ReviewJournal of the Royal Society, Interface2014
Reverse engineering and identification in systems biology: strategies, perspectives and challenges.
Review in Journal of the Royal Society, Interface, 2014. 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
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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
95 citing papers in PubMed.
- Optimization-based framework with flux balance analysis (FBA) and metabolic pathway analysis (MPA) for identifying metabolic objective functions.PLoS computational biology · 2025Article
- Analysing the Structural Identifiability and Observability of Mechanistic Models of Tumour Growth.Bioengineering (Basel, Switzerland) · 2025Article
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Conformal prediction for uncertainty quantification in dynamic biological systems.PLoS computational biology · 2025Article
- The Role of Machine Learning Models in Predicting Cirrhosis Mortality: A Systematic Review.Cureus · 2025Review
- Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.NPJ systems biology and applications · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.Bulletin of mathematical biology · 2024Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- Balancing competing effects of tissue growth and cytoskeletal regulation during Drosophila wing disc development.Nature communications · 2024Article
- Article
- Information integration during bioelectric regulation of morphogenesis of the embryonic frog brain.iScience · 2023Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration.PLoS computational biology · 2023Article
- The SunBEAm birth cohort: Protocol design.The journal of allergy and clinical immunology. Global · 2023Article
- Predictive power of non-identifiable models.Scientific reports · 2023Article
- Model certainty in cellular network-driven processes with missing data.PLoS computational biology · 2023Article
- Cracking the Code of Neuronal Cell Fate.Cells · 2023Review
- Sloppiness: Fundamental study, new formalism and its application in model assessment.PloS one · 2023Article
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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?
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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.