What do ‘Bohemian Rhapsody,’ ‘Macbeth,’ and a list of Facebook Friends All Have in Common?

New research finds that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share large amounts of information efficiently.

Examples of statistical network analysis of characters in two of Shakespeare’s tragedies. Two characters are connected by a line, or edge, if they appear in the same scene. The size of the circles that represent these characters, called nodes, indicate how many other characters one is connected to. The network’s density relates to how complete the graph is, with 100% density meaning that it has all of the characters are connected. (Image: Martin Grandjean)

 

By Erica K. Brockmeier

To an English scholar or avid reader, the Shakespeare Canon represents some of the greatest literary works of the English language. To a network scientist, Shakespeare’s 37 plays and the 884,421 words they contain also represent a massively complex communication network. Network scientists, who employ math, physics, and computer science to study vast and interconnected systems, are tasked with using statistically rigorous approaches to understand how complex networks, like all of Shakespeare, convey information to the human brain.

New research published in Nature Physics uses tools from network science to explain how complex communication networks can efficiently convey large amounts of information to the human brain. Conducted by postdoc Christopher Lynn, graduate students Ari Kahn and Lia Papadopoulos, and professor Danielle S. Bassett, the study found that different types of networks, including those found in works of literature, musical pieces, and social connections, have a similar underlying structure that allows them to share information rapidly and efficiently.

Technically speaking, a network is simply a statistical and graphical representation of connections, known as edges, between different endpoints, called nodes. In pieces of literature, for example, a node can be a word, and an edge can connect words when they appear next to each other (“my” — “kingdom” — “for” — “a” — “horse”) or when they convey similar ideas or concepts (“yellow” — “orange” — “red”).

The advantage of using network science to study things like languages, says Lynn, is that once relationships are defined on a small scale, researchers can use those connections to make inferences about a network’s structure on a much larger scale. “Once you define the nodes and edges, you can zoom out and start to ask about what the structure of this whole object looks like and why it has that specific structure,” says Lynn.

Building on the group’s recent study that models how the brain processes complex information, the researchers developed a new analytical framework for determining how much information a network conveys and how efficient it is in conveying that information. “In order to calculate the efficiency of the communication, you need a model of how humans receive the information,” he says.

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The Optimal Immune Repertoire for Bacteria

by Erica K. Brockmeier

Transmission electron micrograph of multiple bacteriophages, viruses that infect bacteria, attached to a cell wall. New research describes how bacteria can optimize their “memory” of past viral infections in order to launch an effective immune response against a new invader. (Image: Graham Beards)

Before CRISPR became a household name as a tool for gene editing, researchers had been studying this unique family of DNA sequences and its role in the bacterial immune response to viruses. The region of the bacterial genome known as the CRISPR cassette contains pieces of viral genomes, a genomic “memory” of previous infections. But what was surprising to researchers is that rather than storing remnants of every single virus encountered, bacteria only keep a small portion of what they could hold within their relatively large genomes.

Work published in the Proceedings of the National Academy of Sciences provides a new physical model that explains this phenomenon as a tradeoff between how much memory bacteria can keep versus how efficiently they can respond to new viral infections. Conducted by researchers at the American Physical Society, Max Planck Institute, University of Pennsylvania, and University of Toronto, the model found an optimal size for a bacteria’s immune repertoire and provides fundamental theoretical insights into how CRISPR works.

In recent years, CRISPR has become the go-to biotechnology platform, with the potential to transform medicine and bioengineering. In bacteria, CRISPR is a heritable and adaptive immune system that allows cells to fight viral infections: As bacteria come into contact with viruses, they acquire chunks of viral DNA called spacers that are incorporated into the bacteria’s genome. When the bacteria are attacked by a new virus, spacers are copied from the genome and linked onto molecular machines known as Cas proteins. If the attached sequence matches that of the viral invader, the Cas proteins will destroy the virus.

Bacteria have a different type of immune system than vertebrates, explains senior author Vijay Balasubramanian, but studying bacteria is an opportunity for researchers to learn more about the fundamentals of adaptive immunity. “Bacteria are simpler, so if you want to understand the logic of immune systems, the way to do that would be in bacteria,” he says. “We may be able to understand the statistical principles of effective immunity within the broader question of how to organize an immune system.”

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Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania and a member of the Department of Bioengineering Graduate Group

This research was supported by the Simons Foundation (Grant 400425) and National Science Foundation Center for the Physics of Biological Function (Grant PHY-1734030). 

Danielle Bassett Receives New Scholarly Chair

Danielle Bassett, Ph.D.

Danielle Bassett has been named the J. Peter Skirkanich Professor of Bioengineering.

Dr. Bassett is a Professor in the department of Bioengineering at the School of Engineering and Applied Science. She holds a Ph.D. in Physics from the University of Cambridge and completed her postdoctoral training at the University of California, Santa Barbara, before joining Penn in 2013.

Dr. Bassett has received numerous awards for her research, including an Alfred P Sloan Research Fellowship, a MacArthur Fellowship, an Office of Naval Research Young Investigator Award, a National Science Foundation CAREER Award and, most recently, an Erdos-Renyi Prize in Network Science to name but a few. She has authored over 190 peer-reviewed publications as well as numerous book chapters and teaching materials. She is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts.

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