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Molecular Mining to Build Gene Network Model in Discovery Alliance with GeneTrace


Molecular Mining and GeneTrace Systems will collaborate to build a regulatory network model of human gene interactions, a move seen as a step toward integrated target discovery.

Under the terms of the agreement, the two companies will co-fund research and will share revenues generated by any findings that arise during the collaboration.

Molecular Mining will use its data mining and modeling technology to create network models based on experimental data generated by GeneTrace’s high-throughput systems biology platform.

“GeneTrace has excellent quantitative expression measurement technologies,” said Roland Somogyi, Molecular Mining’s CSO. “It was an obvious thing for us to be working together because we all want to predict complex outcomes and gene networks. We’re all believers in the commercial value of systems biology and in silico biology.”

Somogyi expects to make predictions about gene function using GeneTrace’s data, with the ultimate goal being a reliable dynamic gene network model. The marriage of Molecular Mining’s predictive ability and GeneTrace’s data and high-volume wet lab validation technology is “the first large, systematic gene network reverse engineering effort” of its kind, according to Somogyi.

Molecular Mining will build on its existing platform of data mining technologies as the collaboration progresses. “With our current methods, we’ll be able to discover which combinations of genes predict and determine particular outcomes,” Somogyi said. “We have proprietary algorithms that can search these high-dimensional spaces efficiently.”

The discovery alliance is a relatively new aspect of Molecular Mining’s business model, and the company intends to continue collaborating with multiple partners in different areas, Somogyi said.

Enthusiasm is particularly high for this project, which he considers to be a significant step toward understanding the regulatory code that underlies cell function. “We are now ready to challenge the complexity of living systems,” he said.

“I think there’s a lot of biomedically relevant connections and pathway details out there that haven’t been discovered yet. We need an objective method to do so, and I think that’s what we’re focused on,” he said.


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