AMICI: high-performance sensitivity analysis for large ordinary differential equation models

Fröhlich, Fabian; Weindl, Daniel; Schälte, Yannik; Pathirana, Dilan; Paszkowski, Łukasz; Lines, Glenn Terje; Stapor, Paul; Hasenauer, Jan

doi:10.1093/bioinformatics/btab227

Cited by 52 publications

(52 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, a straight-forward ODE implementation is not supported in pyPESTO at the time of publication. Instead, it requires the use of AMICI [ 73 ] via PETab or SBML or manual implementation up to the objective function. In contrast, separates calibration modeling entirely from the process modeling workflow, thereby becoming a valuable toolbox for calibration tasks even without process modeling.…”

Section: Resultsmentioning

confidence: 99%

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

et al. 2022

View full text Add to dashboard Cite

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

show abstract

Section: Resultsmentioning

confidence: 99%

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A commonly used approach with dynamic models is L1 or Tikhonov regularization, which adds a penalty on parameters that deviate from a specific value, favoring the estimates that resemble experimental measurements [124][125][126][127]. Furthermore, to deal with big scale kinetics models, multiple toolboxes have been developed that assist in the development and analysis of this large-scale models [128][129][130][131], and benchmarking studies have evaluated their performance in different setups [14,132], which will help the modeler select the tool that is best suited for a particular problem.…”

Section: Chen Et Al [118] Smallbone Et Al [16] Kesten Et Al [20]mentioning

confidence: 99%

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

et al. 2022

View full text Add to dashboard Cite

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

show abstract

“…Model optimization was performed using pyPESTO 0.2.10 (https://doi.org/10.5281/zenodo.5827905) with fides (Fröhlich & Sorger) version 0.7.5 (https://doi.org/10.5281/zenodo.6038127) as optimizer and AMICI (Fröhlich et al , 2021) version 0.11.25 (https://doi.org/10.5281/zenodo.6025361) as simulation engine. 10 3 optimization runs were performed using randomly sampled initial parameter values.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

Fröhlich

Gerosa

Muhlich

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

BRAFV600E is prototypical of oncogenic mutations that can be targeted therapeutically and treatment of BRAF-mutant melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring causes BRAFV600E melanoma cells to rapidly acquire a drug-adapted state. In patients this is thought to promote acquisition or selection for resistance mutations and disease recurrence. In this paper we use an energy-based implementation of ordinary differential equations in combination with proteomic, transcriptomic and imaging data from melanoma cells, to model the precise mechanisms responsible for adaptive rewiring. We demonstrate the presence of two parallel MAPK (RAF-MEK-ERK kinase) reaction channels in BRAFV600E melanoma cells that are differentially sensitive to RAF and MEK inhibitors. This arises from differences in protein oligomerization and allosteric regulation induced by oncogenic mutations and drug binding. As a result, the RAS-regulated MAPK channel can be active under conditions in which the BRAFV600E-driven channel is fully inhibited. Causal tracing demonstrates that this provides a sufficient quantitative explanation for initial and acquired responses to multiple different RAF and MEK inhibitors individually and in combination.

show abstract

AMICI: high-performance sensitivity analysis for large ordinary differential equation models

Cited by 52 publications

References 14 publications

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

Bayesian calibration, process modeling and uncertainty quantification in biotechnology

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

Contact Info

Product

Resources

About