pyPESTO: A modular and scalable tool for parameter estimation for dynamic models

Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. We present pyPESTO, a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto)

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

ICB-DCM/PEtab v0.0.0a11

PEtab - a tsv-based data format for parameter estimation problems in systems biolog

ICB-DCM/PEtab: PEtab v0.0.0a13

PEtab format updates: Add description of visualization table Cleanup Python package updates: Add visualization functions Add support for condition-specific dynamic parameter

Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines.

Background: Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer death worldwide. The molecular mechanisms of action for anti-HER-family drugs in gastric cancer cells are incompletely understood. We compared the molecular effects of trastuzumab and the other HER-family targeting drugs cetuximab and afatinib on phosphoprotein and gene expression level to gain insights into the regulated pathways. Moreover, we intended to identify genes involved in phenotypic effects of anti-HER therapies. Methods: A time-resolved analysis of downstream intracellular kinases following EGF, cetuximab, trastuzumab and afatinib treatment was performed by Luminex analysis in the gastric cancer cell lines Hs746T, MKN1, MKN7 and NCI-N87. The changes in gene expression after treatment of the gastric cancer cell lines with EGF, cetuximab, trastuzumab or afatinib for 4 or 24 h were analyzed by RNA sequencing. Significantly enriched pathways and gene ontology terms were identified by functional enrichment analysis. Furthermore, effects of trastuzumab and afatinib on cell motility and apoptosis were analyzed by time-lapse microscopy and western blot for cleaved caspase 3. Results: The Luminex analysis of kinase activity revealed no effects of trastuzumab, while alterations of AKT1, MAPK3, MEK1 and p70S6K1 activations were observed under cetuximab and afatinib treatment. On gene expression level, cetuximab mainly affected the signaling pathways, whereas afatinib had an effect on both signaling and cell cycle pathways. In contrast, trastuzumab had little effects on gene expression. Afatinib reduced average speed in MKN1 and MKN7 cells and induced apoptosis in NCI-N87 cells. Following treatment with afatinib, a list of 14 genes that might be involved in the decrease of cell motility and a list of 44 genes that might have a potential role in induction of apoptosis was suggested. The importance of one of these genes (HBEGF) as regulator of motility was confirmed by knockdown experiments. Conclusions: Taken together, we described the different molecular effects of trastuzumab, cetuximab and afatinib on kinase activity and gene expression. The phenotypic changes following afatinib treatment were reflected by altered biological functions indicated by overrepresentation of gene ontology terms. The importance of identified genes for cell motility was validated in case of HBEGF

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts

Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases.Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach.The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates

Contento L, Castelletti N, Raimúndez E, et al. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. medRxiv. 2021

pyPESTO - Parameter EStimation TOolbox for python

<ul> <li>Visualize:<ul> <li>Get optimization result by id (#1116)</li> </ul> </li> <li>Storage:<ul> <li>allow "{id}" in history storage filename (#1118)</li> </ul> </li> <li>Objective:<ul> <li>adjusted PEtab.jl syntax to new release (#1128, #1131)</li> <li>Documentation on PEtab importer updated (#1126)</li> </ul> </li> <li>Ensembles<ul> <li>Additional option for cutoff calculation (#1124)</li> <li>Ensembles from optimization endpoints now only takes free parameters (#1130)</li> </ul> </li> <li>General<ul> <li>Added How to Cite (#1125)</li> <li>Additional summary option (#1134)</li> <li>Speed up base tests (#1127)</li> </ul> </li> </ul>If you use this software, please cite it using these metadata

PEtab-dev/libpetab-python: libpetab-python v0.2.5

<ul> <li>Fix accessing <code>preequilibrationConditionId</code> without checking for presence by @dweindl in https://github.com/PEtab-dev/libpetab-python/pull/228</li> <li>Startpoint sampling for a subset of parameters by @dweindl in https://github.com/PEtab-dev/libpetab-python/pull/230</li> <li>Treat <code>observableParameter</code> overrides as placeholders in noise formulae by @dilpath in https://github.com/PEtab-dev/libpetab-python/pull/231</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/PEtab-dev/libpetab-python/compare/v0.2.4...v0.2.5</p&gt

PEtab-dev/libpetab-python: libpetab-python v0.2.8

<ul> <li>Fixed pandas <code>FutureWarning</code> in <code>petab/visualize/lint.py</code> by @dweindl in https://github.com/PEtab-dev/libpetab-python/pull/242</li> <li>Added <code>petab.Problem.n_{estimated,measurements,priors}</code> by @dweindl in https://github.com/PEtab-dev/libpetab-python/pull/243</li> <li>Require pyarrow by @dweindl in https://github.com/PEtab-dev/libpetab-python/pull/244</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/PEtab-dev/libpetab-python/compare/v0.2.7...v0.2.8</p&gt