A researcher at the University of Michigan has unveiled a new mathematical model that offers new understanding of cell signalling, which may be pretty important in identifying and targeting cellular processed that cause disease conditions.
"This has the potential to be a true paradigm shift," says Dr. Sofia Merajver, a professor in the U-M Department of Internal Medicine and co-director of the Breast Oncology Program at the U-M Comprehensive Cancer Center.
Merajver says that understanding the full complexity of signalling pathways and their interactions is critical in discovering effective treatments for cancer, inflammation, and several other conditions.
An article describing the new mathematical model, published in Plos Computational Biology, says that it offers scientists an opportunity to improve current mathematical models with a superior tool that can take advantage of advances in computing power.
"I would hope that it may help guide us much better than our own intuition to decide what our targets are. If we can understand these pathways better, we should be able to pick more effective targets. This is the step before screening a drug. Until now, there have been very few tools to help us choose a target," Merajver says.
She says that her team has tested their model using experimental data from a well-known signalling pathway involved in many disease states, the MAPK pathway.
The researchers' observations during the experiment suggest that this kind of signalling pathway naturally transmits information not just in a forward direction, but also backwards, which implies new considerations if drugs are to adequately address key targets.
Besides, the study will enable scientists to construct models that take into account interactions between two pathways, or "cross-talk", say the researchers.
Merajver, who had discovered oncogenes that foster metastasis in a previous study, has revealed that her lab has numerous plans to put the model to work immediately.
"We hope it will broaden our understanding on how to inhibit metastasis, since our lab studies this aspect of cancer; this work has many applications for normal and disease conditions," she says.