A research team at the National Institute of Standards and Technology (NIST) has developed a model* for making quantifiable predictions of how a group of cells will react and change in response to a given environment or stimulusand how quickly. The NIST model, in principle, makes it possible to assign reliable numbers to the complex evolution of a population of cells, a critical capability for efficient biomanufacturing as well as for the safety of stem cell-based therapies, among other applications.
The behavior and fate of cells are only partially determined by their DNA. A living cell reacts to both its internal and external environmentthe concentration of a particular protein inside itself or the chemistry of its surroundings, for exampleand those reactions are inherently probabilistic. You can't predict the future of any given cell with certainty.
This inherent uncertainty has consequences, according to NIST biochemist Anne Plant. "In the stem cell area in particular, there's a real safety and effectiveness issue because it's very hard to get 100 percent terminal differentiation of stem cells in a culture," she says. This could be problematic, because a therapist wishing to produce, say, heart muscle cells for a patient, might not want to introduce the wild card of undifferentiated stem cells. "Or effectiveness may be dependent on a mixture of cells at different stages of differentiation. One of the things that is impossible to predict at the moment is: if you waited longer, would the number of differentiated versus nondifferentiated cells change? Or if you were to just separate out the differentiated cells, does that really remove all the nondifferentiated cells? Or could some of them revert back?" says Plant.
The NIST experiments did not use stem cells, but rather fibroblasts, a common model cell for experiments. The team also used a standard tracking technique, modifying a gene of interestin this case, one that codes for a p
|Contact: Michael Baum|
National Institute of Standards and Technology (NIST)