Martin Mönnigmann

Nucleocytoplasmic shuttling of persistently activated STAT3

Persistent activation of the transcription factor STAT3 has been detected in many types of cancer and plays an important role in tumor progression, immune evasion and metastasis. To analyze persistent STAT3 activation we coexpressed STAT3 with v-Src. We found that tyrosine phosphorylation of STAT3 by v-Src is independent of Janus kinases (Jaks), the canonical activators of STATs. The STAT3-induced feedback inhibitor, suppressor of cytokine signaling 3 (SOCS3), did not interfere with STAT3 activation by v-Src. However, the protein inhibitor of activated STAT3 (PIAS3) suppressed gene induction by persistently activated STAT3. We measured nucleocytoplasmic shuttling of STAT3 in single cells by bleaching the YFP moiety of double-labelled STAT3-CFP-YFP in the cytoplasm. Analysis of the subcellular distribution of CFP and YFP fluorescence over time by mathematical modeling and computational parameter estimation revealed that activated STAT3 shuttles more rapidly than non-activated STAT3. Inhibition of exportin-1-mediated nuclear export slowed down nucleocytoplasmic shuttling of v-Src-activated STAT3 resulting in reduced tyrosine phosphorylation, decreased induction of STAT3 target genes and increased apoptosis. We propose passage of persistently activated STAT3 through the nuclear pore complex as a new target for intervention in cancer.

Systematic identifiability testing for unambiguous mechanistic modeling – application to JAK-STAT, MAP kinase, and NF-κB signaling pathway models

BackgroundWhen creating mechanistic mathematical models for biological signaling processes it is tempting to include as many known biochemical interactions into one large model as possible. For the JAK-STAT, MAP kinase, and NF-κ B pathways a lot of biological insight is available, and as a consequence, large mathematical models have emerged. For large models the question arises whether unknown model parameters can uniquely be determined by parameter estimation from measured data. Systematic approaches to answering this question are indispensable since the uniqueness of model parameter values is essential for predictive mechanistic modeling.ResultsWe propose an eigenvalue based method for efficiently testing identifiability of large ordinary differential models and compare this approach to three existing ones. The methods are benchmarked by applying them to models of the signaling pathways mentioned above. In all cases the eigenvalue method proposed here and the orthogonal method find the largest set of identifiable parameters, thus clearly outperforming the other approaches. The identifiability analysis shows that the pathway models are not identifiable, even under the strong assumption that all system state variables are measurable. We demonstrate how the results of the identifiability analysis can be used for model simplification.ConclusionWhile it has undoubtedly contributed to recent advances in systems biology, mechanistic modeling by itself does not guarantee unambiguous descriptions of biological processes. We show that some recent signal transduction pathway models have reached a level of detail that is not warranted. Rigorous identifiability tests reveal that even if highly idealized experiments could be carried out to measure all state variables of these signaling pathways, some unknown parameters could still not be estimated. The identifiability tests therefore show that the level of detail of the investigated models is too high in principle, not just because too little experimental information is available. We demonstrate how the proposed method can be combined with biological insight, however, to simplify these models.

Normal Vectors on Critical Manifolds for Robust Design of Transient Processes in the Presence of Fast Disturbances

Information on steady-state bifurcations, most notably stability boundaries, is frequently used for the analysis and design of nonlinear systems. The bifurcation points separate regions with different dynamic behavior and thus give valuable information about nonlinear systems. They cannot, however, reflect the impact of fast disturbances on the transient behavior of nonlinear systems. The influence of fast disturbances can be addressed by bifurcation points that are defined as critical points during the transient behavior of a dynamic system in the presence of fast disturbances. Specifically, we consider two types of points—grazing points and end-points. At a grazing point the trajectory of a nonlinear system tangentially touches a hypersurface spanned by a state or output constraint. At an end-point the trajectory crosses the hypersurface at a specified final time. These critical points unfold to manifolds in the parameter space of the nonlinear system separating parts of the parameter space that admit t...

Protein loop structure prediction with flexible stem geometries

The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far‐from‐native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the PdbSelect25 set and to 179 loops from proteins from the Casp6 experiment. Proteins 2005.

A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

BackgroundModeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood parameter estimation, only provides valid results, if the complexity of the model is in balance with the amount and quality of the experimental data. If this is the case the model is said to be identifiable for the given data. If a model turns out to be unidentifiable, two steps can be taken. Either additional experiments need to be conducted, or the model has to be simplified.ResultsWe propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results. These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small. We choose parameter variances as stopping criterion, since they are concise and easy to interpret. For both, the parameter estimation and the calculation of parameter variances, multi-start parameter estimations are run on a parallel cluster. In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.ConclusionsWe apply the proposed approach to a model of early signaling events in the JAK-STAT pathway. The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

Fast Calculation of Spectral Bounds for Hessian Matrices on Hyperrectangles

This paper presents a fast method for the calculation of bounds on the spectra of Hessian matrix sets

Model-driven experimental analysis of the function of SHP-2 in IL-6-induced Jak/STAT signaling.

{\cal H} \{\nabla^2 \varphi(x)|x \in S\}

Robust Optimization of Fixed Points of Nonlinear Discrete Time Systems with Uncertain Parameters

of nonlinear functions

Robust Stable Nonlinear Control and Design of a CSTR in a Large Operating Range

\varphi : U\subset \mathbb{R}^n\to\mathbb{R}

Fast explicit MPC with multiway trees

on hyperrectangles