed genetic links between mRNA and miRNA on SSC6 for miR-34a, SSC9 for miR-708 and SSC14 for miR-652. Our analysis of miRNA and mRNA transcript profiles, their correlation with clinically important plasma parameters of hepatic functions as well as information on their genetic regulation provide novel regulatory networks and potential new biomarkers for immune and metabolic traits.