A computational algebra approach to the reverse engineering of gene regulatory networks.

By Reinhard C. Laubenbacher, Brandilyn Stigler. Journal of Theoretical Biology, 2003

308 citations recorded by the source corpus.

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. An analysis of the complexity of the algorithm is performed. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b39e1e20a15de3f4b1a3799e99247ad33fee3f2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Multiplexed protein measurement: technologies and applications of protein and antibody arrays (2006, background): “Furthermore, it is now recognized that single nucleic-acid measurements can lead to spurious conclusions in, for example, studies of transcriptional regulation or heritability of complex trait”
Linear Finite Dynamical Systems (2005, methodology): “Recently, Laubenbacher and Stigler have used FDS models for solving the reverse engineering problem for genetic networks ( Laubenbacher and Stigler, 2004 ).”
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction (2009, methodology): “The use of prior knowledge distinguishes our work from standard reverse engineering (Sachs et al, 2005; Bonneau et al, 2006; Perkins et al, 2006; Bansal et al, 2007; Cho et al, 2007; Nelander et al, 2008), including reverse engineering using Boolean logic (D’haeseleer et al, 2000; Laubenbacher and S”