Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State-Space Models
A major challenge in systems biology is the ability to model complex regulatory interactions. This chapter is concerned with the use of Linear- Gaussian state-space models (SSMs), also known as linear dynamical systems (LDS) or Kalman filter models, to “reverse engineer” regulatory networks from high-throughput data sources, such as microarray gene expression profiling.
LDS models are a subclass of dynamic Bayesian networks used for modeling time series data and have been used extensively in many areas of control and signal processing. We describe results from simulation studies based on synthetic mRNA data generated from a model that contains definite nonlinearities in the dynamics of the hidden factors (arising from the oligomerization of transcription factors). Receiver operating characteristic (ROC) analysis demonstrates an overall accuracy in transcriptional network reconstruction from the mRNA time series measurements alone of approximately a 68% area under the curve (AUC) for 12 time points, and better still for data sampled at a higher rate.
A key ingredient of these models is the inclusion of “hidden factors” that help to explain the correlation structure of the observed measurements. These factors may correspond to unmeasured quantities that were not captured during the experiment and may represent underlying biological processes. Results from the modeling of the synthetic data also indicate that our method is capable of capturing the temporal nature of the data and of explaining it using these hidden processes, some of which may plausibly reflect dynamic aspects of the underlying biological reality.
Key WordsTranscriptional networks microarrays state-space models variational Bayesian reverse engineering
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