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Genomatica Signs Dow as First Commercial Client for In Silico Platform


In a time when early-stage funds are nearly non-existent and deep-pocketed investors are on the sidelines, hands over their wallets, Genomatica of San Diego is executing on a business plan that goes beyond penny-pinching bootstrap models.

Last week, the 12-employee systems biology startup that began operations in June 2000 announced a research agreement with Dow Chemical, its first commercial project. Financial terms were not disclosed.

Christophe Schilling, co-founder and chief scientist for the company, said the deal is encouraging.

“I don’t think there is one time that is any better than another to begin a business of this type,” he told BioInform last week. “If you have a solid technology and a sound business model, you can make that work in any business environment — if you execute on the plan.”

The company is seeking to expand its genome-scale models of microbial functions to describe the behavior of human cells. Genomatica’s constraints-based modeling systems are used to predict the effect of genetic changes on microorganisms before running experiments.

For Dow, Genomatica will develop an in silico metabolic model, based on its SimPheny platform, for use in microbial metabolism and bioprocess modeling.

The company’s core technology is exclusively licensed from the University of California, San Diego, where it was developed by Genomatica co-founder Bernhard Palsson, a UCSD professor of bioengineering. Genomatica also holds a license to patent-pending technology developed at Penn State. The company also works closely with academic groups from the University of Delaware and the University of Massachusetts.

This well-protected license portfolio is a key component of the company’s strategy. Schilling said the company is “pretty vigorous” about securing the appropriate intellectual property rights for its technology. “It’s the strongest barrier to entry,” he noted.

The company has applied its constraints-based modeling technology to over half a dozen organisms, Schilling said, with the most recent model, that of Helicobacter pylori, published in the August 2002 Journal of Bacteriology.

The company is not alone in the field of computation-based biological modeling; it competes with Physiome Sciences, Entelos, and Gene Network Sciences. However, said Schilling, “We think of them as collaborators.” Genomatica “was originally founded on technology validated primarily in the microbial space and that’s not the case with [these competitors],” he noted.

The deal with Dow aligns Genomatica with a firm that shares its worldview, Schilling said.

“Modeling is well established in other fields but not in biology. The crowd in the chemical industry and bioprocessing has a good feel for the technology and understands the impact on the processes, so I think Dow is a good fit for us,” he said.

However, he noted, the company is not limiting itself to the bioprocess industry:“Pharmaceutical companies have large groups like that within and we would like to work with them, too.”

The company has secured SBIR grants from NSF and NIH in addition to two grants totaling over $2 million in funding over the next three years from the Department of Energy’s Genomes to Life project [BioInform 01-07-02].

Capitalizing on “the nice match between our focus and federal government research objectives,” the company raised just over $750,000 in grant revenues last year and expects nearly $1 million this year, Schilling said.

While Genomatica is financially at break-even and projects profitability with the next commercial deal that comes through, the company is also seeking to raise venture-capital investment, Schilling said.

“While the market we are in is not the best for that, we feel we are in a [financial] position that we are comfortable with. We would like to get more capital into the company to fund some ideas that we have going.”

Schilling said the market opportunity for systems modeling is potentially a large one but the companies in this space have to solve technology issues.

“We have to be able to understand what the technology can deliver and demonstrate its power,” he said. “We think it can drive efficiency in a number of areas of biotechnology, and research and development.”


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