Numbers have a funny way about them. Young math students are taught various strategies to make problem-solving easier. Comparing fractions? Find a common denominator or convert to decimals. The strategies get more complex when doing the kind of math used to describe the activities of DNA, RNA, or protein sequences.
In science, when you make a model, its parameters determine its predictions. But what do you do when different sets of parameters result in the same predictions? Call one half 2/4 or 3/6鈥攅ither way, the result鈥檚 the same. In physics, such parameter sets are called gauge freedoms. They play a key role in how we understand electromagnetism and quantum mechanics. Surprisingly, gauge freedoms also arise in computational biology when trying to model how different mutations interact.
Now, 黑料吃瓜资源 (CSHL) quantitative biologists have developed a unified theory for gauge freedoms in models of biological sequences. Their solution could have countless applications, from plant breeding to drug development.
Granted, most folks have never heard of gauge freedoms. So, how common are they? When it comes to computer models used to describe massive genetic datasets, they鈥檙e basically everywhere, says CSHL Associate Professor Justin Kinney, who co-led this study with Associate Professor David McCandlish.
鈥淕auge freedoms are ubiquitous in computational models of how biological sequences work,鈥 Kinney says. 鈥淗istorically, they鈥檝e been dealt with as annoying technicalities. We鈥檙e the first to study them directly in order to get a deeper understanding of where they come from and how to handle them.鈥
Until now, computational biologists have accounted for gauge freedoms using a variety of ad hoc approaches. Kinney, McCandlish, and their colleagues were looking for a better way. Together, they developed a unified approach. Their new mathematical theory provides efficient formulas scientists can use for all sorts of biological applications. These formulas will allow scientists to interpret research results much faster and with greater confidence.
The investigators also published a that reveals where these gauge freedoms ultimately come from. It turns out they鈥檙e needed for models to reflect symmetries in real biological sequences. Perhaps counterintuitively, making biological models behave in a simple and intuitive way requires them to be larger and more complex. 鈥淲e prove that gauge freedoms are necessary to interpret the contributions of particular genetic sequences,鈥 McCandlish adds.
Together, the studies strongly suggest that Kinney and McCandlish鈥檚 unified approach isn鈥檛 just a new strategy for solving theoretical problems. It may prove fundamental for future efforts in agriculture, drug discovery, and beyond.
Written by: Samuel Diamond, Senior Communications Strategist | [email protected] | 516-367-5055
Funding
National Institutes of Health, Alfred P. Sloan Foundation, Simons Center for Quantitative Biology at CSHL, College of Liberal Arts and Sciences at the University of Florida
Citation
Posfai, A., et al., 鈥淕auge fixing for sequence-function relationships鈥, PLOS Computational Biology, March 20, 2025. DOI: