Ursula Klingmüller

Specific recruitment of SH-PTP1 to the erythropoietin receptor causes inactivation of JAK2 and termination of proliferative signals.

The binding of erythropoietin (EPO) to its receptor (EPO-R) activates the protein tyrosine kinase JAK2. The mechanism of JAK2 inactivation has been unclear. We show that the hematopoietic protein tyrosine phosphatase SH-PTP1 (also called HCP and PTP1C) associates via its SH2 domains with the tyrosine-phosphorylated EPO-R. In vitro binding studies suggest that Y429 in the cytoplasmic domain of the EPO-R is the binding site for SH-PTP1. Mutant EPO-Rs lacking Y429 are unable to bind SH-PTP1; cells expressing such mutants are hypersensitive to EPO and display prolonged EPO-induced autophosphorylation of JAK2. Our results suggest that activation of SH-PTP1 by binding to the EPO-R plays a major role in terminating proliferative signals.

Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

MOTIVATION Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. RESULTS We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. AVAILABILITY An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The liver-specific microRNA miR-122 controls systemic iron homeostasis in mice

Systemic iron homeostasis is mainly controlled by the liver through synthesis of the peptide hormone hepcidin (encoded by Hamp), the key regulator of duodenal iron absorption and macrophage iron release. Here we show that the liver-specific microRNA miR-122 is important for regulating Hamp mRNA expression and tissue iron levels. Efficient and specific depletion of miR-122 by injection of a locked-nucleic-acid-modified (LNA-modified) anti-miR into WT mice caused systemic iron deficiency, characterized by reduced plasma and liver iron levels, mildly impaired hematopoiesis, and increased extramedullary erythropoiesis in the spleen. Moreover, miR-122 inhibition increased the amount of mRNA transcribed by genes that control systemic iron levels, such as hemochromatosis (Hfe), hemojuvelin (Hjv), bone morphogenetic protein receptor type 1A (Bmpr1a), and Hamp. Importantly, miR-122 directly targeted the 3′ untranslated region of 2 mRNAs that encode activators of hepcidin expression, Hfe and Hjv. These data help to explain the increased Hamp mRNA levels and subsequent iron deficiency in mice with reduced miR-122 levels and establish a direct mechanistic link between miR-122 and the regulation of systemic iron metabolism.

Simulation Methods for Optimal Experimental Design in Systems Biology

To obtain a systems-level understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often robust with respect to variations in some of the parameters. Hence, it is important to use optimal experimental design considerations in advance of the experiments to improve the information content of the measurements. Using the MAP-Kinase pathway as an example, the authors present a simulation study investigating the application of different optimality criteria. It is demonstrated that experimental design significantly improves the parameter estimation accuracy and also reveals difficulties in parameter estimation due to robustness.

Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor

Seeing EPO The supply of red blood cells in mammals is controlled by the cytokine erythropoietin (EPO). In physiological situations, the concentration of EPO can change by 1000-fold. Becker et al. (p. 1404, published online 20 May) used a combination of mathematical modeling and experimental analysis to discern how cells can maintain a linear response to such a broad range of EPO concentrations. Critical features included internalization of EPO-bound receptors and subsequent degradation of the EPO ligand. Replenishment of receptors at the cell surface required a large supply of EPO receptors maintained in reserve inside the cell. These mechanisms allow cells to experience large increases in EPO concentration without becoming refractory to further stimulation. Modeling and experiments help to explain responsiveness of red blood cell precursors to very large changes in a proliferative signal. Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

Lessons learned from quantitative dynamical modeling in systems biology.

Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.

Addressing parameter identifiability by model-based experimentation.

