Cancer cells migrate through the body for multiple reasons; some simply follow the flow of a fluid, while others actively follow specific chemical trails. So how do you determine which cells are moving and why? Researchers at Purdue University have reverse-engineered a cellular signal processing system and used it as a logic gate, a simple computer, to better understand what causes specific cells to migrate.
For many years, mechanical engineering professor Bumsoo Han and his research group have been studying cancer cells. Build microfluidic structures to simulate your biological environment; he has even used these structures to build a “time machine” to reverse the growth of pancreatic cancer cells.
“In our experiments, we’ve been looking at and studying how these cancer cells migrate, because that’s an important aspect of cancer metastasis,” said Hye-ran Moon, a postdoctoral researcher in Han’s team. “But this is different. We’re trying to addressing the fundamental mechanisms behind these behaviors. And it’s very challenging because cells are very complex systems of molecules and are exposed to multiple signals that cause them to move.”
One such signal involves chemical trails, which many cells are inherently attracted to (much like ants following a scent trail). Another is the flow of fluids; if fluids flow around cells in a certain direction, many cells will follow along. So if a cell is moving, how can you tell if it’s motivated by chemicals, fluid movement, or both?
The team adopted a ternary logic gate model to analyze these signals and predict how cells would move in different environments. His research has been published in lab on a chipA Journal of the Royal Society of Chemistry.
Their experiments took place on a microfluidic platform with a central chamber for the cells and two side platforms. Using this device, they could replicate fluidic flows in one direction, in the opposite direction, or no flow at all. They could also introduce a chemical known to cause cells to migrate. Again, they had the option of chemotaxis in one direction, in the opposite direction, or neither. Would these two signals multiply or cancel each other out?
“With two signals and three options each, we had enough observable data to build a ternary logic gate model,” Moon said.
Logic gates are a construction of computer science, where transistors take a 1 or 0 input and return a 1 or 0 output. Binary logic gates take a combination of two 1’s and 0’s, and generate different results depending on the type of gate it is. be. Ternary logic gates do the same thing, except they have three possible inputs and outputs: 1, 0, and -1.
Moon assigned values in which direction the cells moved under the two different stimuli. “If the cells moved in the direction of the flow, that’s 1,” Moon said. “If they have no directionality, that’s 0. If they’re moving in the opposite direction of flow, that’s -1.”
When the cells encountered fluid or chemical flow individually, they moved in the positive direction (the “1”). When both were present at the same address, the effect was additive (still “1”). However, when the two flowed in opposite directions, the cells moved in the direction of the chemicals (the “-1”), rather than the fluid flow.
Based on these observations, Moon extrapolated a 3×3 grid to simplify the results. The signals from these cancer cells could now be diagrammed like an electrical engineer would diagram a circuit.
Of course, the real world is never that simple. “Actually, the chemical stimulus is a gradient, not an on-off switch,” Moon said. “Cells will only move once a certain flow threshold has been entered; and if you enter too much, the cell shorts out and doesn’t move at all. The accuracy with which we can predict that movement is a non-linear relationship. “
Moon also emphasized that this particular experiment is very simple: two stimuli, in strictly opposite directions, in a single dimension. The next step would be to build a similar experiment, but on a two-dimensional plane; and then another in a three-dimensional volume. And that’s just for starters; once you add multiple stimuli and factor in time as the fourth dimension, the calculations become incredibly complex. “Now you understand why biologists need to use supercomputers!” Luna said.
This study was conducted in collaboration with the Purdue Institute for Cancer Research; the Weldon School of Biomedical Engineering; the Purdue Department of Physics and Astronomy; and Andrew Mugler and Soutick Saha of the Department of Physics and Astronomy at the University of Pittsburgh.
“This is a perfect example of how microfluidic devices can be used in cancer research,” Moon said. “Doing this experiment in a biological environment would be extremely difficult. But with these devices, we can go directly to individual cells and study their behavior in a controlled environment.”
“This model can be applied to much more than just physical cancer cells,” Moon continued. “Any cell can be affected by different signals, and this provides a framework for researchers to study those influences and determine why they happen. Genetic engineers have also embraced the logic gate model, treating genes as processors that give different results.” when you give them certain instructions. There are many branches we can go with this concept.”