**Core Courses:**

These three classes are most relevant to the Bioinformatics Minor. We encourage students interested in careers in Bioinformatics to take as many of these courses as possible. Completion of two out of three courses is required to complete the Bioinformatics Minor. However, we encourage students to take the remaining core course as an elective! They require statistics and programming prerequisites:

- Statistics 100A or Math 170A or Biostatistics 100A or 110A
- AND one of Program In Computing (PIC) 10C or Computer Science 32.

In order to take PIC 10C or Computer Science 32, students should complete **PIC 10A,10B** or **Computer Science 31**. **Mathematics 33A** is also strongly recommended for students interested in taking the core courses.

**Computer Science 121. Introduction to Bioinformatics and Genomics. (4)**

(Same as Chemistry CM160A.) Lecture, four hours; discussion, two hours. Recommended requisites: CS32 or Program in Computing 10C, and Biostatistics 100A or 110A or Mathematics 170A or Statistics 100A. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms.The course is intended for both students in engineering as well as students from the biological sciences and medical school. No prior knowledge of biology is required. Concurrently scheduled with course Computer Science CM221. P/NP or letter grading.

**Computer Science 122. Algorithms in Bioinformatics and Systems. (4)**

(Same as Chemistry CM160B) . Lecture, four hours; discussion, two hours. Recommended requisite: Computer Science 32 or Program in Computing 10C, and and Biostatistics 100A or 110A or Mathematics 170A or Statistics 100A. Development and application of computational approaches to biological questions. Focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. The computational techniques discussed include techniques from statistics and computer science. The course is intended for both students in engineering as well as students from the biological sciences and medical school. Concurrently scheduled with course Computer Science CM222. Letter grading.

**Computer Science 124. Computational Genetics (4)**

(Same as Human Genetics CM124.) Lecture, four hours; discussion, two hours. Recommended requisite: Computer Science 32 or Program in Computing 10C, and and Biostatistics 100A or 110A or Mathematics 170A or Statistics 100A. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving these problems using computational techniques. The computational techniques discussed include techniques from statistics and computer science. The course is intended for both students in engineering as well as students from the biological sciences and medical school. Concurrently scheduled with course Computer Science CM224. Letter grading.

**Introductory Seminar:**

The Introduction to Computational and Systems Biology seminar is a great way to get an introduction and overview of Computational Biology and Bioinformatics at UCLA. Students are encouraged to take this seminar as early as possible. This seminar course is a requirement for the Bioinformatics Minor.

**Computer Science M184. Introduction to Computational and Systems Biology. (2)
**(Same as Bioengineering M184 and Computational and Systems Biology M184.) Lecture, two hours; outside study, four hours. Enforced requisites: one course from 31, Civil Engineering M20, Mechanical and Aerospace Engineering M20, or Program in Computing 10A, and Mathematics 3B or 31B. Survey course designed to introduce students to computational and systems modeling and computation in biology and medicine, providing motivation, flavor, culture, and cutting-edge contributions in computational biosciences and aiming for more informed basis for focused studies by students with computational and systems biology interests. Presentations by individual UCLA researchers discussing their active computational and systems biology research. P/NP grading.

**Bioinformatics and Genomics Electives:**

These courses cover various topics in Bioinformatics, Genomics and Computational Biology. All of these courses are electives in the Bioinformatics Minor. Unlike the Bioinformatics core courses, many of these courses do not require the programming or statistics prerequisites. Thus these courses are often a good starting point for students in the life sciences interested in Bioinformatics.

