MiS Preprint Repository

Motivation:
The identification of the topology and function of gene regulation networks remains a challenge. A frequently used strategy is to reconstruct gene regulatory networks from time series of gene expression levels from data pooled from cell populations.

However, this strategy causes problems if the gene expression in different cells of the population is not synchronous, as is expected to be the case in the transcription factor network that controls lineage commitment in haematopoietic stem cells. Here, a promising alternative may be to measure the gene expression levels in single cells individually. The inference of a network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, due to experimental limitations a complete determination of the precursor state is not possible.

Results:
We investigate a strategy for the inference of gene regulatory networks from incomplete expression data based on dynamic Bayesian networks that permits prediction of the number of experiments necessary for network inference depending on noise in the data, prior knowledge, limited attainability of initial states and other inference parameters. Our inference strategy combines a gradual ''Partial Learning'' strategy only based on true experimental observations for the network topology with expectation maximization for the network parameters. We illustrate our strategy by extensive computer simulations in a high-dimensional parameter space on the network inference in simulated single-cell-based experiments during haematopoietic stem cell commitment. We find for example that the feasibility of network inferences increases significantly with the experimental ability to force the system into