Table 2.

Main features of the benchmarks.The model IDs follow the nomenclature in Villaverde et al. (2015) and Fröhlich et al. (2017a)

ID	B2	B3	B4	B5	BM1	BM3	TSP
Original ref.	Chassagnole et al. (2002)	Kotte et al. (2010)	Villaverde et al. (2014)	MacNamara et al. (2012)	Smith and Shanley (2013)	Chen et al. (2009)	Moles et al. (2003)
Organism	Escherichia coli	Escherichia coli	Chinese hamster	Generic	Mouse	Human	Generic
Description	Metabolic	Metabolic	Metabolic	Signaling	Signaling	Signaling	Metabolic
level		& transcription
Parameters	116	178	117	86	383	219	36
Upper bounds	$10·p_{\text{ref}}$	$10·p_{\text{ref}}$	$10·p_{\text{ref}}$	varying	$10·p_{\text{ref}}$	${10}^3·p_{\text{ref}}$	${10}^5·p_{\text{ref}}$
Lower bounds	$0.1·p_{\text{ref}}$	$0.1·p_{\text{ref}}$	$0.1·p_{\text{ref}}$	varying	$0.1·p_{\text{ref}}$	${10}^{-3}·p_{\text{ref}}$	${10}^{-5}·p_{\text{ref}}$
Dynamic states	18	47	34	26	104	500	8
Observed states	9	47	13	6	12	5	8
Experiments	1	1	1	10	1	4	16
Data points	110	7567	169	960	120	105	2688
Data type	Measured	Simulated	Simulated	Simulated	Measured	Measured	Simulated
Noise level	Reala	No noise	Variableb	Uniformc	Reala	Reala	$\sigma =10{\%}^d$

^aNoise levels are unknown as real measurement data are used.

^bNoise levels differ for readouts.

^cNoise level is constant ($=0.05$); the data values generated by this model are between 0 and 1 by construction.

^dNoise levels are proportional to the signal intensity.