“Science provides adventure and excitement every single day. When you’re pushing boundaries, you get to jump into the abyss of new areas. It can be scary, but it’s an incredible opportunity to try to improve our world and people’s lives,” says César de la Fuente, Ph.D., a Presidential Assistant Professor in the Perelman School of Medicine and School of Engineering and Applied Science at the University of Pennsylvania, Philadelphia. Our interview with Dr. de la Fuente highlights his journey of becoming a scientist and his research using artificial intelligence to discover new drugs.
Q: How did you first become interested in science?
A: I’ve always been fascinated by the world around me. I grew up in a town in northwest Spain, right on the Atlantic Ocean. As a kid, I would go to the beach to investigate marine organisms and bring home all sorts of different fish to study. My mom wasn’t too happy about that! We’re all born scientists, but we tend to lose that curiosity as we enter adulthood. The key is to not lose our ability to learn every day.
The cloud. To many, it’s a mysterious black hole that somehow transports photos and files from their old or lost phone to their new one. To some researchers, though, it’s an invaluable resource that allows them access to data analytics tools they wouldn’t otherwise have.
Scientists have begun using cloud computing to store, process, and analyze their data through online bioinformatics tools. Biological data sets are often large and hard to interpret, requiring complex calculating instructions—or algorithms—to understand them. Fortunately, these algorithms can run on local computers or remotely through cloud computing.
One advantage of cloud-based programs over local computers is the ability to analyze data without taking up the user’s personal storage space. With cloud-based storage, researchers can store their large data files, including their labeled notes called annotations. Another benefit is that users have easy access to software packages within the cloud for data analysis. The cloud also encourages collaboration among scientists by making it easy to share large amounts of data.
Dr. Brian Munsky. Credit: Colorado State University.
“I think having a career in science is really the best way to rechannel the inner child, to remain forever curious about the world,” says Brian Munsky, Ph.D., an associate professor of chemical and biological engineering at Colorado State University, Fort Collins. Check out the highlights of our interview with Dr. Munsky below to learn how his childhood practical jokes led to him running a research group that uses computational and experimental methods to study complex processes inside cells.
“You’re not going to be able to do biology without understanding programming in the future,” Melissa Wilson, Ph.D., an associate professor of genomics, evolution, and bioinformatics at Arizona State University, said in her 2019 NIGMS Early Career Investigator Lecture. “You don’t have to be an expert programmer. But without understanding programming, I can assert you won’t be able to do biology in the next 20 years.”
A growing number of researchers, like Dr. Wilson, are studying biology using computers and mathematical methods. Some of them started in traditional biology or other life science labs, while others studied computer science or math first. Here, we’re featuring two researchers who took different paths to computational biology.
Recent news headlines are awash in references to “modeling the spread” and “flattening the curve.” You may have wondered what exactly this means and how it applies to the COVID-19 pandemic. Infectious disease modeling is part of the larger field of computer modeling. This type of research uses computers to simulate and study the behavior of complex systems using mathematics, physics, and computer science. Each model contains many variables that characterize the system being studied. Simulation is done by adjusting each of the variables, alone or in combination, to see how the changes affect the outcomes. Computer modeling is used in a wide array of applications, from weather forecasting, airplane flight simulation, and drug development to infectious disease spread and containment.
Sohini Ramachandran, Brown University. Credit: Danish Saroee/Swedish Collegium for Advanced Study.
Recent advances in computing enable researchers to explore the life sciences in ways that would have been impossible a few decades ago. One new tool is the ability to sequence genomes, revealing people’s full DNA blueprints. The collection of more and more genetic data allows researchers to compare the DNA of many people and observe variations, including those shared by people with a common ancestry.
Sohini Ramachandran , Ph.D., is director of the Center for Computational Molecular Biology and associate professor of biology and computer science at Brown University in Providence, Rhode Island. She is also a recent recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). Dr. Ramachandran researches the causes and consequences of human genetic variations using computer models. Starting with genomic data from living people, her lab applies statistical methods, mathematical modeling, and computer simulations to discover how human populations moved and changed genetically over time.
Many researchers who search for anti-cancer drugs have labs filled with chemicals and tissue samples. Not Rommie Amaro. Her work uses computers to analyze the shape and behavior of a protein called p53. Defective versions of p53 are associated with more human cancers than any other malfunctioning protein.
We asked the heads of our scientific divisions to tell us about some of the big questions in fundamental biomedical science that researchers are investigating with NIGMS support. This article is the second in an occasional series that explores these questions and explains how pursuing the answers could advance understanding of important biological processes.
The number of copies of mRNA molecules (bright green) observed here in yeast cells (dark blue) fluctuates randomly. Credit: David Ball, Virginia Tech.
For some health conditions, the cause is clear: A single altered gene is responsible. But for many others, the path to disease is more complex. Scientists are working to understand how factors like genetics, lifestyle and environmental exposures all contribute to disease. Another important, but less well-known, area of investigation is the role of chance at the molecular level.
One team working in this field is led by John Tyson at Virginia Tech. The group focuses on how chance events affect the cell division cycle, in which a cell duplicates its contents and splits into two. This cycle is the basis for normal growth, reproduction and the replenishment of skin, blood and other cells throughout the body. Errors in the cycle are associated with a number of conditions, including birth defects and cancer. Continue reading “How Cells Manage Chance”
Football image credit: Stock image. The colored contoured lines show the periodic stops in the growth of a bacterial colony. Credit: Süel Lab, UCSD.
What do these images of football fans and bacterial cells have in common? By following simple rules, each individual allows the group to accomplish tasks none of them could do alone—a stadium wave that ripples through the crowd or a cell colony that rebounds after antibiotic treatment.
These collective behaviors are just a few examples of what scientists call emergent phenomena. While the reasons for the emergence of such behavior in groups of birds, fish, ants and other creatures is well understood, they’ve been less clear in bacteria. Two independent research teams have now identified some of the rules bacterial cells follow to enable the colony to persist. Continue reading “The Simple Rules Bacteria Follow to Survive”