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. Specifications of PEtab, the PEtab Python library, as well as links to examples, and all supporting software tools are available at https://github.com/PEtab-dev/PEtab, a snapshot is available at https://doi.org/10.5281/zenodo.3732958. All original content is available under permissive licenses

Disentangling ERBB Signaling in Breast Cancer Subtypes—A Model-Based Analysis

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype

PenTag, a Versatile Platform for Synthesizing Protein-Polymer Biohybrid Materials

The site-specific and covalent conjugation of proteins on solid supports and in hydrogels is the basis for the synthesis of biohybrid materials offering broad applications. Current methods for conjugating proteins to desired targets are often challenging due to unspecific binding, unstable (noncovalent) coupling, or expensive and difficult-to-synthesize ligand molecules. Here, is presented PenTag, an approach for the bioorthogonal, highly specific, and covalent conjugation of a protein to its ligand for various applications in materials sciences. Penicillin-binding protein 3 (PBP3) is engineered and shows that this protein can be used for the stable and spontaneous conjugation of proteins to dyes, polymers, or solid supports. PenTag as a crosslinking tool is applied for synthesizing stimuli-responsive hydrogels or for the development of a biohybrid material system performing computational operations emulating a 4:2 encoder. Based on this broad applicability and the use of a small, cheap, and easy-to-functionalize ligand and a stable, soluble recombinant protein, is seen PenTag as a versatile approach toward biohybrid material synthesis.ISSN:1616-3028ISSN:1616-301

Supplementary code to Schmiester et al. *PEtab — Interoperable Specification of Parameter Estimation Problems in Systems Biology*

Each repository or tool is contained in a subfolder. In addition, there is a README.md file with further information, a list of the supporting tools and PEtab related repositories, and links to the respective websites. Further, a file download.sh is provided for easy download of the latest state of the toolsThis archive contains supplementary code for the manuscript PEtab — Interoperable Specification of Parameter Estimation Problems in Systems Biology by Schmiester, Schälte et al.. It contains a snapshot of the PEtab repositories as well as the parameter estimation tools currently supporting the formatPeer reviewe