June 17, 2008 — Viewed in the abstract, the human body is a marvelous creation — gracefully proportioned, supple and strong. Considered at the cellular level, things take on a completely different aspect. The human cell is a hive of autonomous activity, with thousands of electrical and chemical events occurring in complex patterns to produce the full range of metabolic, regulatory and signaling functions necessary to keep us alive. There's a Rube Goldberg quality to this clockwork, built with chunks of genetic material, cobbled together and repurposed by evolution over billions of years.

The challenge that systems biologists like Jason Papin face is to make sense of this sprawling cellular enterprise, to trace each chain of molecular events to their functional consequence, and to devise the mathematical equations that ultimately yield a digital representation of the human cell.

"The potential of the digital cell is enormous," said Papin, an assistant professor in the Department of Biomedical Engineering at U.Va.'s Engineering School. "Using a digital model, you could knock out a specific gene or change the structure of a protein and learn how it affects the function of the cell. This information can help researchers home in on more effective therapies."

A great deal of fundamental work must be done before this promise can be realized. Researchers are developing prototype models using simple organisms like the Escherichia coli bacterium and focusing on just a set of events, like those associated with metabolism.

Even here, the task is formidable. As a start, they must assemble all the known facts about the cellular processes they want to model. This means collecting all the relevant observations from the scientific literature and combing the vast databases of information generated by high-throughput studies of gene expression. Only then can they begin to develop their computational models, and once they do, the facts they are missing stand out in high relief. 

Given this circumstance, it makes sense for Papin to join forces with scientists who are producing the information he needs. With Marc Vidal and Kourosh Salehi-Ashtiani at the Dana-Farber Cancer Institute at Harvard, he is part of a project funded by the Department of Energy to build a digital model of the metabolic system of Chlamydomonas reinhardtii, a species of algae that is commonly used in biology to study processes like photosynthesis and cell migration.

"As part of its metabolism, this algae can produce hydrogen," noted Papin. "If it could be scaled up, it would be a highly efficient source of renewable energy."

Vidal is a world-class expert in developing and applying large-scale experimental techniques to determine where individual genes begin and end and to identify their function, an activity called genome annotation.

"This information is the raw material for our models," Papin says. "As we use this information to build a metabolic model for Chlamydomonas reinhardtii, the gaps we observe will help our partners focus on areas of the genome that are poorly characterized." 

Once the genes related to metabolic function are all identified, Papin can then manipulate the model, identifying ways to alter gene expression to maximize the production of hydrogen. Currently, Papin and his colleagues are working on a paper detailing 250 reactions, a quarter of the 1,000 reactions they hope to include in a metabolic model of this simple organism.

"Obviously, the number of reactions we would have to account for increases if we were modeling a human cell," he says, "but there are essential similarities in metabolism from one organism to the next that are important to understand."