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Computational modeling can be used to investigate complex signaling networks in biology. However, most modeling tools are not suitable for molecular cell biologists with little background in mathematics. We have built a visual-based modeling tool for the investigation of dynamic networks. Here, we describe the development of computational models of cartilage development and osteoarthritis, in which a panel of relevant signaling pathways are integrated. In silico experiments give insight in the role of each of the pathway components and reveal which perturbations may deregulate the basal healthy state of cells and tissues. We used a previously developed computational modeling tool Analysis of Networks with Interactive Modeling (ANIMO) to generate an activity network integrating 7 signal transduction pathways resulting in a network containing over 50 nodes and 200 interactions. We performed in silico experiments to characterize molecular mechanisms of cell fate decisions. The model was used to mimic biological scenarios during cell differentiation using RNA-sequencing data of a variety of stem cell sources as input. In a case-study, we wet-lab-tested the model-derived hypothesis that expression of DKK1 (Dickkopf-1) and FRZB (Frizzled related protein, WNT antagonists) and GREM1 (gremlin 1, BMP antagonist) prevents IL1β (Interleukin 1 beta)-induced MMP (matrix metalloproteinase) expression, thereby preventing cartilage degeneration, at least in the short term. We found that a combination of DKK1, FRZB and GREM1 may play a role in modulating the effects of IL1β induced inflammation in human primary chondrocytes. ispartof: CELLULAR SIGNALLING vol:68 ispartof: location:England status: published

BACKGROUND: Dysregulation of protein kinase-mediated signaling is an early event in many diseases, including the most common clinical cardiac arrhythmia, atrial fibrillation (AF). Kinomic profiling represents a promising technique to identify candidate kinases.OBJECTIVE: In this study we used kinomic profiling to identify kinases altered in AF remodeling using atrial tissue from a canine model of AF (atrial tachypacing).METHODS: Left atrial tissue obtained in a previous canine study was used for kinomic array (containing 1024 kinase pseudosubstrates) analysis. Three groups of dogs were included: nonpaced controls and atrial tachypaced dogs, which were contrasted with geranylgeranylacetone-treated dogs with AF, which are protected from AF promotion, to enhance specificity of detection of putative kinases.RESULTS: While tachypacing changed activity of 50 kinases, 40 of these were prevented by geranylgeranylacetone and involved in differentiation and proliferation (SRC), contraction, metabolism, immunity, development, cell cycle (CDK4), and survival (Akt). Inhibitors of Akt (MK2206) and CDK4 (PD0332991) and overexpression of a dominant-negative CDK4 phosphorylation mutant protected against tachypacing-induced contractile dysfunction in HL-1 cardiomyocytes. Moreover, patients with AF show down- and upregulation of SRC and Akt phosphorylation, respectively, similar to findings of the kinome array.CONCLUSION: Contrasting kinomic array analyses of controls and treated subjects offer a versatile tool to identify kinases altered in atrial remodeling owing to tachypacing, which include Akt, CDK4, and SRC. Ultimately, pharmacological targeting of altered kinases may offer novel therapeutic possibilities to treat clinical AF.

Massive parallel analysis using array technology has become the mainstay for analysis of genomes and transcriptomes. Analogously, the predominance of phosphorylation as a regulator of cellular metabolism has fostered the development of peptide arrays of kinase consensus substrates that allow the charting of cellular phosphorylation events (often called kinome profiling). However, whereas the bioinformatical framework for expression array analysis is well-developed, no advanced analysis tools are yet available for kinome profiling. Especially intra-array and interarray normalization of peptide array phosphorylation remain problematic, due to the absence of “housekeeping” kinases and the obvious fallacy of the assumption that different experimental conditions should exhibit equal amounts of kinase activity. Here we describe the development of analysis tools that reliably quantify phosphorylation of peptide arrays and that allow normalization of the signals obtained. We provide a method for intraslide gradient correction and spot quality control. We describe a novel interarray normalization procedure, named repetitive signal enhancement, RSE, which provides a mathematical approach to limit the false negative results occuring with the use of other normalization procedures. Using in silico and biological experiments we show that employing such protocols yields superior insight into cellular physiology as compared to classical analysis tools for kinome profiling.

Background Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. Results ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. Conclusions Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0286-z) contains supplementary material, which is available to authorized users.