Mathematical description of biological processes such as gene regulatory networks or signalling pathways by dynamic models utilising ordinary differential equations faces challenges if the model parameters like rate constants are estimated from incomplete and noisy experimental data. Typically, biological networks are only partially observed. Only a fraction of the modelled molecular species is measurable directly. This can result in structurally non-identifiable model parameters. Furthermore, practical non-identifiability can arise from limited amount and quality of experimental data. In the challenge of growing model complexity on one side, and experimental limitations on the other side, both types of non-identifiability arise frequently in systems biological applications often prohibiting reliable prediction of system dynamics. On theoretical grounds this article summarises how and why both types of non-identifiability arise. It exemplifies pitfalls where models do not yield reliable predictions of system dynamics because of non-identifiabilities. Subsequently, several approaches for identifiability analysis proposed in the literature are discussed. The aim is to provide an overview of applicable methods for detecting parameter identifiability issues. Once non-identifiability is detected, it can be resolved either by experimental design, measuring additional data under suitable conditions; or by model reduction, tailoring the size of the model to the information content provided by the experimental data. Both strategies enhance model predictability and will be elucidated by an example application. [Includes supplementary material].

Stat5 activation enables erythropoiesis in the absence of EpoR and Jak2

Erythropoiesis requires erythropoietin (Epo) and stem cell factor (SCF) signaling via their receptors EpoR and c-Kit. EpoR, like many other receptors involved in hematopoiesis, acts via the kinase Jak2. Deletion of EpoR or Janus kinase 2 (Jak2) causes embryonic lethality as a result of defective erythropoiesis. The contribution of distinct EpoR/Jak2-induced signaling pathways (mitogen-activated protein kinase, phosphatidylinositol 3-kinase, signal transducer and activator of transcription 5 [Stat5]) to functional erythropoiesis is incompletely understood. Here we demonstrate that expression of a constitutively activated Stat5a mutant (cS5) was sufficient to relieve the proliferation defect of Jak2(-/-) and EpoR(-/-) cells in an Epo-independent manner. In addition, tamoxifen-induced DNA binding of a Stat5a-estrogen receptor (ER)* fusion construct enabled erythropoiesis in the absence of Epo. Furthermore, c-Kit was able to enhance signaling through the Jak2-Stat5 axis, particularly in lymphoid and myeloid progenitors. Although abundance of hematopoietic stem cells was 2.5-fold reduced in Jak2(-/-) fetal livers, transplantation of Jak2(-/-)-cS5 fetal liver cells into irradiated mice gave rise to mature erythroid and myeloid cells of donor origin up to 6 months after transplantation. Cytokine- and c-Kit pathways do not function independently of each other in hematopoiesis but cooperate to attain full Jak2/Stat5 activation. In conclusion, activated Stat5 is a critical downstream effector of Jak2 in erythropoiesis/myelopoiesis, and Jak2 functionally links cytokine- with c-Kit-receptor tyrosine kinase signaling.

Computational processing and error reduction strategies for standardized quantitative data in biological networks

High‐quality quantitative data generated under standardized conditions is critical for understanding dynamic cellular processes. We report strategies for error reduction, and algorithms for automated data processing and for establishing the widely used techniques of immunoprecipitation and immunoblotting as highly precise methods for the quantification of protein levels and modifications. To determine the stoichiometry of cellular components and to ensure comparability of experiments, relative signals are converted to absolute values. A major source for errors in blotting techniques are inhomogeneities of the gel and the transfer procedure leading to correlated errors. These correlations are prevented by randomized gel loading, which significantly reduces standard deviations. Further error reduction is achieved by using housekeeping proteins as normalizers or by adding purified proteins in immunoprecipitations as calibrators in combination with criteria‐based normalization. Additionally, we developed a computational tool for automated normalization, validation and integration of data derived from multiple immunoblots. In this way, large sets of quantitative data for dynamic pathway modeling can be generated, enabling the identification of systems properties and the prediction of targets for efficient intervention.

Theoretical and experimental analysis links isoform- specific ERK signalling to cell fate decisions

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal‐regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin‐induced pathway activation in primary erythroid progenitor (colony‐forming unit erythroid stage, CFU‐E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU‐E cells. Model analysis showed bow‐tie‐shaped signal processing and inherently transient signalling for cytokine‐induced ERK signalling. Sensitivity analysis predicted that, through a feedback‐mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over‐expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double‐phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.