High-tech Modeling: Computer Mockups Hone in on Human Physiology

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Source: RBC_tube.jpg - RBC Tube
This image illustrates a flow of healthy (orange) and diseased (blue) blood cells. Each blood cell is represented by a mesh made of 500 particles, and small spheres show a sub-set of the particles representing the blood plasma, while instantaneous streamlines and slices represent the ensemble average velocity.

An exploration of recent projects at Argonne National Laboratory and Johns Hopkins University traverse the numerical and graphical landscape of cerebral blood flow, the pathology of malaria, brain aneurysm, sickle cell anemia and even cancer. Computer modeling provides ultra-specific information regarding human response to factors of disease and drug toxicology, which presents not only a supplement but also perhaps one day a potential alternative, in some cases, to animal studies.

Just a few decades ago the computation we take for granted at the touch of a mouse or track pad atop our required vast rooms filled with hardware. Now that those functions have been miniaturized into ever-smaller housing, the worlds’ supercomputers are hard at work with some of life’s most complex questions—for instance, what does it take to model the human brain? Supercomputers like the IBM Blue Gene/Q, Mira, at Argonne National Laboratory just outside Chicago in Lemont, Ill., is being put to the task of answering precise questions about our physiology.

For years, Argonne computer scientists ran simulations on Mira’s predecessor, a Blue Gene/P, mapping discrete neural networks and applying functions of disease to gain a better understanding of the interactions between neurons. Some significant work has been done in the area of epilepsy, but another effort, one to develop a multi-scale computational model of blood flow within the brain, endeavors to connect everything from individual molecules, proteins, cells, arteries and dynamic circulation in the next generation of human modeling. These models are already being applied to a range of clinical applications. Leading this project is George E.M. Karniadakis, PhD, also a professor of applied mathematics at Brown University in Providence, R.I., and a research scientist at the Massachusetts Institute of Technology in Cambridge, Mass.

At Argonne, Karniadakis models hematological disorders at the cellular level. He has modeled individual blood cells deformed due to parasites in the case of malaria infection and genetic variation in hemoglobin in the context of sickle cell anemia all in an effort to better understand these mechanisms of disease. Additionally, Karniadakis and his colleagues are looking into how brain aneurysms develop and rupture as a result of basilary artery occlusion.

“In this case, we are trying to create predictive models that will inform us of when the aneurysm might rupture,” explains Karniadakis. This information could one day be modeled to predict when a specific patient’s aneurysm is likely to rupture for better staging of disease and clinical decision-making. “The idea is to begin to understand some of the observations that are impossible to understand with imaging alone or with laboratory experiments.”

Imaging cannot touch this level of predictive modeling due to inherent limitations in resolution and an inability to capture dynamic events at the micron level and at fractions of a second. 

“It’s very difficult to visualize with sufficient accuracy exactly what is going on,” adds Karniadakis.

Some of the most experimental modeling research at the Argonne Leadership Computing Facility (ALCF), which houses Mira, involves the mapping of neurovascular networks that quantify how neurons interact with brain vessels and how the modulation of blood flow affects individual astrocytes and glial cells.

“We actually quantify these interactions and the interesting thing is that the interaction is bidirectional,” says Karniadakis. Neurons are not only taking up oxygen and nutrients from the blood, but modulating blood flow. “This type of modeling is not very detailed at this point, because we don’t have a lot of data. We don’t yet need a supercomputer to do this work.” 

As computation develops, “lumped models” that include untold numbers of physiological parameters serve as functions in each individual model, but limitations remain. Scientists have not yet fully modeled these networks. “These neural networks are running parallel, but we’ve never been able to actually connect them,” says Karniadakis.

The Blue Brain Project, an international effort, aims to model the entire brain in full and relies on the power of supercomputers and other powerhouses of computation to connect disparate networks. The project is starting small by simulating a cortical column of a rat brain, which is typically comprised of 10,000 neurons. In humans, such a column would connect 100,000 neurons. To put that