I was asked today to propose new grade level courses for the Department of Molecular Biology and Genetics. This is my proposal.
PhD level courses proposition
The study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system. This approach combines biology theory with mathematical and computational models.
Our students need to know the basic concepts of statistical analysis and mathematical modeling used in Systems Biology, and computational concepts for carry out these models by themselves or in collaboration.
Advanced Scientific Computing
Grad students that follow a specialization on Systems Biology or Computational Biology will be interacting with scientists of other areas.
They will model complex biological systems, simulate them to understand them, and find the values of the parameters in the model that better fit the experimentally observed results.
They need the language and concepts of parallel and distributed computing, clusters, cloud, massive parallel computation, artificial intelligence, neural networks, genetic algorithms and other topics of high performance computing and data mining.
We should provide the environment where they can grasp these concepts. Either an “Advanced Computing” lecture, a workshop or a laboratory.
New science is often coming from new instruments. From Galileo’s telescope to the next generation DNA sequencers and PCR amplification, the availability of better measurements tools not only provides new data, it also results in better understanding the nature.
Technological changes in the last 5 years enable us to build low-cost laboratory instruments that can be easily modified for new experiments. In this class we will teach the physicochemical principles that are the basis of modern molecular biology instruments and the construction techniques that will allow the future researchers to build new, better, cheaper, faster or more sensitive instruments. This course is based on courses that MIT has been teaching since 1998.
Master level courses proposition
Methodology of research
The future researchers should know what is Science, the scientific method and their limitations. They need to know the difference between correlation and causality. Moreover, they should learn the difference between data, information, knowledge and understanding.
This course can include some “history of science” and some “epistemology”. It should also include “deductive and inductive logic”, “common fallacies and how to avoid them” and a formal introduction to “statistical inference”, in both the classical and Bayesian approach.
Metagenomics, environmental sampling and complex communities
It is well known that growing cultures in the lab induces a bias that distorts the composition of environmental communities.
Nevertheless in the recent years it has become clear that to understand many biological process (in nature, industry and human health) we need to sample and understand the interactions in complex microbial environments. It can be seen as “genomics of the ecosystem”. This course should teach the theoretical and practical tools needed to acquire and analyze such consortia.
Scientific Communication and Collaboration workshop
Here students can exercise and improve their writing and speaking skills. They should practice writing papers and present them, in English, as a rehearsal of a conference. It can be shared with other departments to encourage multidisciplinary interaction and learning of technical language.
Collaboration also requires online tools. Papers will be written by many authors simultaneously using Dropbox, Google Docs or GitHub. Or maybe it will be more like Wikipedia or arXiv. Our students need to know these tools and the methodology that each one requires.
A specific topic that should be addressed in this course is Intelectual property. Many of these collaborations and publications will not be necessarily published in scientific journals. Some may be patented, some may be licensed for industrial applications. Others may be published on Wikipedia or other web media. Or delivered as open source. The alternatives are many and they are relevant for the new generation of molecular biologists.
There should be a lecture where the students learn what can be patented, how to patent an invention and about the different licenses that protect or disclose intelectual property.
Advanced undergrad courses
The next two courses are designed for undergrad students but can be also followed by master students. These are basic concept that the Master student must handle so if he/she cannot prove knowledge of them then they need to level up.
Introduction to Data Science
The students will learn how to handle experimental data and how to communicate with scientists of other data-oriented disciplines.
They will learn how to produce publication quality reports withcreproducible results. How to get raw data, extracting relevant information, filter it using several selection criteria. How to store and retrieve it in efficient and useful ways. How to transform it, organize it, categorize it, display, show and understand the results.
Tools include Unix command line tools, SQL and the R statistical package. The student should be able to understand how computer networks work and what are their limitations.
Programming for Research
Currently the plan is to teach Python and BioPython to analyze, model, evaluate and predict the behavior of genomic and molecular biology entities. The students should be able to interact with high end servers, use command line tools and be comfortable in computing environments others than Microsoft Windows.
The objective of this course is no to make our students experts on computer science, but to give them the concepts and language that will allow them to collaborate in interdisciplinary groups.
University on the Internet era
Digital technologies promotes a shift from faculty-centered to learner-centered teaching process (Katz (1999)). It is no longer true that the teacher is the only source of knowledge. We should assume (and encourage) that students use free on-line material. Then we can focus on teaching and practicing in the laboratory what cannot be learned on the web. And we need a plan to make our own on-line classes.
Katz, R.N. 1999. Dancing with the Devil: Information Technology and the New Competition in Higher Education. The Jossey-Bass Higher and Adult Education Series. Jossey-Bass Publishers.