Biohealth science’s connection to quantitative sciences

Redefining quantitative and biohealth sciences

Faculty and researchers in the College of Science are interpreting and advancing biohealth sciences in innovative new ways by applying the natural sciences, such as mathematics, statistics and chemistry. In recent times, researches in biology and medicine have been guided by biomolecular analysis technologies, mathematics and computations, and scientists are using these tools to address a spectrum of biological questions about diseases, from how they spread to risk factors.

In the last few years, our College has experienced an impressive spurt of transdisciplinary research in the quantitative and biohealth sciences. Ongoing studies and research advances range from analyzing genetic data on epidemics and inventing disease-detecting biosensors to developing statistical methods to better understand neuron connectivity and the transmission of signals in the brain. Through collaborative research across our campus, our faculty are paving the way for innovative biohealth science research which broadens the training of students across scientific disciplines.

Biological systems and mathematical models

Connections between biology and the mathematical sciences are fueling innovation and expansion in those disciplines. Statistician Sharmodeep Bhattacharyya explains how interpreting data from various experimental sources can generate new insights and solutions in the areas of neuroscience and genomics.

“Statistical methods, with their inherent objective of analyzing the uncertainty of a system help identify key interesting factors in the deluge of interesting data,” said Bhattacharyya.  “Such jobs can range from identifying a key set of genes affecting a disease for a specific group of people (like in precision medicine) or identifying the interaction between key regions of the brain for people who have a set of genes that causes a neurological disease.”

Bhattacharyya has developed new statistical methods to analyze Electro-Cortico Graph (ECoG) array data from human and rat brains to identify connections involving speech and hearing.

Mathematician Vrushali Bokil’s research demonstrates how mathematical modeling, analysis and numerical simulations can illuminate insights in complex biological systems and how the health sciences, in turn, can spark new mathematical ideas. She collaborates with a mix of biologists and mathematicians across the country as well as in the UK, France and Germany on a project funded by NIMBioS (the National Institute of Mathematical and Biological Synthesis).

The project will allow Bokil and her colleagues to generate novel mathematical and statistical methods involving multiple hosts and multiple pathogens and that operate across a range of spatiotemporal scales, and to analyze the effects of climate change and human activities on the emergence of new plant viruses. Bokil points to the increasing use of mathematics to model complicated biological systems.

“It is exciting to be at the interface of biology and math,” said Bokil. “I write down a system of equations that models the physical or biological system. While the mathematical modeling and numerical simulations are fascinating in and of themselves, the added value of feeding back into biological applications is very rewarding.”

Benjamin Dalziel, an assistant professor in Integrative Biology, is part of a growing breed of biologists who are turning the biological sciences into a more quantitative field. Dalziel is a population biologist who uses mathematical tools to answer questions about the spread of infectious diseases, such as influenza and measles in populations and cities.

Dalziel, who also has an appointment in the mathematics department, maps hotspots of pathogen activity and diversification, and develops mathematical models to explain the patterns he finds. A current project explores whether there are systematic differences among cities with respect to their epidemic risk.

“I find the connections between mathematical modeling and biology very interesting. After developing a model, we ask, ‘Is this happening in nature and how do we test it?’ And if nature is doing something different, ‘What did we get wrong with the model?’ Sometimes there is a lot you have to do with the model besides [reviewing the] data to understand its behavior and to get it to interface with the real world,” said Dalziel, who is developing a new mathematics course specifically for the life sciences.

Innovative disease imaging

A major application of analytical chemistry and its quantitative aspects to biology involves the creation of tools that directly aid in the diagnosis of cancer, heart disease, strokes and other serious ailments.

Chemistry assistant professor Sean M. Burrows runs a busy lab comprising undergraduates and doctoral students and their research is focused on innovating technologies to visualize biomarkers of disease. They pioneer novel, colorful fluorescent biosensor designs—analytical devices that relate biological molecules to a fluorescent signal—for visualizing and quantifying microRNAs, which are small non-coding RNA molecules that have a role in a plethora of gene regulatory events.

MicroRNAs hold great potential to yield information about the beginning stages of a disease and cell/tissue activity. Burrows and his team are trying to develop highly efficient fluorescent technologies for basic research and clinical use.

“Basically the idea is to design an imaging technology that will give us more information on the molecular interactions within the cell,” explains Borrows. “[For example], can we create an instrument that greatly advances the information content in terms of the numbers of colors we can look at in a cell? With the current technology, you could see one or two colors from the cell. But if we can look at 10 or more different colors, that will tell us much more about a biological mechanism,” adds Burrows.

In an exciting breakthrough, the Burrows group designed a more efficient fluorescent biosensor for better signal interpretation from microRNA biosensors. The innovation has attracted significant attention in the field and was favorably reviewed in an article on the field of emerging microRNA biosensors in Analytical Chemistry.

However, existing imaging technology to learn about the underlying details of cellular mechanisms, such as the super resolution microscopy, is expensive. Burrows is keen to develop a cheaper alternative that can be used in a regular microscope.

“We can then open the door for more researchers to get more information from the cells they are interested in studying. This, in turn, will enable more transformative breakthroughs to understand disease progression and ultimately find cures.”

This figure shows that the biosensors can enter a cell’s cytoplasm and nucleus to find where microRNA expression occurs within the cell. Green shows the cytoplasm and the dark green spot is the nucleus. Red indicates the biosensor. The circle and the red nucleus indicate where the biosensor has entered the cytoplasm and nucleus, respectively.

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