Science by the Numbers: Modeling Complex Biological Processes

Monday, December 17, 2018

computer microchip with binary code

Computers are valuable tools for modeling biological processes, says the IRP’s Dr. Carson Chow, and the more powerful the computer, the more complex the model can be.

Science is a process of trial and error. Most successful research publications are preceded by at least a few false starts and perhaps weeks or even months of tinkering to get experiments to work. For IRP senior investigator Carson Chow, Ph.D., this process of testing and throwing out one potential solution after another is an essential part of his research, so much so that he may go through thousands of iterations before arriving at one that works. However, rather than test each approach himself, he leverages the IRP’s considerable computing power to considerably accelerate the process of sorting the wheat from the chaff.

Whereas most research scientists are specialists, devoting their careers to the study of one specific aspect of their field, Dr. Chow is more of a jack-of-all-trades. At any one time, he might be studying the genetics of obesity, scrutinizing the timing of gene activity, investigating the behavior of hormone receptors, or examining the biological basis of mental illnesses. To him, the one unifying factor in all these endeavors is math.

“The same math that describes heat flowing down a rod is the same math that describes molecules bouncing around your room is the same math that describes the passage of ions in neurons,” Dr. Chow says. “I’m like a carpenter — I work on different houses. Biologists work on just one house, but I work on whatever house is broken.”

Much of Dr. Chow’s work involves creating and testing mathematical models that represent biological systems. At the most basic level, a mathematical model takes a set of assumptions and turns them into an equation whose outcome can be tested to see if it matches what happens in the real world. For example, one model of weight loss asserts that a person who eats 3,500 fewer calories than his or her body uses will lose one pound of fat. A mathematical equation for this process might read as follows: (calories used – calories consumed) / 3500 = pounds lost. A scientist can then test this model by feeding a group of research volunteers 3,500 calories fewer than they need. If the volunteers lose one pound each on average, the model is correct. If not, it clearly needs some tweaking, and in fact research by Dr. Chow and others has suggested that this long-referenced model of weight loss is indeed incorrect.1,2

Supercomputers like NIH’s Biowulf system are a key part of this fine-tuning procedure. Their massive computing power allows researchers like Dr. Chow to test many models designed to represent a biological process, each slightly different from its counterparts, until they find one that nearly matches real-world observations. Specialized algorithms also help streamline this search by narrowing down the field of possibilities the computer must sort through.

“Sometimes you’ve got an equation and it’s got a lot of knobs, and you don’t know what the settings of the knobs should be,” Dr. Chow explains. “So what you do is you try all of the possible settings, and you just run a bunch of different versions until the outcome looks like what you expect. You use the computer to sweep through all the possibilities of the model. The more powerful the computer, the faster you can search and the more complex you can make the model.”

Much of Dr. Chow’s recent work has focused on creating mathematical models of how brain cells communicate with one another. In a paper published in September, for example, Dr. Chow enlisted Biowulf to develop of a network of model neurons that can fire in any of the ways scientists have observed real neurons firing, including mimicking the activity pattern of neurons involved in planning and executing movements.3 Once scientists have reasonably accurate models of the way healthy neurons behave, Dr. Chow says, researchers can “break” these models to make them behave the way collections of brain cells do in patients with mental illnesses like autism. This would then allow scientists to test their theories about what causes those conditions.

“It’s hard to make progress if you don’t have some underlying theory of what you think the system is doing,” Dr. Chow says. “Unless data is put into some context, it’s hard to use data. Having a theory is crucial for conceptualizing a disease, figuring out how to treat it, and knowing what to do next.”

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References:

[1] Quantification of the effect of energy imbalance on bodyweight. Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL, Swinburn BA. Lancet. 2011 Aug 27;378(9793):826-37. doi: 10.1016/S0140-6736(11)60812-X.

[2] Can a weight loss of one pound a week be achieved with a 3500-kcal deficit? Commentary on a commonly accepted rule. Thomas DM, Martin CK, Lettieri S, Bredlau C, Kaiser K, Church T, Bouchard C, Heymsfield SB. Int J Obes (Lond). 2013 Dec;37(12):1611-3. doi: 10.1038/ijo.2013.51.

[3] Learning recurrent dynamics in spiking networks. Kim CM, Chow CC. Elife. 2018 Sep 20;7. pii: e37124. doi: 10.7554/eLife.37124.

Category: Science

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