ReviewIntegrative biology : quantitative biosciences from nano to macro2012
Logic-based models in systems biology: a predictive and parameter-free network analysis method.
Review in Integrative biology : quantitative biosciences from nano to macro, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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
47 citing papers in PubMed.
- Mathematical Modeling of Neuroinflammation in Neurodegenerative Diseases.CPT: pharmacometrics & systems pharmacology · 2025Review
- Vitamin D exerts endogenous control over TiScience · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis.NPJ systems biology and applications · 2024Article
- Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors.Scientific reports · 2023Article
- A multiscale mechanistic model of human dendritic cells forFrontiers in immunology · 2023Article
- Vitamin D and Systems Biology.Nutrients · 2022Review
- Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.Biotech (Basel (Switzerland)) · 2022Article
- Inference of regulatory networks through temporally sparse data.Frontiers in control engineering · 2022Article
- Inference of a Boolean Network From Causal Logic Implications.Frontiers in genetics · 2022Article
- Can Systems Biology Advance Clinical Precision Oncology?Cancers · 2021Review
- A practical guide to mechanistic systems modeling in biology using a logic-based approach.Briefings in bioinformatics · 2021Review
- Unsupervised logic-based mechanism inference for network-driven biological processes.PLoS computational biology · 2021Article
- Article
- Exact solving and sensitivity analysis of stochastic continuous time Boolean models.BMC bioinformatics · 2020Article
- Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients.Science advances · 2020Article
- Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells.Bioinformatics (Oxford, England) · 2020Article
- Integration of single-cell multi-omics for gene regulatory network inference.Computational and structural biotechnology journal · 2020Review
- A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint.Scientific reports · 2019Article
- The logic behind neural control of breathing pattern.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.
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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.