Saint Louis University’s program in bioinformatics and computational biology provides an interdisciplinary curriculum that combines a variety of required and elective courses from relevant fields.
BCB 5100: Advanced Scripting for the Life Sciences
This course explores intermediate programming techniques, with a focus on combining the use of various software packages and existing tools to facilitate the gathering, processing and visualization of biological data sets.
BCB 5200: Introduction to Bioinformatics I
The course focuses on the study of nucleotide and peptide sequences and structures from a computational perspective. Topics include sequence alignment, detecting and understanding mutations, gene identification, and structural comparison and prediction.
BCB 5250: Introduction to Bioinformatics II
This course focuses on the study of interaction and evolution of biological sequences and structures. Topics include interaction networks, clustering, phylogenic trees and how biological systems change at the genomic.
BCB 5300: Algorithms in Computational Biology
This course introduces the foundations of algorithmic techniques and analysis, as motivated by biological problems. Topics include dynamic programming, tree and graph algorithms, sequence analysis and hidden Markov models. Motivations include sequence alignment, motif finding, gene prediction and phylogeny.
BCB 5810: Bioinformatics Colloquium
The course provides students with current information about studies in bioinformatics and computational biology through presentations given by faculty members, students and invited speakers. Students who enroll for credit must present a 20-to-30 minute talk as part of the seminar, demonstrating their oral communication skills while presenting technical content.
BCB 5910: Internship in Bioinformatics
Internships will include experiences in research and development laboratories of local biotechnology companies, as well as in research laboratories in SLU's departments of biology, chemistry, computer science, mathematics, and in departments in the School of Medicine. Prerequisites: BCB 5200 and 5250 courses in Introduction to Bioinformatics I and II.
BCB 5930: Topics in Bioinformatics
BCB 5970: Research Topics
This course will provide research experiences in SLU's departments of biology, chemistry, computer science, mathematics and statistics, and in departments in the School of Medicine.
BIOL 4911: Integrated Bioinformatics Internship
Students will work with laboratories conduction molecular biology/bioinformatics research to gain practical experience. Internships will include research and development laboratories of local biotechnology companies, and in the departments of Biology, Chemistry, or Mathematics & Computer Science.
BIOL 5030: Genomics
This course introduces core concepts, techniques and analytical methods of genomics. The topics of this course include: genome projects; structure, components and evolutionary dynamics of genomes; sequencing, mapping and assembly techniques; online resources, databases and analytical methods for genomic studies.
BIOL 5050: Molecular Technique Lab
Students will learn principles of molecular biology and recombinant DNA technology, and will gain hands-on experience with nucleic acid isolation, cloning, sequencing, and analysis.
BIOL 5060: Advanced Topics in Molecular Biology
This course encompasses the central roles of DNA and RNA in molecular biology and the technologies used to analyze and manipulate nucleic acids in biomedical research. Topics to be covered are the structure, topology, and arrangement of nucleic acids in genomes, recombinant DNA technology, bioinformatics, and current research in molecular biology.
BIOL 5070: Advanced Biological Chemistry
An in-depth analysis of selected topics in biological chemistry. Topics may include for example, protein structure and function, and nucleotides and nucleic acids.
BIOL 5090: Biometry
This course is intended for graduate students in biology. The course will cover the description of biological distributions and probabilities; the application of hypothesis testing, including the relationship between biological and statistical hypothesis; the nature of biological data, samples and sampling regimes, and how these fit within the scientific method. Central to the course is the use of biological models and experiments.
BIOL 5100: Cellular and Molecular Genetics
The cellular and molecular basis of genetically controlled biological phenomena from microorganisms to complex, multicellular organisms. Topics considered are transcriptional regulation in prokaryotes and eukaryotes, chronomatin structure and function, sporulation, yeast mating types, pattern formation in Drosophila, sex determination, and genetic control of development in C. elegans. Experimental methods used to study these events will be stressed.
BIOL 5170: Introduction to GIS
This class introduces concepts, science and theory of GIS with hands-on experiences. After successful completion of the course, students will be able to demonstrate fundamental techniques of geospatial analysis and mapping.
