Dataset Information

Disentangling function from topology to infer the network properties of disease genes.

ABSTRACT:

Background

The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.

Results

We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function.

Conclusions

Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes.

SUBMITTER: Ghersi D

PROVIDER: S-EPMC3614482 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disentangling function from topology to infer the network properties of disease genes.

Ghersi Dario D Singh Mona M

BMC systems biology 20130116

<h4>Background</h4>The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.<h4>Results</h4>We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of a ...[more]

PMID: 23324116

Similar Datasets

Project description:BackgroundRecent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations.ResultsWe propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells.ConclusionsThe proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.

Project description:Biological materials combine stress relaxation and self-healing with non-linear stress-strain responses. These characteristic features are a direct result of hierarchical self-assembly, which often results in fiber-like architectures. Even though structural knowledge is rapidly increasing, it has remained a challenge to establish relationships between microscopic and macroscopic structure and function. Here, we focus on understanding how network topology determines the viscoelastic properties, i.e., stress relaxation, of biomimetic hydrogels. We have dynamically crosslinked two different synthetic polymers with one and the same crosslink. The first polymer, a polyisocyanopeptide (PIC), self-assembles into semi-flexible, fiber-like bundles, and thus displays stress-stiffening, similar to many biopolymer networks. The second polymer, 4-arm poly(ethylene glycol) (starPEG), serves as a reference network with well-characterized structural and viscoelastic properties. Using one and the same coiled coil crosslink allows us to decouple the effects of crosslink kinetics and network topology on the stress relaxation behavior of the resulting hydrogel networks. We show that the fiber-containing PIC network displays a relaxation time approximately two orders of magnitude slower than the starPEG network. This reveals that crosslink kinetics is not the only determinant for stress relaxation. Instead, we propose that the different network topologies determine the ability of elastically active network chains to relax stress. In the starPEG network, each elastically active chain contains exactly one crosslink. In the absence of entanglements, crosslink dissociation thus relaxes the entire chain. In contrast, each polymer is crosslinked to the fiber bundle in multiple positions in the PIC hydrogel. The dissociation of a single crosslink is thus not sufficient for chain relaxation. This suggests that tuning the number of crosslinks per elastically active chain in combination with crosslink kinetics is a powerful design principle for tuning stress relaxation in polymeric materials. The presence of a higher number of crosslinks per elastically active chain thus yields materials with a slow macroscopic relaxation time but fast dynamics at the microscopic level. Using this principle for the design of synthetic cell culture matrices will yield materials with excellent long-term stability combined with the ability to locally reorganize, thus facilitating cell motility, spreading, and growth.

Dataset Information

Disentangling function from topology to infer the network properties of disease genes.

Background

Results

Conclusions

Publications

Disentangling function from topology to infer the network properties of disease genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets