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| Seema Nanda |
Mathematician Seema Nanda loves the company of biologists. After all, Nanda — a professor at the TIFR Centre for Applied Mathematics in Bangalore — belongs to a rare breed of scientists who opt to use their number-crunching skills to tame the most slippery of human afflictions: cancer.
Nanda was an odd person out at a recent international meeting of biologists in Bangalore which discussed some cutting edge work on stem cells and their role in regenerating organs and treating diseases.
“Maths biology is really big around the world. But in India, it hasn’t taken off a big way,” she told KnowHow. Using mathematical modelling, Nanda showed how chemotherapy for chronic myelogenous leukaemia, a type of blood cancer, could be administered more effectively.
“Mathematics is an extremely useful tool. It helps doctors and others who work on diseases like cancers to ask the right questions and arrive at the right dosages for treatment,” she says.
“We all know how doctors arrive at the dosage required for treatment — through trial and error. What mathematical modelling can do is help them to arrive at the right dosage much faster and more efficiently,” Nanda explains.
Not that this makes it an exact science but at least it gives doctors a better idea. It helps us understand what the right regime is. “So you don’t start off with a low-end or rather high-end dosage,” she says. In diseases like cancers, particularly in those where a combination of drugs is used, administering the right dosage has been a major dilemma.
“Mathematical models of cancer cell growth and response to chemotherapy were found to be efficient in predicting the minimum concentration of chemotherapy necessary to prevent cancer cell growth,” observes Arni Srinivasa Rao, a researcher at the Centre for Mathematical Biology at Oxford.
In Bengal, too, a team of researchers is trying to develop mathematical models for the treatment of cancers. A team of bioinformatics experts at the Bengal Engineering & Science University in Howrah led by Durjoy Majumder recently reported a new strategy to minimise the side effects of chemotherapy in cancer treatment. Called metronomic chemotherapy, it helps release anti-cancer drugs in smaller, harmless doses.
Rao, who has earlier developed mathematical models for the National AIDS Control Organisation and the World Bank for understanding key health issues, says simulation has also been found useful in analysing certain cases of heart failure and in evaluating drug impact.
Recently Rao developed a mathematical model for understanding the spread of avian bird flu in India. “This has been accepted for publication in a forthcoming issue of a mathematical journal from the US,” he told KnowHow.
Lifestyle diseases like diabetes too can benefit from such number crunching. In the west, mathematical modelling is being used to understand the insulin-glucose metabolism. “Medication is available and diabetes can be kept under check. But we still don’t know the exact metabolism as to how and when the blood sugar goes up and insulin kicks in. How this happens from minute to minute is not well understood,” says Nanda.
Rao agrees. “Models for diabetes could be very complicated, because most of the good models developed to understand the impact of drugs involve several variables, parameters and multiple risk factors. Such models for diabetes were found to be successful in disease management and patient care.”
For instance, mathematical models can be constructed to assist hospital administrators in care giving and hospital management. If the objective is to predict the number of beds, medicines and critical care equipment required in the future for a hospital, calculating these values based on a simple arithmetic relation among the population of the city, the number of past patients, the average duration of stay and the average utilisation of intensive care units might lead to improper estimates because these variables need not be controlled by simple relations. In such situations, mathematical models constructed using the above information could lead to better predictions for hospital care.
“Except for a few instances, mathematical models are not being used widely for hospital care. Models for epidemiology, disease progression, cell growth and disease management are better known than models for hospital care,” he says.