MiS Preprint Repository

We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV ( that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint

Gene network inference from incomplete expression data: transcriptional control of hemopoietic commitment

Kristin Missal, Michael A. Cross and Dirk Drasdo


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.

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


Related publications

2006 Repository Open Access
Kristin Missal, Michael A. Cross and Dirk Drasdo

Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment

In: Bioinformatics, 22 (2006) 6, pp. 731-738