A general foothold into the overlap of maximum entropy methods and biology: John Harte's work on applying the mathematical theory of maximum entropy to ecology is certainly one of the better known examples of the application of this area of mathematics to science, in part because he literally wrote the textbook: [Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics (Oxford Series in Ecology and Evolution)][1] To be clear, maximum entropy (also known as MaxEnt in some of the literature, though most/all researchers use the longer form in publications) is a mathematical tool stemming from the fields of probability theory, statistics, and information theory. It's use is classically most often seen in thermodynamics, statistical thermodynamics, physics, and information theory, primarily because these were the areas in which E.T. Jaynes was working when he posited the idea. [Wikipedia has links to his two seminal papers.][2]But because of it's mathematical form, it can be applied in a multitude of areas, typically where one can utilize probabilistic methods. If you're looking for additional areas of application, simply google the phrase "applied maximum entropy" and you'll find a [wealth of areas][3] including: econometrics, natural language processing, nuclear medicine, queuing systems, mass spectrometry, image processing, machine learning, and many others. For ecology related work, a cross search on maximum entropy and "genetics", "evolution", "species", and similar words will provide a wealth of papers like ["A maximum entropy approach to species distribution modeling"][4]. Given the generic nature of your question, I might suggest that you'll find E.T. Jaynes' paper ["On the Rationale of Maximum-Entropy Methods" (IEEE, 1982)][5] useful. Those generally interested in the broader applications of information theoretic methods to biology will likely appreciate some of the work that came out of last year's [NIMBioS Workshop on Information and Entropy in Biological Systems][6] (which Harte both attended and presented at), the [BIRS Workshop Biological and Bio-Inspired Information Theory][7], and the 2014 [CECAM Entropy in Biomolecular Systems][8]. The NIMBios Workshop was organized by John Baez, a physicist, who has worked with MaxEnt methods and explored them on his blog "[Azimuth][9]". Those with a more sophisticated mathematical background (including measure theory, functional analysis, etc.) may appreciate Henryk Gzyl's text http://amzn.to/1MFrrIC</a">The Method of Maximum Entropy (World Scientific: Series on Advances in Mathematics for Applied Sciences, Vol 29, 1995). [1]: http://amzn.to/1SnNB2c [2]: https://en.wikipedia.org/wiki/Principle_of_maximum_entropy [3]: https://scholar.google.com/scholar?q=applied%20maximum%20entropy [4]: http://dl.acm.org/citation.cfm?id=1015412 [5]: ftp://129.240.33.108/pub/outgoing/IMN/Prediction%20modelling%20artikler%20fra%20Anders%20K%20W/Jaynes%201982,%20On%20the%20rational%20of%20Maximum-Entropy%20models.pdf [6]: http://boffosocko.com/2015/05/20/videos-from-nimbios-workshop-on-information-and-entropy-in-biological-systems/ [7]: http://www.birs.ca/events/2014/5-day-workshops/14w5170 [8]: http://www.cecam.org/workshop-1014.html [9]: https://johncarlosbaez.wordpress.com/?s=maximum%20entropy