Biology is increasingly a field dominated by high-throughput methods, yielding large data sets which require data analysis using both public domain/commercial software as well as new algorithms to be implemented in a programming language. Previous generations of biologists painstakingly isolated proteins using techniques from biochemistry, collaborating protein chemists then determined the 3-dimensional structure by X-ray diffraction, binding sites and protein function were determined, genetic screens were employed, etc. While this work is and will remain critical for biology, there has been a paradigm shift, from the older model, which can be summarized as many experiments, one gene, to the newer model of one experiment, many genes.
Along with this paradigm shift, newer computational techniques, going well-beyond classical biostatistics, have become necessary. Some buzzwords: profile, hidden Markov model, neural network, support vector machine, Monte Carlo, etc. The new fields of bioinformatics and computational molecular biology have seen an influx of mathematicians, computer scientists, physicists, chemists, statisticians working on problems such as the following.
Bioinformatics is a rapidly maturing field at the confluence of biology, mathematics and computer science. It strives to better understand the molecules essential for life, by harnessing the power and speed of computers. Mathematical models and software are developed by computational biologists, who must have good skills in math and computer science. However, the working (experimental) biologist is generally a user of "black box" bioinformatics software and databases, with little knowledge of the underlying algorithms. While there's no need for the working biologist to be able to be able to develop sophisticated mathematical models or design and implement novel algorithms, there is a need to understand the broad ideas of algorithms underlying the tools the biologist uses. As a working professional in the medical and life sciences, this knowledge is important to understand the scope, applicability, and limitations of certain tools -- and, who knows, you may become turned on by the idea of ferreting out biological insights by applying mathematics and computer science, and so choose to pursue further courses to become a computational biologist.
This introductory course requires that you have a basic understanding of molecular biology, genetics, and use of Internet, but does not require that you have any background in mathematics or programming -- nevertheless, if you have had a course in probability theory and statistics, then you'll understand more easily some of the approaches. Nevertheless, the class will be self-contained, and I will not assume that anyone has had any math beyond calculus.
Goals of the course are: (1) to broadly understand the type of mathematical and algorithmic reasoning that lies behind various important bioinformatics tools, (2) to gain some working knowledge in using certain biological databases and on-line bioinformatics algorithms.
Homework, possible quizzes 20% Midterm examination 35% PowerPoint Oral Presentation 10% Final Exam 35%
Class policy is that unexcused absences from midterm examinations receive a 0. Absences due to illness, medical or family emergencies and comparable serious situations are excused provided that a letter from the dean is presented.
No late homework will be accepted.
The grading policy is subject to change. If so, then this will be clearly announced with ample time.
Any deviation from this policy, can immediately result in a course grade of "F" and be turned over to the Board of Academic Integrity for a hearing.
Please refer to Acdademic Integrity Policy for more details concerning the university academic integrity policy.
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