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TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Parikh AP, Wu W, Curtis RE, Xing EP - Bioinformatics (2011)

Bottom Line: However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time.For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks.Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

ABSTRACT

Motivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.

Results: We propose a novel algorithm, Treegl, an ℓ(1) plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.

Availability: Software will be available at http://www.sailing.cs.cmu.edu/.

Contact: epxing@cs.cmu.edu.

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Related in: MedlinePlus

Overview of results for the identified networks. Note that the nodes on the circles are not actual genes but correspond to GO process groups. The thickness of a line between two GO groups A and B is proportional to how many genes in A interact with those in B.
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Figure 5: Overview of results for the identified networks. Note that the nodes on the circles are not actual genes but correspond to GO process groups. The thickness of a line between two GO groups A and B is proportional to how many genes in A interact with those in B.

Mentions: Figure 4 gives an overview of all the recovered networks using Cytoscape (Shannon et al., 2003). As one can see the networks exhibit many different topologies reflecting their underlying biological differences. To shed more light on these differences, Figure 5 shows the interactions among the second level GO groups in the recovered networks. The thickness of a link between two groups is proportional to the number of edges present between genes that are members of these GO groups. T4 cells display increased activities in cell proliferation and signaling, both indicative of their malignant state, compared to S1 cells. The T4R cells lie somewhere in between: MMP-T4R cells tend to have only a few interactions, since the network is quite sparse. While both the PI3K-MAPKK-T4R and EGFR-ITGB1 networks show reduced activities in growth and locomotion compared to S1 cells, the former network has more activities in cell proliferation and reduced signaling than the latter one. Taken together, these data suggest that although T4 cells can be morphologically reverted back to the normal-looking T4R cells, the underlying molecular mechanisms in the reverted cells are different from those in either S1 or T4 cells.Fig. 4.


TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Parikh AP, Wu W, Curtis RE, Xing EP - Bioinformatics (2011)

Overview of results for the identified networks. Note that the nodes on the circles are not actual genes but correspond to GO process groups. The thickness of a line between two GO groups A and B is proportional to how many genes in A interact with those in B.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117339&req=5

Figure 5: Overview of results for the identified networks. Note that the nodes on the circles are not actual genes but correspond to GO process groups. The thickness of a line between two GO groups A and B is proportional to how many genes in A interact with those in B.
Mentions: Figure 4 gives an overview of all the recovered networks using Cytoscape (Shannon et al., 2003). As one can see the networks exhibit many different topologies reflecting their underlying biological differences. To shed more light on these differences, Figure 5 shows the interactions among the second level GO groups in the recovered networks. The thickness of a link between two groups is proportional to the number of edges present between genes that are members of these GO groups. T4 cells display increased activities in cell proliferation and signaling, both indicative of their malignant state, compared to S1 cells. The T4R cells lie somewhere in between: MMP-T4R cells tend to have only a few interactions, since the network is quite sparse. While both the PI3K-MAPKK-T4R and EGFR-ITGB1 networks show reduced activities in growth and locomotion compared to S1 cells, the former network has more activities in cell proliferation and reduced signaling than the latter one. Taken together, these data suggest that although T4 cells can be morphologically reverted back to the normal-looking T4R cells, the underlying molecular mechanisms in the reverted cells are different from those in either S1 or T4 cells.Fig. 4.

Bottom Line: However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time.For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks.Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

ABSTRACT

Motivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.

Results: We propose a novel algorithm, Treegl, an ℓ(1) plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.

Availability: Software will be available at http://www.sailing.cs.cmu.edu/.

Contact: epxing@cs.cmu.edu.

Show MeSH
Related in: MedlinePlus