Inference of interampatte gene regulatory networks : with application to apoptosis signalling

• Objective: Inference of gene regulatory networks (GRN) from quantitative expression data has the potential to reveal all interactions existing within a selected set of genes. However, microarray data typically only contain a few characteristic modes or eigengenes, even when a large number of arrays are recorded at varying experimental conditions. The reason and implications of this inherent rank deficiency has largely been neglected, even though rank deficiency caused by fewer experiments than measured genes has been addressed. We explain why the data in the former case are rank deficient, what it implies for network inference, and how to counteract it through experiment design. Results: We define interampatte systems as systems characterised by strong interactions necessary to both amplify and attenuate different signals at multiple time-scales. GRN are interampatte with strong directional dependence. This generic network property make microarray data rank deficient and gives rise to features observed as characteristic modes, eigengenes and co-expressed genes. While few modes imply that low order models can be used for data compression and prediction, it effectively prevents inference of causal interactions, since many sparse networks with completely different structure fit equally well to the dataset. We illustrate this problem using a previously published model of apoptosis signalling. Inference based on standard experiments, i.e. perturbing genes one-by-one, is shown to yield networks with the wrong structure although its predictive ability is validated using independent validation data. We present an iterative algorithm for experiment design that guarantees sufficient excitation of all network modes and demonstrate its effectiveness. Conclusions: Systematic design of perturbation experiments, where several genes are perturbed simultaneously in a controlled fashion, is necessary in order to infer the true structure of GRN from expression data. It is likely that many inferred network models with validated predictive properties have falsely identified gene interactions.

Subject headings

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Apoptosis

Gene regulatory networks

Network inference

Publication and Content Type

vet (subject category)

kon (subject category)