Tuberous sclerosis complex (TSC) is caused by inactivating mutations in either TSC1 or TSC2 and is characterized by uncontrolled mTORC1 activation. Drugs that reduce mTOR activity are only partially successful in the treatment of TSC, suggesting that mTOR-independent pathways play a role in disease development. Here, kinome profiles of wild-type and Tsc2−/− mouse embryonic fibroblasts (MEFs) were generated, revealing a prominent role for PAK2 in signal transduction downstream of TSC1/2. Further investigation showed that the effect of the TSC1/2 complex on PAK2 is mediated through RHEB, but is independent of mTOR and p21RAC. We also demonstrated that PAK2 over-activation is likely responsible for the migratory and cell cycle abnormalities observed in Tsc2−/− MEFs. Finally, we detected high levels of PAK2 activation in giant cells in the brains of TSC patients. These results show that PAK2 is a direct effector of TSC1-TSC2-RHEB signaling and a new target for rational drug therapy in TSC.

Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires to capture their dynamics through computational support and models. ANIMO (Analysis of Networks with Interactive MOdelling) is a tool that enables construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analysed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signalling networks. This enforces precision and uniformity in the definition of signalling pathways, contributing to the integration of isolated signalling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behaviour of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behaviour of the network components is displayed graphically, allowing for an intuitive and interactive modelling experience.

Computational modeling of biological networks permits the comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. Although a large number of software tools have been developed, the versatility of these tools is limited by mathematical complexities that prevent their broad adoption and effective use by molecular biologists. This study clarifies the basic aspects of molecular modeling, how to convert data into useful input, as well as the number of time points and molecular parameters that should be considered for molecular regulatory models with both explanatory and predictive potential. We illustrate the necessary experimental preconditions for converting data into a computational model of network dynamics. This model requires neither a thorough background in mathematics nor precise data on intracellular concentrations, binding affinities or reaction kinetics. Finally, we show how an interactive model of crosstalk between signal transduction pathways in primary human articular chondrocytes allows insight into processes that regulate gene expression.

Purpose: Computational modeling of biological networks permits comprehensive analysis of cells and tissues to define molecular phenotypes and novel hypotheses. We recently presented ANIMO (Analysis of Networks with Interactive Modeling), an intuitive software tool for modeling molecular networks for use by biologists. We used ANIMO to generate a computational model of articular cartilage, ECHO. Over 1.5 million people in the Netherlands suffer from osteoarthritis (OA) in one or more joints. OA is a painful, disabling disease and currently cannot be cured. In a subset of OA patients, joint cartilage is replaced by bone via endochondral ossification. This is a natural process in growing long bones, where transient growth plate cartilage is replaced by bone. In contrast, healthy joint cartilage is permanent as it is protected against bone formation. Stable joint cartilage is under control of master transcription factor SOX9, whereas bone formation is controlled by RUNX2. The processes that regulate the switch between a SOX9+ state and a RUNX+ state are poorly understood, which greatly hampers the development of successful therapies. Methods: Based on a large-scale literature study and our own experiments, we recently developed ECHO (Executable Chondrocyte), a computational model of the key processes that regulate expression and activity of SOX9 and RUNX2. Simulations in ECHO were performed to investigate the robustness of the chondrocyte network. To validate ECHO predictions, we used FRAP to measure mobility of SOX9 and RUNX2, which we have shown to be a faithful readout of their activity. Primary chondrocytes of 2 OA donors were tested at passage 2 after isolation. In these donors we observed little interdonor variation in SOX9 mobility. Using lipofectamine LTX we obtained 40-70% transfection efficiencies in primary human chondrocytes even at low passages. Results: In its unperturbed form, ECHO displays two stable states in which activities of SOX9 and RUNX2 are mutually exclusive. We tested the hypothesis that addition of WNT (performed with a few mouse clicks) will change permanent into transient cartilage by inducing hypertrophy. Indeed, when we add WNT, a known regulator of bone formation, the permanent or SOX9+ state changes to a transient or RUNX2+ state. However, it is known that healthy articular cartilage is resistant to hypertrophic differentiation. Our group has previously found that this was probably due to the secretion of DKK1, FRZB and GREM1. We therefore added nodes to ECHO representing DKK1, FRZB and GREM1 (figure 1). GREM1 and DKK1 are able to stabilize the permanent cartilage or SOX9+ state even after addition of WNT to ECHO. We observed that in our model activation of WNT leads to a switch from a SOX9+ state to a RUNX2+ state. To prove that WNT/β-catenin signaling can directly regulate SOX9 function, we investigated the response of SOX9 mobility to WNT3A in live primary chondrocytes. Addition of WNT3A to human chondrocytes transfected with SOX9-GFP resulted in a significant decrease of the immobile SOX9 fraction from 53% to 34% within 15 minutes after addition. A direct correlation between the elevated levels of β-catenin after WNT addition and the activity of SOX9 indicates that β-catenin can directly change the mobility by complex formation with SOX9. Conclusions: Using ECHO we predicted the stimuli that prevents hypertrophic differentiation differentiation of articular cartilage, and tested this experimentally with FRAP using SOX9 and RUNX2 mobility as a read-out.