CBE 5734
Transcript Abbreviation:
Molec Informatics
Course Description:
Overview of molecular informatics, with emphasis on its use in modern drug discovery and exploration of chemical structure-property relationships.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
3.00
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Sr or Grad standing in Engineering, or permission of instructor.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 734.
Course Goals / Objectives:
Understand the basic concepts of chemical and biomolecular informatics, with emphasis on how these impact areas relevant to chemical and biomolecular engineers
Understand recently-developed high-throughput experimental techniques commonly used in chemical and biochemical processes, and how these require new and improved ways of analyzing the vast quantities of data generated by these methods
Become familiar with public databases for chemical and biological data, and methods for extracting information from these. Emphasis on internet resources for engineers interested in informatics
Introduce common statistical methods used in informatics, including sequence analysis, multivariate regression, pattern analysis, hierarchical organization of data, recursive partitioning
Explore a specific case study to learn how informatics techniques are used to explore chemical toxicity
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Overview of databases for chemical information. Representation of the chemical structures of molecules (SDF and MOL files, SMILES code, graph theory approaches, etc.) | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Methods for evaluating the similarity between chemical compounds: substructure representation, cluster analysis, recursive partitioning, and other methods | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Use of molecular descriptors and physicochemical properties in chemoinformatics | 7.0 | 0.0 | 0 | 0.0 | 0 |
| Nucleic acids, genes and genomes; amino acids, proteins and proteomes; classifying amino acids based on their chemical properties; overview of gene and protein databases | 8.0 | 0.0 | 0 | 0.0 | 0 |
| Sequence analysis: alignment of gene and protein sequences (dot matrix plots, scoring matrices, gap penalties, FASTA and BLAST algorithms) | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Markov chain analysis of single sequences, brief introduction to hidden Markov models (HMM) | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Overview of high throughput screening experiments and data analysis: microarray experiments to determine gene expression profiles and HTS chemical toxicity studies | 5.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 43 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 30% |
| Exams | 40% |
| Final Exam | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: