A decade ago or so, if you were that kid who loved science periods in school, and also wished to pursue it for your higher studies, there was this point after your 10th board results, where you were faced with a question of taking either Math or Biology. Everybody was sorted into two “streams,” and there was a clear stereotypical demarcation of what the two groups of people were supposed to do. The ‘Biology people’ would take up medicine or deal with plants and animals in general. The ‘Math people’ would be engineers who built stuff, carried out math, and passionately typed code on their computers. Fortunately now, this wall has become increasingly porous for high school kids grappling with career decisions and also for the scientific community as a whole.
It was tough for me to imagine that there could be an intersection between the two disciplines. As I learned more about biology, the role of computers and mathematics in helping biologists started to become more and more apparent. Want to amplify your DNA sample? Leave your samples inside a thermocycler, and it automatically gets that done. Want to look up the sequence of a gene? You have online open databases with information for just that. Want to monitor an animal’s behavior for an experiment? You have software to do that for you too! These are examples of how tech can make biologists’ work a little more comfortable and efficient. There are also problems in biology where computation is necessary to assess solutions.
I worked on one such problem during my summer in Professor Srinivasa Chakravarthy’s Computational Neuroscience Lab in IIT Madras. Among a lot of other exciting things, the lab was working on preparing a biophysical model of the neuron, to study the metabolism of energy production, and other critical biological processes. Metabolism is the sum of all chemical reactions inside a cell. Some of these chemical reactions will lead to the breakdown of a particular molecule, while others will result in the formation of another biological molecule. All of this will result in an interconnected, complex “metabolic” system of that biological entity.
How do you measure the metabolism of a cell? How do you record the concentration of reactants, intermediate metabolites, and products of these chemical reactions at a particular time inside one specific type of neuron in a person’s brain? Monitoring the progress of a single isolated reaction in a test tube, however tedious, is doable. On the other hand, keeping an eye on multiple interconnected chemical reactions in a living system, not so much. There are also experiments which are quite impossible to perform in real life. For example, for a part of my work, I was required to change the amount of blood flowing to the neurons and record the resulting change in energy metabolites. Now, you would not want to toy with the blood supply to an animal’s brain every time you wanted to do this experiment, would you? So what do you do? You replicate these reactions on the computer by making something called mathematical models.
Remember making models with ice-cream sticks and Thermocol for primary school projects? Mathematical models are similar. The only difference is that the building blocks are differential equations in this case. These models are expressions of real-world systems in terms of mathematical concepts. Consider a simple chemical reaction in glycolysis, which is the process of breaking down glucose to produce energy. The first reaction in this process is the addition of a phosphate group to glucose by an enzyme called Hexokinase. You could write this as a simple “Reactant A” gives rise to “Product B” reaction. But then, it is not so simple. A lot of other metabolites can either speed-up or inhibit this reaction and thus help keep the rate of the reaction under a complex system of control. We add all the factors corresponding to this metabolic regulation for a particular reaction when we write the actual mathematical expression for that reaction. These mathematical expressions are differential equations which represent the change in the concentration of said metabolite, with respect to time. A lot of these differential equations come together to give rise to a model of the glycolysis pathway. Now you can study the effect of reduction of one particular species in a specific biological pathway on another, or maybe explore the relationship between two different reactions.
Larger and more sophisticated mathematical models can help us gain an understanding of complicated processes involved with disorders like Diabetes, Cancer, and even Parkinson’s. My work focused on gaining insights about the pathophysiology of Parkinson’s with respect to the energy metabolism of a neuron. I was supposed to do this with the help of an integrated neuron- astrocyte model. Neurons, the primary functional units of the brain and spinal cord, don’t account for all of the cells of our nervous system. Astrocytes, among others, are star-shaped glial cells that outnumber neurons by four-to-one and support them in a lot of functions. A significant role of astrocytes is the storage of glycogen. Glycogen serves as an emergency energy reserve for neighboring neurons. It can be broken down into glucose and taken up by neurons as and when required to meet their energy requirements. This relationship of metabolic support between astrocytes and neurons is of immense interest while studying neurodegenerative disorders such as Parkinson’s disease. Parkinson’s disease is characterized by the death of neurons in a specific part of the basal ganglia of the brain. Lately, research has suggested a possible connection between this cell loss and energy metabolism. Examining the energy generation mechanisms in these particular cells will help us gain a more thorough understanding of the cause of the disease. It will also enable us to find better and more permanent treatments for patients in the future. Of course, this is not the only application of computational methods in biology. The approach mentioned above comes under a field called computational biology. There are several other fields such as bioinformatics, which deals with the organization, analysis, and interpretation of large amounts of biological data. There is also bionics that integrates biological systems with electronics to help engineer prosthetics and other technology to override disabilities. Newer technologies that integrate life science, engineering, math, and computers are emerging every day. These innovations are proving to be amazingly helpful in solving a lot of pressing problems in today’s world, and questions we previously thought had no solutions.
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