ArticleCellular and molecular bioengineering2021
Article in Cellular and molecular bioengineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed.
- A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.Bulletin of mathematical biology · 2026Article
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- LAG3+ CD8+ T cell subset drives HR+/HER2- breast cancer reduction in bispecific antibody armed activated T cell therapy.Journal of immunology (Baltimore, Md. : 1950) · 2025Article
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Distinguishing multiple roles of T cell and macrophage involvement in determining lymph node fates during Mycobacterium tuberculosis infection.PLoS computational biology · 2025Article
- Timing matters in macrophage/CD4+ T cell interactions: an agent-based model comparingmSystems · 2025Article
- Article
- SimService: a lightweight library for building simulation services in Python.Bioinformatics (Oxford, England) · 2024Article
- Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.PLoS computational biology · 2023Article
- FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.Bioinformatics (Oxford, England) · 2023Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
- Calibration methods to fit parameters within complex biological models.Frontiers in applied mathematics and statistics · 2023Article
- A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.Frontiers in systems biology · 2023Article
- Pharmacokinetics and Pharmacodynamics of TAK-164 Antibody Drug Conjugate Coadministered with Unconjugated Antibody.The AAPS journal · 2022Article
- Quantifying the contribution of pathways of nosocomial acquisition of COVID-19 in English hospitals.International journal of epidemiology · 2022Article
- A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes.Journal of theoretical biology · 2022Article
- Simulating the Selection of Resistant Cells with Bystander Killing and Antibody Coadministration in Heterogeneous Human Epidermal Growth Factor Receptor 2-Positive Tumors.Drug metabolism and disposition: the biological fate of chemicals · 2022Article
- MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY.Current opinion in systems biology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
introductionMathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.
methodsHerein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.
resultsWe illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.
conclusionsWe present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.
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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.