TY - GEN
T1 - Dynamic modeling of time-course microarray data by estimating hidden transcriptional activity profiles
AU - Tiemann, C.A.
AU - Moon, S.
AU - Stark, J.
AU - Starmans, M.H.W.
AU - Lambin, Ph.
AU - Hilbers, P.A.J.
AU - Riel, van, N.A.W.
PY - 2008
Y1 - 2008
N2 - ObjectiveIt is a challenge to reveal and understand gene networks. Microarrays are a great utility for this, as with microarrays it is possible to monitor gene expression for thousands of genes simultaneously. This presents new challenges for the development of methods for the analysis of tremendous amounts of data. Last decade, many different methods have been proposed. However, often they are limited and inaccurate. The reason for this is that these methods neglect important biological factors, like transcript degradation rates, sensitivity of genes to transcription factors and the activity of transcription factors itself. In this work, a method has been developed, using a different approach. A dynamic ordinary differential equation model has been used, which takes account for biological factors. Model parameters have been estimated using time-course microarray data.ResultsThe developed method takes the data of an arbitrary number of microarray experiments as input and estimates the underlying transcriptional activity profile of each gene and produces a ranked list of genes that behave most consistent in the different experiments. The method is based on a mathematical model of gene transcription. First, to test the method, artificial data has been generated, based on a known activity profile. Subsequently, to further test the method, three microarray time course experiments in which hypoxia has been induced in three different cancer cell lines, has been used.ConclusionsThe method is able to reproduce artificial activity profiles correctly. Furthermore, the estimated activity profiles of the hypoxia dataset look biologically convincing. The produced ranked list of genes looks promising as a lot of the top genes have been related to cancer or hypoxia in other researches. With this method it is possible to include biological factors and it is easy adaptable to specific cases. Furthermore, it is easy to include new obtained microarray data.
AB - ObjectiveIt is a challenge to reveal and understand gene networks. Microarrays are a great utility for this, as with microarrays it is possible to monitor gene expression for thousands of genes simultaneously. This presents new challenges for the development of methods for the analysis of tremendous amounts of data. Last decade, many different methods have been proposed. However, often they are limited and inaccurate. The reason for this is that these methods neglect important biological factors, like transcript degradation rates, sensitivity of genes to transcription factors and the activity of transcription factors itself. In this work, a method has been developed, using a different approach. A dynamic ordinary differential equation model has been used, which takes account for biological factors. Model parameters have been estimated using time-course microarray data.ResultsThe developed method takes the data of an arbitrary number of microarray experiments as input and estimates the underlying transcriptional activity profile of each gene and produces a ranked list of genes that behave most consistent in the different experiments. The method is based on a mathematical model of gene transcription. First, to test the method, artificial data has been generated, based on a known activity profile. Subsequently, to further test the method, three microarray time course experiments in which hypoxia has been induced in three different cancer cell lines, has been used.ConclusionsThe method is able to reproduce artificial activity profiles correctly. Furthermore, the estimated activity profiles of the hypoxia dataset look biologically convincing. The produced ranked list of genes looks promising as a lot of the top genes have been related to cancer or hypoxia in other researches. With this method it is possible to include biological factors and it is easy adaptable to specific cases. Furthermore, it is easy to include new obtained microarray data.
M3 - Conference contribution
SP - pp.-102
BT - International Conference on Systems Biology, Sweden, Goteborg
ER -