**Chemistry ****C100. Genomics and Computational Biology. (5)**

Lecture, three hours; discussion, one hour. Requisite: course 153B. Introduction for biochemistry students of technologies and experimental data of genomics, as well as computational tools for analyzing them. Biochemistry and molecular biology dissected life into its component parts, one gene at time, but lacked integrative mechanisms for putting this information back together to predict what happens in complete organism (e.g., over 80 percent of drug candidates fail in clinical trials). High-throughput technologies such as sequencing, microarrays, mass-spec, and robotics have given biologists incredible new capabilities to analyze complete genomes, expression patterns, functions, and interactions across whole organisms, populations, and species. Use and analysis of such datasets becomes essential daily activity for biomedical scientists. Core principles and methodologies for analyzing genomics data to answer biological and medical questions, with focus on concepts that guide data analysis rather than algorithm details. Concurrently scheduled with course C200. P/NP or letter grading

**Computer Science C****M121. Introduction to Bioinformatics. (4)**

(Same as Chemistry CM160A.) Lecture, four hours; discussion, two hours. Enforced requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Prior knowledge of biology not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms. Concurrently scheduled with course CM221. P/NP or letter grading.

**Computer Science ****CM122. Algorithms in Bioinformatics and Systems Biology. (4)**

(Same as Chemistry CM160B.) Lecture, four hours; discussion, two hours. Enforced requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Course CM121 is not requisite to CM122. Designed for engineering students as well as students from biological sciences and medical school. Development and application of computational approaches to biological questions, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Computational techniques include those from statistics and computer science. Concurrently scheduled with course CM222. Letter grading.

**Computer Science ****CM124. Computational Genetics. (4)**

(Same as Human Genetics CM124.) Lecture, four hours; discussion, two hours; outside study, six hours. Enforced requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Concurrently scheduled with course CM224. Letter grading.

**Computer Science/Computational and Systems Biology CM186****. Computational Systems Biology: Modeling and Simulation of Biological Systems. (5)**

(Same as Bioengineering CM186 and Computational and Systems Biology M186.) Lecture, four hours; laboratory, three hours; outside study, eight hours. Corequisite: Electrical Engineering 102. Dynamic biosystems modeling and computer simulation methods for studying biological/biomedical processes and systems at multiple levels of organization. Control system, multicompartmental, predator-prey, pharmacokinetic (PK), pharmacodynamic (PD), and other structural modeling methods applied to life sciences problems at molecular, cellular (biochemical pathways/networks), organ, and organismic levels. Both theory- and data-driven modeling, with focus on translating biomodeling goals and data into mathematics models and implementing them for simulation and analysis. Basics of numerical simulation algorithms, with modeling software exercises in class and PC laboratory assignments. Concurrently scheduled with course CM286. Letter grading.

**Computational and Systems Biology ****M187. Research Communication in Computational and Systems Biology. (2 to 4)
**(Same as Bioengineering C M187 and Computer Science CM187.) Lecture, four hours; outside study, eight hours. Requisite: course M186. Closely directed, interactive, and real research experience in active quantitative systems biology research laboratory. Direction on how to focus on topics of current interest in scientific community, appropriate to student interests and capabilities. Critiques of oral presentations and written progress reports explain how to proceed with search for research results. Major emphasis on effective research reporting, both oral and written. Letter grading.

**Ecology and Evolutionary Biology C135. Population Genetics. (4)**

Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Strongly recommended: course 100, Mathematics 31A, 31B. Basic principles of genetics of population, dealing with genetic structure of natural populations and mechanisms of evolution. Equilibrium conditions and forces altering gene frequencies, polygenic inheritance, molecular evolution, and methods of quantitative genetics. Concurrently scheduled with course C235. Letter grading.

**Human Genetics C144. Genomic Technology. (4)**

Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Survey of key technologies that have led to successful application of genomics to biology, with focus on theory behind specific genome-wide technologies and their current applications. Concurrently scheduled with course C244. S/U or letter grading.

**Physiological Sciences 125. ****Molecular Systems Biology. (5)**

Lecture, three hours; discussion, one hour. Enforced requisites: Life Sciences 2, 3, 4, 23L. Quantitative description of molecular systems that underlie myriad phenotypes in living cells. Topics include various -omics fields and high-throughput technologies, network biology, and synthetic biology. Introductory lectures on molecular biology, emerging bioinformatic approaches, and systems modeling integrated with discussions of their applications in disease-related research. Review of recent literature to gain overall perspectives about new science of systems biology. Letter grading.