Computer Modeling Reveals Hidden Conversations Within Cells

Prior to the UCSD team’sfindings, which are published in the September 16 issue of the journalScience, many scientists expressed doubts that a computational approachcould represent the intricate mechanisms through which cells respond tooutside signals. However, the researchers report that their computermodel accurately predicts particular behaviors of living cells. Theyalso believe that the model has important practical applications,including guiding the design of better treatments for cancer and otherdiseases that involve failures in cell communication

“Ourcomputational approach revealed how the same set of proteins producephysiologically different outputs in response to only subtly differentinputs,” explained Alexander Hoffmann, an assistant professor ofchemistry and biochemistry, who led the team. “This is the first steptoward developing drugs that interfere with one of the pathologicalfunctions of the proteins, but leave the healthy functions intact. Forexample, many current cancer drugs dramatically reduce immune function.Computer modeling should make it possible to design anti-cancer drugsthat do not weaken patients’ immune systems.”

The computer modelcomprises 70 equations to account for the behavior of five proteins andthree RNA molecules in the “NF-kappaB signaling pathway,” whichregulates genes involved in cancer, inflammation, immune function andcell death. Each equation takes into account a different parameter,such as how quickly a protein is synthesized, or how quickly it isdegraded.

The researchers chose the NF-kappaB proteins becausethere is a wide body of prior research that they were able to draw onto set the initial parameters in the model. As they were developing themodel, they repeatedly tested and refined it by comparing the model’spredictions with the results of experiments with living cells.

“Thebeauty of this kind of interdisciplinary work is the almost circularway the model’s predictions drive the design of new experiments, andthe how results of those experiments can be fed back into the model toimprove it,” said Shannon Werner, a graduate student in chemistry andbiochemistry, who did the experimental work described in the paper.

Oncethe model consistently predicted the behavior of living cells in avariety of experimental conditions, the researchers used the model toinfer what was going on inside cells in much greater detail than wouldbe possible through laboratory experiments alone.

The modelrevealed why two natural chemicals have opposite physiological effects.When exposed to one of the chemicals, the proteins create positivefeedback that lengthens the amount of time they are active. Whenexposed to the other chemical, they initiate negative feedback, whichshuts them down rapidly.

“The prevailing view has been thatproteins are either on or off like a light switch, but that didn’texplain how activating the same proteins with different chemicals couldhave opposing effects on cells,” explained Hoffmann. “Our model showsthat, analogous to how a telephone transmits an infinite number ofdifferent signals along a single wire, it is the timing of theproteins’ activity that allows them to exert intricate control over thebehavior of a cell. The computer model reveals the hidden conversationsin the cell’s wiring.”

The researchers attribute their success indeveloping the computer model, despite criticism that the computationalapproach would require too many simplifications to accurately modelcell communication, to the diverse expertise they brought together.

“Developinga computer model is both science and art,” said Derren Barken, agraduate student in bioinformatics and experienced software engineer,who programmed the model. “It requires intuition built up over time,but it also requires someone like Alex, who can critically evaluate thescientific literature to decide what parameters need to be included inthe model, and someone like Shannon who can take the predictions of themodel and design experiments to test them in the laboratory.”

The study was supported by the National Institutes of Health, the National Science Foundation and the UC Academic Senate.