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.

Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, it is difficult to assess the performance of different imputation methods and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of an imputation algorithm (DIMA). The performance and broad applicability of DIMA are demonstrated on 142 quantitative proteomics data sets from the PRoteomics IDEntifications (PRIDE) database and on simulated data consisting of 5–50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm, which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 80% of the cases. DIMA implementation is available in MATLAB at and in R at .

Epithelial repair relies on the activation of stress signaling pathways to coordinate tissue repair. Their deregulation is implicated in chronic wound and cancer pathologies. Using TNF-α/Eiger-mediated inflammatory damage to Drosophila imaginal discs, we investigate how spatial patterns of signaling pathways and repair behaviors arise. We find that Eiger expression, which drives JNK/AP-1 signaling, transiently arrests proliferation of cells in the wound center and is associated with activation of a senescence program. This includes production of the mitogenic ligands of the Upd family, which allows JNK/AP-1-signaling cells to act as paracrine organizers of regeneration. Surprisingly, JNK/AP-1 cell-autonomously suppress activation of Upd signaling via Ptp61F and Socs36E, both negative regulators of JAK/STAT signaling. As mitogenic JAK/STAT signaling is suppressed in JNK/AP-1-signaling cells at the center of tissue damage, compensatory proliferation occurs by paracrine activation of JAK/STAT in the wound periphery. Mathematical modelling suggests that cell-autonomous mutual repression between JNK/AP-1 and JAK/STAT is at the core of a regulatory network essential to spatially separate JNK/AP-1 and JAK/STAT signaling into bistable spatial domains associated with distinct cellular tasks. Such spatial stratification is essential for proper tissue repair, as coactivation of JNK/AP-1 and JAK/STAT in the same cells creates conflicting signals for cell cycle progression, leading to excess apoptosis of senescently stalled JNK/AP-1-signaling cells that organize the spatial field. Finally, we demonstrate that bistable separation of JNK/AP-1 and JAK/STAT drives bistable separation of senescent signaling and proliferative behaviors not only upon tissue damage, but also in RasV12, scrib tumors. Revealing this previously uncharacterized regulatory network between JNK/AP-1, JAK/STAT, and associated cell behaviors has important implications for our conceptual understanding of tissue repair, chronic wound pathologies, and tumor microenvironments.

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In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. However, the performance evaluation of competing methods is limited by the small amount of publicly available data from biological experiments. Therefore, simulation studies with a realistic representation of the data are a promising alternative and bring the advantage of knowing the ground truth. Results: We present an approach for designing a realistic simulation study. Based on 19 published systems biology models with experimental data, we assess typical measurement characteristics such as observables, observation type, measurement error, and observation times. For the latter, we estimate typical time features by fitting a transient response function. We demonstrate the approach on a meal model of the glucose insulin system, a mitogen-activated protein-kinase cascade and a model for the epidermal growth factor signaling. The performance of the realistic design is validated on 9 systems biology models in terms of optimization, integration and identifiability. For any dynamic model downloaded from an online database, our algorithm analyzes the model dynamics and specifies a realistic experimental design. The approach is specifically suited for systematic benchmarking of methods for time-course data in the context of systems biology. In particular, various application settings such as number of parameters, initial conditions, error model etc. can be tested. Availability: The approach is implemented in the MATLAB-based modelling toolbox Data2Dynamics and available at https://github.com/Data2Dynamics/d2d.