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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

Breast cancer genealogy. Solid arrows correspond to the genealogy. The dotted lines correspond to extra penalties between the T4R and S1 cells.
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Figure 3: Breast cancer genealogy. Solid arrows correspond to the genealogy. The dotted lines correspond to extra penalties between the T4R and S1 cells.

Mentions: Our goal is to investigate the gene regulatory networks of normal breast cells (S1 cells), malignant breast cancer cells (T4 cells), and nontumorigenic breast cancer cells reverted by different drugs (T4R cells). The exact tree-genealogy underlying these cell-type specific networks is shown in Figure 3: S1 cells with polarized acinar structures evolve into tumorigenic T4 cells which form disorganized apolar colonies, and then three drugs are applied individually to T4 cells and different reverted cells (T4R) with organized structures which resemble S1 cells are produced.Fig. 3.


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

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

Breast cancer genealogy. Solid arrows correspond to the genealogy. The dotted lines correspond to extra penalties between the T4R and S1 cells.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: Breast cancer genealogy. Solid arrows correspond to the genealogy. The dotted lines correspond to extra penalties between the T4R and S1 cells.
Mentions: Our goal is to investigate the gene regulatory networks of normal breast cells (S1 cells), malignant breast cancer cells (T4 cells), and nontumorigenic breast cancer cells reverted by different drugs (T4R cells). The exact tree-genealogy underlying these cell-type specific networks is shown in Figure 3: S1 cells with polarized acinar structures evolve into tumorigenic T4 cells which form disorganized apolar colonies, and then three drugs are applied individually to T4 cells and different reverted cells (T4R) with organized structures which resemble S1 cells are produced.Fig. 3.

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