ArticleNature communications2011
Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes.
Article in Nature communications, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Chaos in synthetic microbial communities.PLoS computational biology · 2022Article
- Parameter estimation in models of biological oscillators: an automated regularised estimation approach.BMC bioinformatics · 2019Article
- Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties.Scientific reports · 2017Article
- Article
- Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.Cell reports · 2016Article
- Modelling proteasome and proteasome regulator activities.Biomolecules · 2014Review
- A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.Nature protocols · 2014Article
- Maximizing the information content of experiments in systems biology.PLoS computational biology · 2013Article
- Dynamic noise, chaos and parameter estimation in population biology.Interface focus · 2012Article
- Bayesian design strategies for synthetic biology.Interface focus · 2011Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, for example, by fitting models to a finite number of data points. Here we develop a qualitative inference framework that allows us to both reverse-engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems.
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