73
Maiwald T, Timmer J (2008) Dynamical modeling and multi-experiment fitting with PottersWheel.
Bioinformatics 24(18):2037–2043. https://doi.org/10.1093/bioinformatics/btn350 (PubMed
PMID: 18614583; PubMed Central PMCID: PMC2530888)
Mischnik M, Boyanova D, Hubertus K et al (2013a) A Boolean view separates platelet activatory
and inhibitory signalling as verified by phosphorylation monitoring including threshold behav
iour and integrin modulation. Mol Biosyst 9(6):1326–1339. https://doi.org/10.1039/c3mb25597b
(PubMed PMID: 23463387 *This work uses platelets as an example to show how systems biol
ogy regulation controls the fragile balance between blood coagulation and blood flow to prevent
thrombosis or bleeding. Inhibitory and activating pathways are modeled in detail)
Mischnik M, Hubertus K, Geiger J et al (2013b) Dynamical modelling of prostaglandin signal
ling in platelets reveals individual receptor contributions and feedback properties. Mol BioSyst
9(10):2520–2529. https://doi.org/10.1039/c3mb70142e (PubMed PMID: 23903629)
Mischnik M, Gambaryan S, Subramanian H et al (2014) A comparative analysis of the bistabil
ity switch for platelet aggregation by logic ODE based dynamical modeling. Mol BioSyst
10(8):2082–2089. https://doi.org/10.1039/c4mb00170b (PubMed PMID: 24852796)
Naseem M, Philippi N, Hussain A et al (2012) Integrated systems view on networking by hormones
in Arabidopsis immunity reveals multiple crosstalk for cytokinin. Plant Cell 24(5):1793–1814.
https://doi.org/10.1105/tpc.112.098335 (*This work shows how experiment and modeling inter
act in bioinformatics to elucidate a complex plant hormone network here)
Naseem M, Kaltdorf M, Hussain A et al (2013a) The impact of cytokinin on jasmonate-salicylate
antagonism in Arabidopsis immunity against infection with Pst DC3000. Plant Signal Behav
8(10). https://doi.org/10.4161/psb.26791 (PubMed PMID: 24494231)
Naseem M, Kunz M, Ahmed N et al (2013b) Integration of Boolean models on hormonal interactions
and prospects of cytokinin-auxin crosstalk in plant immunity. Plant Signal Behav 8(4):e23890.
https://doi.org/10.4161/psb.23890 (PubMed PMID: 23425857)
Philippi N, Walter D, Schlatter R et al (2009) Modeling system states in liver cells: survival, apop
tosis and their modifications in response to viral infection. BMC Syst Biol 3:97. https://doi.org/1
0.1186/1752-0509-3-97 (PubMed PMID: 19772631; PubMed Central PMCID: PMC2760522)
Schlatter R, Philippi N, Wangorsch G et al (2012) Integration of Boolean models exemplified on
hepatocyte signal transduction. Brief Bioinform 13(3):365–376. https://doi.org/10.1093/bib/
bbr065 (*Detailed overview of Boolean network models and how to model them comparatively)
Steiert B, Kreutz C, Raue A, Timmer J. Recipes for Analysis of Molecular Networks Using the
Data2Dynamics Modeling Environment. Methods Mol Biol.2019;1945:341–362. https://doi.
org/10.1007/978-1-4939-9102-0_16.
Stratmann AT, Fecher D, Wangorsch G et al (2014) Establishment of a human 3D lung cancer
model based on a biological tissue matrix combined with a Boolean in silico model. Mol Oncol
8(2):351–365. https://doi.org/10.1016/j.molonc.2013.11.009 (Epub2013Dec18)
Wangorsch G, Butt E, Mark R et al (2011) Time-resolved in silico modeling of fine-tuned cAMP
signaling in platelets: feedback loops, titrated phosphorylations and pharmacological modula
tion. BMC Syst Biol 5:178. https://doi.org/10.1186/1752-0509-5-178 (*Shows detailed model
ing with differential equations and time series analysis)
Application Examples
Baur F, Nietzer SL, Kunz M et al (2019) Connecting cancer pathways to tumor engines: a stratifica
tion tool for colorectal cancer combining human in vitro tissue models with Boolean in silico
models. Cancers (Basel) 12(1):28. https://doi.org/10.3390/cancers12010028
Baur F, Nietzer S, Kunz M et al (2020) Connecting cancer pathways to tumor engines: astratification
tool for colorectal cancer combining human in vitro tissue models with Boolean in silico models.
Cancers (Basel) 12(1), 28. pii: E1761. https://doi.org/10.3390/cancers12010028
Literature