Modeling dynamic functional relationship networks and application to ex vivo human erythroid differentiation
Fan Zhu1, Lihong Shi1, Hongdong Li1
1Department of Computational Medicine and Bioinformatics, Department of Cell and Developmental Biology, Department of Internal Medicine and Department of Computer Science and Engineering, University of Michigan, MI48109, USA.
Bioinformatics (Oxford, England)
|August 14, 2014
Summary
We developed a novel algorithm to model dynamic gene functional networks during cell differentiation. This approach identified key genes and novel drivers in human erythroid cell differentiation, outperforming static networks.
Area of Science:
- Systems Biology
- Genomics
- Computational Biology
Background:
- Functional relationship networks offer insights into gene co-functionality.
- Existing networks are static, failing to capture dynamic changes during cell differentiation.
- Cell lineages undergo dynamic reprogramming of functional relationships.
Purpose of the Study:
- To develop a novel algorithm for modeling dynamic gene functional networks.
- To apply this algorithm to human erythroid cell differentiation.
- To identify dynamic gene interactions and novel regulatory genes.
Main Methods:
- Leveraged differentiation stage-specific expression data.
- Integrated large-scale heterogeneous functional genomic data.
- Applied a novel algorithm to time-course RNA-Seq data from human erythroid differentiation.
Main Results:
- Developed dynamic gene functional networks that accurately predict gene connection changes during erythropoiesis.
- Identified known critical genes (HBD, GATA1) and functional connections.
- Discovered novel potential drivers of erythroid differentiation (OSBP2, PDZK1IP1) missed by static networks.
Conclusions:
- The novel dynamic network modeling approach accurately captures gene functional reprogramming during differentiation.
- This method enhances understanding of gene function dynamics during development.
- The approach is applicable to other differentiation processes with time-course expression data.
