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Time Series Dependent Analysis of Unparametrized Thomas Networks
Sept.-Oct. 2012 (vol. 9 no. 5)
pp. 1338-1351
Hannes Klarner, DFG Res. Center Matheon, Freie Univ. Berlin, Berlin, Germany
Heike Siebert, DFG Res. Center Matheon, Freie Univ. Berlin, Berlin, Germany
Alexander Bockmayr, DFG Res. Center Matheon, Freie Univ. Berlin, Berlin, Germany
This paper is concerned with the analysis of labeled Thomas networks using discrete time series. It focuses on refining the given edge labels and on assessing the data quality. The results are aimed at being exploitable for experimental design and include the prediction of new activatory or inhibitory effects of given interactions and yet unobserved oscillations of specific components in between specific sampling intervals. On the formal side, we generalize the concept of edge labels and introduce a discrete time series interpretation. This interpretation features two original concepts: 1) Incomplete measurements are admissible, and 2) it allows qualitative assumptions about the changes in gene expression by means of monotonicity. On the computational side, we provide a Python script, erda.py, that automates the suggested workflow by model checking and constraint satisfaction. We illustrate the workflow by investigating the yeast network IRMA.
Index Terms:
time series,biology computing,cellular biophysics,genetics,genomics,microorganisms,oscillations,sampling methods,yeast network IRMA,time series dependent analysis,unparametrized Thomas networks,data quality,inhibitory effects,activatory effects,oscillations,sampling intervals,discrete time series interpretation,gene expression,monotonicity,Python script,model checking,Time series analysis,Regulators,Computational modeling,Time measurement,Bioinformatics,Computational biology,Labeling,constraint satisfaction.,Time series analysis,model checking,temporal logic,biology and genetics
Citation:
Hannes Klarner, Heike Siebert, Alexander Bockmayr, "Time Series Dependent Analysis of Unparametrized Thomas Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 5, pp. 1338-1351, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.61
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