ArticleBioinformatics (Oxford, England)2020
Efficient parameterization of large-scale dynamic models based on relative measurements.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.PLoS computational biology · 2026Article
- Calibrating tissue level PDE models of ligand dynamics using single cell and spatial transcriptomics data.NPJ systems biology and applications · 2026Article
- Reconstructing and comparing signal transduction networks from single-cell protein quantification data.Bioinformatics (Oxford, England) · 2026Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research.Computational and structural biotechnology journal · 2024Article
- Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.Royal Society open science · 2024Article
- Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.Bioinformatics (Oxford, England) · 2024Article
- Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells.PLoS computational biology · 2024Article
- Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.iScience · 2023Article
- pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.Bioinformatics (Oxford, England) · 2023Article
- Model certainty in cellular network-driven processes with missing data.PLoS computational biology · 2023Article
- Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks.PLoS computational biology · 2023Article
- Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models.PLoS computational biology · 2022Article
- Artificial neural networks enable genome-scale simulations of intracellular signaling.Nature communications · 2022Article
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
- Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways.Neuroinformatics · 2022Review
- Article
- Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments.Patterns (New York, N.Y.) · 2021Article
- A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization.iScience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.
resultsHere, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements. AVAILABILITY AND IMPLEMENTATION: Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.