Untangling the wires: A strategy to trace functional interactions in signaling and gene networks

Abstract

Emerging technologies have enabled the acquisition of large genomics and proteomics data sets. However, current methodologies for analysis do not permit interpretation of the data in ways that unravel cellular networking. We propose a quantitative method for determining functional interactions in cellular signaling and gene networks. It can be used to explore cell systems at a mechanistic level or applied within a ''modular'' framework, which dramatically decreases the number of variables to be assayed. This method is based on a mathematical derivation that demonstrates how the topology and strength of network connections can be retrieved from experimentally measured network responses to successive perturbations of all modules. Importantly, our analysis can reveal functional interactions even when the components of the system are not all known. Under these circumstances, some connections retrieved by the analysis will not be direct but correspond to the interaction routes through unidentified elements. The method is tested and illustrated by using computer-generated responses of a modeled mitogen-activated protein kinase cascade and gene network.

Sensitivity analysis approaches applied to systems biology models

“…Global coefficients are usually calculated from a given steady state. In contrast, the local coefficients stand for the individual functional entity that is 'isolated' from the system [32,33]. The meanings of 'local' and 'global' coefficients defined here are with respect to the system, rather than the parameter space.…”

Quantitative microscopy and systems biology: seeing the whole picture

“…To study the structure of the regulatory and signal transduction networks that govern function in intact living cells, the advanced Xuorescence imaging methods mentioned above need to be translated to a high-throughput setting. This will make it possible to elucidate the logical topology (network structure) of large biochemical networks by imaging their response to chemical or genetic perturbations (Kholodenko et al 2002;Santos et al 2007).…”

Discriminating direct and indirect connectivities in biological networks

“…reverse engineering | synthetic biology | direct and indirect connectivities | human cells | nonparametric resampling A focal point of systems biology is the reverse engineering of gene regulatory networks (1)(2)(3)(4)(5). The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6)(7)(8)(9)(10).…”

“…Importantly, remedies to address this problem should not further muddle the interpretation by removing true network edges (14). A number of theoretical approaches have been proposed to overcome this hurdle (4,(15)(16)(17)(18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.…”