Invalidating models of gene regulatory networks: the implications of characteristic and weak modes for network inference
2009 (English)In: Proceedings of Conference on Engineering Principles in Biological Systems: Hinxton, UK, Engineering Principles in Biological systems conference, Hinxton (UK) , 2009Conference paper, Poster (Refereed)
Analysis of published gene expression data sets reveal that the variation in expression is concentrated to significantly fewer ‘characteristic modes’ or ‘eigengenes’ than both the number of recorded assays and the number of measured genes. In other words, the responses obtained in standard experiments are typically concentrated to a subset of the gene space. This is an advantage when considering modelling for predicting gene responses to external perturbations, since the model only needs to capture the characteristic modes correctly for this purpose. However, it seriously hampers network inference, since it implies that models with widely different network structure are practically indistinguishable based on standard response data. Furthermore, as we show here, the presence of characteristics modes implies that it is easy to validate and hard to invalidate false model structures. The information required to invalidate a false model is hidden in the weak modes that contribute only weakly to the gene response data and therefore are largely hidden in the measurement noise. Here we use two published gene expression data sets and an in silico gene regulatory network to illustrate the principal differences between validation and invalidation of models of gene regulatory networks. All three systems have a high degree of interampatteness (see ref.), i.e. some perturbations are amplified while others are attenuated by the system. The response of an interampatte system to random perturbations can be desccribed well based on the characteristic modes only, implying that it is easy to validate any model that predicts the characteristic modes correctly. To invalidate a model we need to design specific perturbations that yield a sufficiently strong signal also for perturbations that are attenuated by the system, i.e., that excites the weak modes of the network. From a biological perspective it is trivial to realize that amplification and attenuation of perturbations are equally important for biological function, and hence a proper model should be able to predict both the weak and characteristic modes correctly. We stress that the common assumption that the quality of a model can be judged based on its ability to predict response data only holds for systems with a low degree of interampatteness. Nordling TEM, Jacobsen EW Interampatteness–a generic property of biochemical networks. IET Systems Biology, 2009, in press.
Place, publisher, year, edition, pages
Engineering Principles in Biological systems conference, Hinxton (UK) , 2009.
Experiment design, System identification, Systems Biology
Bioinformatics and Systems Biology Control Engineering
IdentifiersURN: urn:nbn:se:kth:diva-80755OAI: oai:DiVA.org:kth-80755DiVA: diva2:496740
Conference on Engineering Principles in Biological Systems
QC 201306042012-02-102012-02-102013-06-04Bibliographically approved