Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.
ABSTRACT Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.
- [Show abstract] [Hide abstract]
ABSTRACT: Activation of the antigen receptors on the surface of B cells in response to their cognate ligands is tightly controlled by feedback mechanisms. Apart from ligand induced signaling, B cell receptors (BCRs) emanate ligand independent tonic signaling crucial for B cell survival and development. In the absence of a ligand, BCR tonic signaling is controlled by the basal activity of the Src family protein tyrosine kinase Lyn and the protein tyrosine phosphatase SHP. The binding of an antigen to the BCR causes receptor clustering or aggregation which is one of the earliest events in B cell activation. Lyn binds to aggregated receptors and phosphorylates them. In turn phosphorylation enhances the stability of receptor clusters against dissociation into monomers as well as the binding of Lyn to the receptor clusters, thereby producing positive feedback loops that enhance receptor clustering and activation. Apart from Lyn mediated positive feedback loops, SHP and BCR aggregates mutually inhibit each other to form a double negative feedback loop. Here, we present a simple computational model of BCR proximal signaling that incorporates these multiple feedback loops between the three molecules BCR, Lyn and SHP and their complexes. The model predicts bistable behaviour in the system that explains both the tonic signaling and ligand mediated receptor activation and a range of other biological phenomena in a unified manner. We find the bistability to be highly tunable by changes in the protein levels while remaining sufficiently robust to changes in the rate constants. The nested architecture of multiple feedback loops enhances the robustness of the bistability. Our model explains the recent experimental observation of the lack of response of germinal center B cells to ligand stimulation in terms of the tunability of the bistable switch by modification of SHP levels.Molecular BioSystems 07/2013; · 3.35 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: In this work we present results of a detailed Bayesian parameter estimation for an analysis of ordinary differential equation models. These depend on many unknown parameters that have to be inferred from experimental data. The statistical inference in a high-dimensional parameter space is however conceptually and computationally challenging. To ensure rigorous assessment of model and prediction uncertainties we take advantage of both a profile posterior approach and Markov chain Monte Carlo sampling. We analyzed a dynamical model of the JAK2/STAT5 signal transduction pathway that contains more than one hundred parameters. Using the profile posterior we found that the corresponding posterior distribution is bimodal. To guarantee efficient mixing in the presence of multimodal posterior distributions we applied a multi-chain sampling approach. The Bayesian parameter estimation enables the assessment of prediction uncertainties and the design of additional experiments that enhance the explanatory power of the model. This study represents a proof of principle that detailed statistical analysis for quantitative dynamical modeling used in systems biology is feasible also in high-dimensional parameter spaces.Mathematical biosciences 04/2013; · 1.30 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.PLoS Computational Biology 12/2012; 8(12):e1002815. · 4.87 Impact Factor