MOTIVATION: Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS: We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION: The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Computer-assisted automatic quantification (CAQ) was developed as an alternative method for the diagnosis of hepatic steatosis in order to compensate for observer-dependent bias. Here, we aim to demonstrate that CAQ can provide an accurate and precise result in analysis of fatty content, but that it is inappropriate to validate CAQ by comparison with conventional pathologist estimation (PE). Male rats were fed with a methionine-choline-deficient plus high-fat diet for three days, one week, or two weeks to induce mild, moderate, or severe steatosis. Samples were collected from all liver lobes. Severity of hepatic steatosis was assessed by an experienced pathologist who estimated the percentage of hepatocytes containing lipid droplets. Fatty content was quantified by PE, CAQ, and biochemical analysis (BA). CAQ, PE, and BA can correctly reflect severe fatty change. However, in the case of mild and moderate steatosis, PE could not reflect the true fatty content ( r between PE and BA was <0). The result of CAQ correlated well with that of BA among the various degrees of severity of hepatic steatosis. In conclusion, due to a difference between event-based and surface-based analysis, it is inappropriate to validate the CAQ of hepatic steatosis by comparison with PE.

BACKGROUND: Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers. RESULTS: Our presented automatic approach for hepatocyte (HC) quantification is suitable for the analysis of an entire digitized histological section given in form of a series of images. It is the main part of an automatic hepatocyte quantification tool that allows for the computation of the ratio between the number of proliferating HC-nuclei and the total number of all HC-nuclei for a series of images in one processing run. The processing pipeline allows us to obtain desired and valuable results for a wide range of images with different properties without additional parameter adjustment. Comparing the obtained segmentation results with a manually retrieved segmentation mask which is considered to be the ground truth, we achieve results with sensitivity above 90% and false positive fraction below 15%. CONCLUSIONS: The proposed automatic procedure gives results with high sensitivity and low false positive fraction and can be applied to process entire stained sections.