New Informatics Approach Combines Metabolic, Regulatory Networks to Elucidate Cells' Activities
A new paper written by researchers from the Institute for Systems Biology describes a computational approach for studying regulatory activities in cells that relies on integrated networks of transcriptional and metabolic data.
The study, published in PLOS Computational Biology earlier this month, describes software called the Gene Expression and Metabolism Integrated for Network Inference (GEMINI) which uses an integrated model of network and metabolic data to explore growth phenotypes in Saccharomyces cerevisiae .
GEMINI builds on work from the same researchers published in 2010 in the Proceedings of the National Academy of Sciences. That paper describes the Probabilistic Regulation of Metabolism (PROM), which provides a mechanism for integrating transcriptional regulatory networks and metabolic networks in a single in silico model and using it to make predictions about phenotypes such as flux and growth rate.
While based on PROM, GEMINI is designed to tackle a slightly different question, as the PLOS Comp. Bio. paper explains. While PROM "solves the forward problem of combining disparate networks to predict phenotype" with GEMINI "we iteratively use PROM to aid in solving the more challenging inverse problem — guiding TRN structure prediction using the metabolic network and the emergent phenotype measurements," the researchers wrote. "In doing so, our new method serves as a tool to refine the inferred TRN and improve the predictive power of the integrated network models."
Nathan Price, ISB's associate director and co-author on both papers, explained that while PROM uses the integrated network to try to predict what happens when transcription factors are deleted, GEMINI says "we don't know what the gene regulatory network is perfectly so we are going to use the fact that we can link these two together to now look at where we make wrong predictions,"
"GEMINI says, [for example], 'I have a prediction that this transcription factor influences a gene that happens to code for a metabolic enzyme,'" Price said. "Because we can link these things together and make growth predictions, we can say, 'When we knock out that transcription factor in yeast, does it have the decrease in growth rate that we predict because of this regulatory interaction.'"
The developers claim that theirs is the first approach that integrates regulatory and metabolic data in this way and use it to study cell's activities. They write in PLOS Comp. Bio that it improves on previous strategies that have used primarily "proximal data such as gene co-expression and transcription factor binding" to reconstruct and study TRNs. While these methods, they said, can be used to quickly reconstruct TRNs, "the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions."
Their findings, the researchers conclude, "suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types."
Metabolic networks are one of the better understood cellular systems, according to Price, making it the ideal starting point for studying at least those regulatory activities in which cell metabolism plays a role, such as growth rates.
"It's hard to just look at a transcriptome and say [for example] if I knock out a transcription factor, that is going to lead to the death of this tuberculosis cell," he said. "But as soon as you tie it on to metabolism, there are very … clear rules about what … leads to cell death in metabolism in a way that you don't see directly in gene regulatory networks."
For their next steps, Price and his colleague and co-author, Sriram Chandrasekaran, are trying to use GEMINI to study networks in cells other than yeast, such as human cell lines. He also said that they're exploring ways to integrate it with network inference algorithms with an eye toward creating a "model-guided platform for synthetic biology." A third potential application would be to use GEMINI to study regulatory-metabolic interactions associated with disease-specific cancers, as well as metabolic and neurodegenerative diseases, he said.
The study, published in PLOS Computational Biology earlier this month, describes software called the Gene Expression and Metabolism Integrated for Network Inference (GEMINI) which uses an integrated model of network and metabolic data to explore growth phenotypes in Saccharomyces cerevisiae .
GEMINI builds on work from the same researchers published in 2010 in the Proceedings of the National Academy of Sciences. That paper describes the Probabilistic Regulation of Metabolism (PROM), which provides a mechanism for integrating transcriptional regulatory networks and metabolic networks in a single in silico model and using it to make predictions about phenotypes such as flux and growth rate.
While based on PROM, GEMINI is designed to tackle a slightly different question, as the PLOS Comp. Bio. paper explains. While PROM "solves the forward problem of combining disparate networks to predict phenotype" with GEMINI "we iteratively use PROM to aid in solving the more challenging inverse problem — guiding TRN structure prediction using the metabolic network and the emergent phenotype measurements," the researchers wrote. "In doing so, our new method serves as a tool to refine the inferred TRN and improve the predictive power of the integrated network models."
Nathan Price, ISB's associate director and co-author on both papers, explained that while PROM uses the integrated network to try to predict what happens when transcription factors are deleted, GEMINI says "we don't know what the gene regulatory network is perfectly so we are going to use the fact that we can link these two together to now look at where we make wrong predictions,"
"GEMINI says, [for example], 'I have a prediction that this transcription factor influences a gene that happens to code for a metabolic enzyme,'" Price said. "Because we can link these things together and make growth predictions, we can say, 'When we knock out that transcription factor in yeast, does it have the decrease in growth rate that we predict because of this regulatory interaction.'"
The developers claim that theirs is the first approach that integrates regulatory and metabolic data in this way and use it to study cell's activities. They write in PLOS Comp. Bio that it improves on previous strategies that have used primarily "proximal data such as gene co-expression and transcription factor binding" to reconstruct and study TRNs. While these methods, they said, can be used to quickly reconstruct TRNs, "the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions."
Their findings, the researchers conclude, "suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types."
Metabolic networks are one of the better understood cellular systems, according to Price, making it the ideal starting point for studying at least those regulatory activities in which cell metabolism plays a role, such as growth rates.
"It's hard to just look at a transcriptome and say [for example] if I knock out a transcription factor, that is going to lead to the death of this tuberculosis cell," he said. "But as soon as you tie it on to metabolism, there are very … clear rules about what … leads to cell death in metabolism in a way that you don't see directly in gene regulatory networks."
For their next steps, Price and his colleague and co-author, Sriram Chandrasekaran, are trying to use GEMINI to study networks in cells other than yeast, such as human cell lines. He also said that they're exploring ways to integrate it with network inference algorithms with an eye toward creating a "model-guided platform for synthetic biology." A third potential application would be to use GEMINI to study regulatory-metabolic interactions associated with disease-specific cancers, as well as metabolic and neurodegenerative diseases, he said.
Comments