PEtab—Interoperable specification of parameter estimation problems in systems biology

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.     

Intervarietal and intravarietal genetic structure in Douglas‐fir: nuclear SSRs bring novel insights into past population demographic processes,phylogeography, and intervarietal hybridization

Douglas‐fir (Pseudotsuga menziesii) is one of numerous wide‐range forest tree species represented by subspecies/varieties, which hybridize in contact zones. This study examined the genetic structure of this North American conifer and its two hybridizing varieties, coastal and Rocky Mountain, at intervarietal and intravarietal level. The genetic structure was subsequently associated with the Pleistocene refugial history, postglacial migration and intervarietal hybridization/introgression. Thirty‐eight populations from the USA and Canada were genotyped for 13 nuclear SSRs and analyzed with simulations and traditional population genetic structuring methods. Eight genetic clusters were identified. The coastal clusters embodied five refugial populations originating from five distinct refugia. Four coastal refugial populations, three from California and one from western Canada, diverged during the Pleistocene (56.9–40.1 ka). The three Rocky Mountain clusters reflected distinct refugial populations of three glacial refugia. For Canada, ice covered during the Last Glacial Maximum, we present the following three findings. (1) One refugial population of each variety was revealed in the north of the distribution range. Additional research including paleodata is required to support and determine whether both northern populations originated from cryptic refugia situated south or north of the ice‐covered area. (2) An interplay between intravarietal gene flow of different refugial populations and intervarietal gene flow by hybridization and introgression was identified. (3) The Canadian hybrid zone displayed predominantly introgressants of the Rocky Mountain into the coastal variety. This study provides new insights into the complex Quaternary dynamics of this conifer essential for understanding its evolution (outside and inside the native range), adaptation to future climates and for forest management.     

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

     

Climate variability drives recent tree mortality in Europe

Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental‐scale patterns of recent tree mortality from the only consistent pan‐European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold‐induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite‐derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large‐scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state‐of‐the‐art remote sensing to anticipate an increased likelihood of tree mortality in space and time.     

Threshold-free population analysis identifies larger DRG neurons to respond stronger to NGF stimulation

Sensory neurons in dorsal root ganglia (DRG) are highly heterogeneous in terms of cell size, protein expression, and signaling activity. To analyze their heterogeneity, threshold-based methods are commonly used, which often yield highly variable results due to the subjectivity of the individual investigator. In this work, we introduce a threshold-free analysis approach for sparse and highly heterogeneous datasets obtained from cultures of sensory neurons. This approach is based on population estimates and completely free of investigator-set parameters. With a quantitative automated microscope we measured the signaling state of single DRG neurons by immunofluorescently labeling phosphorylated, i.e., activated Erk1/2. The population density of sensory neurons with and without pain-sensitizing nerve growth factor (NGF) treatment was estimated using a kernel density estimator (KDE). By subtraction of both densities and integration of the positive part, a robust estimate for the size of the responsive subpopulations was obtained. To assure sufficiently large datasets, we determined the number of cells required for reliable estimates using a bootstrapping approach. The proposed methods were employed to analyze response kinetics and response amplitude of DRG neurons after NGF stimulation. We thereby determined the portion of NGF responsive cells on a true population basis. The analysis of the dose dependent NGF response unraveled a biphasic behavior, while the study of its time dependence showed a rapid response, which approached a steady state after less than five minutes. Analyzing two parameter correlations, we found that not only the number of responsive small-sized neurons exceeds the number of responsive large-sized neurons--which is commonly reported and could be explained by the excess of small-sized cells--but also the probability that small-sized cells respond to NGF is higher. In contrast, medium-sized and large-sized neurons showed a larger response amplitude in their mean Erk1/2 activity.     

Internalization of Met Requires the Co-Receptor CD44v6 and Its Link to ERM Proteins

Receptor Tyrosine Kinases (RTKs) are involved in many cellular processes and play a major role in the control of cell fate. For these reasons, RTK activation is maintained under tight control. Met is an essential RTK that induces proliferation, differentiation, migration, survival and branching morphogenesis. Deregulation of Met by overexpression, amplification or lack of effective degradation leads to cancer and metastasis. We have shown that Met relies on CD44v6 for its activation and for signaling in several cancer cell lines and also in primary cells. In this paper, we show that internalization of Met is dependent on CD44v6 and the binding of Ezrin to the CD44v6 cytoplasmic domain. Both CD44v6 and Met are co-internalized upon Hepatocyte Growth Factor induction suggesting that Met-induced signaling from the endosomes relies on its collaboration with CD44v6 and the link to the cytoskeleton provided by ERM proteins.     

c-Met recruits ICAM-1 as a coreceptor to compensate for the loss of CD44 in Cd44 null mice

CD44 isoforms act as coreceptors for the receptor tyrosine kinases c-Met and VEGFR-2. However, Cd44 knockout mice do not show overt phenotypes, in contrast to Met and Vegfr-2 knockout mice. We hypothesized that CD44 is being compensated for by another factor in Cd44 null mice. Using RNAi technology and blocking experiments with antibodies, peptides, and purified ectodomains, as well as overexpression studies, we identified intercellular adhesion molecule-1 (ICAM-1) as a new coreceptor for c-Met in CD44-negative tumor cells and in primary hepatocytes obtained from Cd44 null mice. Most strikingly, after partial hepatectomy, CD44v6-specific antibodies inhibited liver cell proliferation and c-Met activation in wild-type mice, whereas ICAM-1-specific antibodies interfered with liver cell proliferation and c-Met activation in Cd44 knockout mice. These data show that ICAM-1 compensates for CD44v6 as a coreceptor for c-Met in Cd44 null mice. Compensation of proteins by members of the same family has been widely proposed to explain the lack of phenotype of several knockout mice. Our experiments demonstrate the functional substitution of a protein by a heterologous one in a knockout mouse.