ArticleBMC systems biology2009
Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling.
Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 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
95 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.Molecular systems biology · 2026Article
- Logic-based modeling of inflammatory macrophage cross talk with glomerular endothelial cells in diabetic kidney disease.American journal of physiology. Renal physiology · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Global Sensitivity Analysis of a Novel Signaling Network for Heart Growth With Local IGF1 Production.International journal for numerical methods in biomedical engineering · 2025Article
- Irreversibility in bacterial regulatory networks.Science advances · 2024Article
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems.PLoS computational biology · 2024Article
- A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets.Mathematical biosciences · 2024Article
- Assessing biological network dynamics: comparing numerical simulations with analytical decomposition of parameter space.NPJ systems biology and applications · 2023Article
- Reducing Boolean networks with backward equivalence.BMC bioinformatics · 2023Article
- Leveraging network structure in nonlinear control.NPJ systems biology and applications · 2022Review
- From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
- Boolean modelling in plant biology.Quantitative plant biology · 2022Review
- Discrete Logic Modeling of Cell Signaling Pathways.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics.The AAPS journal · 2021Article
- Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.Frontiers in oncology · 2021Review
- Biological Regulatory Network (BRN) Analysis and Molecular Docking Simulations to Probe the Modulation of IPGenes · 2020Article
35 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments.
resultsIn this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one.
conclusionThe presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems.
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.