A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

413 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Network Inference and Biological Dynamics (2011, methodology): “Kinetic models have been described for many cellular processes [Cantone et al. (2009); Schoeberl et al. (2002); Swat, Kel and Herzel (2004); Wilkinson (2009)].”
Revealing strengths and weaknesses of methods for gene network inference (2010, background): “…gene networks to produce different types of steady-state and time-series gene expression data that are commonly used for gene network inference (8, 12): steady-state expression levels of the unperturbed network, steady-state levels of knockout and knockdown experiments for every gene, and…”
Advantages and limitations of current network inference methods (2010, background): “To further bridge the gap between in silico and real data, the use of synthetic gene networks has been propose”
Quantitative and logic modelling of gene and molecular networks (2015, background): “However, the advantages and pitfalls of the various categories are often complementary, and the best approach to robustly infer accurate networks seems to be a combination of methods (81).”
Network inference using steady-state data and Goldbeter-Koshland kinetics (2012, methodology): “For example gene regulation may be modelled using Michaelis-Menten functionals (Cantone et al., 2009), and metabolism may be modelled using c© Oxford University Press 2005.”