BIOL 5180: Intermediate GIS
This course covers intermediate and advanced topics in GIS including remote sensing for GIS, geospatial statistics and GIS biography. Each part is instructed by a professor specialized in the particular area.
BIOL 5190: GIS in Biology
BIOL 5700: Advanced Molecular Biology
Current problems in plant morphology and systematics. Library/laboratory phase stressed.
BIOL 5780: Molecular Phylogenetic Analysis
This course is designed to give students the knowledge and technical competence necessary for working with molecular phylogenetic data. Students will learn how to edit and align sequence data, and will explore how alternative alignments affect phylogenetic reconstructions. They will learn how to access and download data from online databases such as Genbank and Tree Base. Differing analytical approaches will be presented and discussed, including current and ongoing controversies in the primary literature. Students will gain experience using numerous software packages for analyzing data, testing constraints, choosing likelihood models, assessing support and exploring character evolution.
BIOL 5970: Research Topics
CHEM 4610: Biochemistry I
An upper-level, one semester, undergraduate course focusing on biomolecules. Topics to be covered include biological buffers, thermodynamics, amino acids, proteins, carbohydrates, lipids, membranes, nucleic acids, recombinant DNA, enzymes, and molecular motors.
CHEM 4615: Biochemistry I Laboratory
This laboratory is intended to introduce the students to many of the important techniques employed by biochemists including but not limited to buffers, titrations, spectrophotometry, ion exchange chromatography, thin layer chromatography, quantification of protein concentration, electrophoresis, and enzyme kinetics.
CHEM 4620: Biochemistry II
An upper-level, one semester, undergraduate course focusing on metabolism and information transfer. Topics to be covered include glycolysis, citric acid cycle, electron transport, oxidative phosphorylation, photosynthesis, synthesis and degradation of biomolecules, transcription, replication, and translation.
CHEM 4625: Biochemistry II Laboratory
An upper level undergraduate laboratory surveying advanced components of biochemistry. This laboratory introduces many of the advanced techniques employed by biochemists including but not limited to isolation and characterization of enzymes, NMR, ligand binding, recombinant DNA techniques, X-ray crystallography, PCR, and computer modeling.
CHEM 5370: Computational Chemistry
A description of the theory and practice of computational methods used in modern chemical research. Students gain knowledge of computational methods through classroom instruction and semester-long projects focused on a molecular system of their choice. Molecular calculations are performed using Gaussian 03 on a supercomputer.
CHEM 5970: Research Topics
CSCI 5150: Computational Geometry
The goal of computational geometry is to find efficient algorithms for solving geometric problems. Topics include convex hulls, Voronoi diagrams, Delaunay triangulations, geometric search and geometric data structures.
CSCI 5710: Databases
This course introduces the foundations of database systems: the relational model, file organization and indexes, relational algebra, structured query language, the entity model, normalization, object databases.
CSCI 5750: Machine Learning
This course introduces students to the field of machine learning with emphasis on the probabilistic models that dominate contemporary applications. Students will discover how computers can learn from examples and extract salient patterns hidden in large data sets. The course will introduce classification algorithms that predict discrete states for variables as well as regression algorithms that predict continuous values for variables. Attention will be given to both supervised and unsupervised settings in which (respectively) labeled training data is or is not available. Emphasis is placed on both the conceptual relationships between these different learning problems as well as the statistical models and computational methods used to employ those models.
CSCI 5830: Image Processing
This course will introduce the fundamentals of image processing and computer vision, including image models and representation, image analysis methods such as feature extraction (color, texture, edges, shape, skeletons, etc.), image transformations, image segmentation, image understanding, motion and video analysis, and application-specific methods such as medical imaging, facial recognition, and content-based image retrieval.
CSCI 5850: High-Performance Computing
Use processor features, multiple cores, memory, graphics cards and clusters to maximize efficiency of computer software. Topics include vectorizing code, cache and memory efficiency, multithreaded programming, gpu programming and distributed programming.
CSCI 5930: Topics in Computer Science
CSCI 5970: Research Topics
Independent work with faculty.
MATH 4850: Mathematical Statistics
Descriptive statistics, probability distributions, random variables, expectation, independence, hypothesis testing, confidence intervals, regression and ANOVA. Applications and theory. Taught using statistical software.