Abstract: Motivation: Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing. Results: We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. Availability and implementation: The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models

Abstract: Zygotic genome activation (ZGA) in the development of flies, fish, frogs and mammals depends on pioneer-like transcription factors (TFs). Those TFs create open chromatin regions, promote histone acetylation on enhancers, and activate transcription. Here, we use the panel of single, double and triple mutants for zebrafish genome activators Pou5f3, Sox19b and Nanog, multi-omics and mathematical modeling to investigate the combinatorial mechanisms of genome activation. We show that Pou5f3 and Nanog act differently on synergistic and antagonistic enhancer types. Pou5f3 and Nanog both bind as pioneer-like TFs on synergistic enhancers, promote histone acetylation and activate transcription. Antagonistic enhancers are activated by binding of one of these factors. The other TF binds as non-pioneer-like TF, competes with the activator and blocks all its effects, partially or completely. This activator-blocker mechanism mutually restricts widespread transcriptional activation by Pou5f3 and Nanog and prevents premature expression of late developmental regulators in the early embryo

Abstract: Reelin is a large glycoprotein with a dual role in the mammalian brain. It regulates the positioning and differentiation of postmitotic neurons during brain development and modulates neurotransmission and memory formation in the adult brain. Alterations in the Reelin signaling pathway have been described in different psychiatric disorders. Reelin mainly signals by binding to the lipoprotein receptors Vldlr and ApoER2, which induces tyrosine phosphorylation of the adaptor protein Dab1 mediated by Src family kinases (SFKs). In turn, phosphorylated Dab1 activates downstream signaling cascades, including PI3-kinase-dependent signaling. In this work, a mechanistic model based on ordinary differential equations was built to model early dynamics of the Reelin-mediated signaling cascade. Mechanistic models are frequently used to disentangle the highly complex mechanisms underlying cellular processes and obtain new biological insights. The model was calibrated on time-resolved data and a dose-response measurement of protein concentrations measured in cortical neurons treated with Reelin. It focusses on the interplay between Dab1 and SFKs with a special emphasis on the tyrosine phosphorylation of Dab1, and their role for the regulation of Reelin-induced signaling. Model selection was performed on different model structures and a comprehensive mechanistic model of the early Reelin signaling cascade is provided in this work. It emphasizes the importance of Reelin-induced lipoprotein receptor clustering for SFK-mediated Dab1 trans-phosphorylation and does not require co-receptors to describe the measured data. The model is freely available within the open-source framework Data2Dynamics (www.data2dynamics.org). It can be used to generate predictions that can be validated experimentally, and provides a platform for model extensions both to downstream targets such as transcription factors and interactions with other transmembrane proteins and neuronal signaling pathways