Inference of gene regulatory networks from time series data

Reverse engineering of gene regulatory relationships from genomics data
is a crucial task to dissect the complex underlying regulatory mechanism
occurring in a cell. From a computational point of view the
reconstruction of gene regulatory networks is an undetermined problem as
the large number of possible solutions is typically high in contrast to
the number of available independent data points. Many possible solutions
can fit the available data, explaining the data equally well, but only
one of them can be the biologically true solution. Several strategies
have been proposed in literature to reduce the search space and/or
extend the amount of independent information. Moreover, several methods
have been developed to infer gene networks from steady-state data, much
less literature is produced about time-course data, so the development
of algorithms to infer gene networks from time-series measurements is a
current challenge into bioinformatics research area.

Here I will present two recently proposed algorithms, the first termed
TimeDelay-ARACNE tries to extract dependencies between pairs of genes
at different time delays, providing a measure of these dependencies in
terms of mutual information. The basic idea of the proposed algorithm is
to detect time-delayed dependencies between the expression profiles by
assuming as underlying probabilistic model a stationary Markov Random
Field. Less informative dependencies are filtered out using an
auto--calculated threshold, retaining most reliable connections.
TimeDelay-ARACNE can infer small local networks of time regulated
gene-gene interactions detecting their versus and also discovering
cyclic interactions also when only a medium-small number of measurements
are available.

The second method is based on formal methods, mathematically rigorous
techniques widely adopted in engineering to specify and verify complex
software and hardware systems. Starting with a formal specification of
gene regulatory hypotheses we are able to mathematically prove whether a
time course experiment belongs or not to the formal specification,
determining in fact whether a gene regulation exists or not. The method
is able to detect both direction and sign (inhibition/activation) of
regulations whereas most of literature methods are limited to undirected
and/or unsigned relationships.

The validation of these algorithms to in different biological context,
including detection of master regulators of cancer profiles will be also
reported.