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A massively oversimplified cartoon of a fitness landscape

Evolutionary Systems Biology...

... is an emerging field that builds bridges between evolutionary biology and current systems biology with the help of computational modeling fueled by experimental observations. A central aim of evolutionary systems biology is to ultimately predict fitness and other phenotypes from hereditary material and environmental quantities. This long-term goal is equivalent to quantifying the fitness landscapes that govern much of evolution.

Fitness landscapes determine how populations evolve over time. Understanding them could help solve practical problems from reducing rates of antibiotics resistance evolution to anticipating how species evolve in dynamic ecosystems. But how can we move from massively oversimplified cartoons like the one shown here to deeper understanding? This is where a combination of molecular systems biology simulation models based on experimental results and evolutionary approaches can help. In this course we will discuss a broad range of biological, chemical, mathematical, statistical and computational aspects that can serve as starting points to jumpstart your own research contributions to this exciting new field.


This course ...

... has been designed from scratch to give you a taste for what real researchers do. You will venture out into the unknown and who knows what you will find? In the safe limits of the course and with coaching, you will step into the role of a scientist working in an interdisciplinary team with your peers, facing the same challenges scientists face every day.

How? You get a group project to work on (or will pick questions you are interested in), learn how to collaborate, think on your own, maintain a weekly research-log, review the work of your peers, and with your team repeatedly revise your final research proposal that will then be treated like a real one. You can work on any research question you like as long as it contributes to the broad scope of evolutionary systems biology or includes you building a model with tools you learn about in the course.

After the course you will have learned how to build some computational models for biological systems. You will also have learned how to start engaging in interdisciplinary communication. This course will have given you the opportunity to face many of the challenges that scientists face, however with a safety net that can help you evaluate and explain what you experience in your quest to find, where to go next.

You will write a lot and think a lot in this course. This will help you to develop your writing skills and make you more discerning in your thinking. To provide an integrated experience, everything will happen on this website that has been especially designed for this course.

Target audience: advanced undergraduates and graduate students who have found their field. It does not matter, whether your discipline is Biology, Biochemistry, Genetics, Ecology, ...... or Math, Statistics, Physics, Chemistry, Computer Science, Engineering, .... You cannot learn interdisciplinary communication skills in a course where everybody is from a similar discipline.

Informal prerequisites: curiosity, a strong will to engage in real research, some background in your field and an interest to collaborate with people from other fields to arrive at a more complete picture. Your field could be any bio or engineering discipline, math, computer science, physics, chemistry. Since interdisciplinary communication is like translating between different languages and culture, you might even have a humanities degree and contribute to the course in a meaningful way. You need to have found your discipline and know something about it to determine how your work might fit into an evolutionary systems biology context.

Prerequisites: No formal prerequisites. You need to be curious and interested in biology and modeling.

Enrollment: This course is designed to be small, so enroll early to ensure you get a place. Open enrollment Genetics 677-Section11 (if you have questions, please email ).

Credit: This is a 3 credit course that will require about 9 hours of work per week outside of class.