Large-Scale Signaling Network Reconstruction

Abstract

Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this paper, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfies the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose two methods which provide near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed methods on synthetic and real data sets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.

Construction of signaling networks with incomplete RNAi data

“…Computational methods have been developed to address this through the use of available data related to gene expression and protein-protein interaction networks [10]. Existing methods tend to utilize Bayesian networks, probabilistic Boolean networks, correlation based models, combinatorial optimization methods and differential equation models [11], [12], [13]. Among these, some utilize reference signaling networks to construct network topologies.…”

“…Among these, some utilize reference signaling networks to construct network topologies. These methods assume that a network topology that is similar to the one being constructed is already available [12], [13] . The methods which take advantage of such prior knowl edge tend to be computationally cheaper and more accurate than those which do not.…”

“…One recent computational method which uses reference net works to construct signal transduction networks from RNAi data is Signaling Network Constructor (SiNeC) [12]. In essence, SiNeC uses the known signaling network of one organism as a reference to construct a new signaling network of a different organism that adheres to the RNAi constraints of the new organism [12].…”

“…In essence, SiNeC uses the known signaling network of one organism as a reference to construct a new signaling network of a different organism that adheres to the RNAi constraints of the new organism [12]. One practical limitation of existing methods, such as SiNeC, is that they assume that RNAi data is complete, i.e., the RNAi constraints for all genes are known.…”

GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

“…In the work of Liu and Zhao [17], gene expression data was utilized to better delineate the pathway components of the S. cerevisiae MAPK signaling pathway found in protein-protein interaction data. In the work of Hashemikhabir et al [18], the problem of reconstructing a signaling pathway was framed as finding the minimum number of operations to modify a Reynolds' cultural algorithm framework. Elements in the belief space influence the next generation in the population space.…”