BIO310 Bioinformatics
BIO310 Bioinformatics
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2025
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Genetics and Bioengineering
Mohamed Ibrahim
Course Lecturer
Course Objectives
The course aims : - Apply and use computer Bioinformatics softwares and databases website in order to solve problems related to the protein engineering and bioinformatics - Apply knowledge to solve Biological problems in different fields of interest, - Use the knowledge as base for further inquiry and exploration, - Have substantial competency in content, skills, and awareness within the field of bioinformatics
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Understanding bioinformatics, Zvelebil and O. Baum - Garland Science. 2007.
Additional Literature
NATeaching Methods
Class presentations and discussions with examples will be held online through Microsoft TEAMS
Online Active tutorial sessions for engaged learning
Team assignments
Team projects that involve real data, computer analysis, summary, interpretation and reporting
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Motivation and introduction to biology | Ch.1, Ch.2 |
| 2 | Sequence analysis | Ch.3 |
| 3 | Sequence analysis | Ch.4 |
| 4 | Sequence analysis | Ch.4 |
| 5 | Pairwise sequence alignment | Ch.5 |
| 6 | Multiple sequence alignment | Ch.6 |
| 7 | Recovering Evolutionary History | Ch.7 |
| 8 | Phylogenetic trees, clustering methods | Ch.8 |
| 9 | Protein structures | Ch.11 |
| 10 | Protein structures | Ch.11 |
| 11 | Protein structures | Ch.12 |
| 12 | Structure prediction (secondary, tertiary) | Ch.13 |
| 13 | Motifs, active sites, docking | Ch.14 |
| 14 | analysing structure - function relationships | Ch.14 |
| 15 | Revision |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| BIO310.1 | Course | Thursday 09:00 - 11:50 | A F1.25 | - | - |
| BIO310.1 | Tutorial | Friday 18:00 - 18:50 | A F1.3 - Computer Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 12:00 - 15:00 | A F1.14 | |
| Tuesday | 10:00 - 12:00 | A F1.14 | |
| Wednesday | 10:00 - 12:00 | A F1.14 | |
| Thursday | 10:00 - 12:00 | A F1.14 | |
| Friday | 10:00 - 12:00 | A F1.14 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Active tutorials
AI: Not AllowedAlignment with Learning Outcomes : 1 2 4
In-term exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Term project and presentation
AI: Not AllowedAlignment with Learning Outcomes : 2 4
Assignments
AI: Not AllowedAlignment with Learning Outcomes : 2 3 4
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
42 hours ⏳ (14 week × 3 h)
Assignments
10 hours ⏳ (2 week × 5 h)
Active tutorials
14 hours ⏳ (14 week × 1 h)
Home study
42 hours ⏳ (14 week × 3 h)
In-term Exam Study
10 hours ⏳ (1 week × 10 h)
Final exam study
12 hours ⏳ (1 week × 12 h)
Term project/presentation
20 hours ⏳ (2 week × 10 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [BIO310] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Oct 10, 2025 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| BIO310 | Bioinformatics | 3 | 1 | 6 | Thursday 09:00 - 12:00 | |||||
| Prerequisite | NS103 | It is a prerequisite to | - | |||||||
| Lecturer | Mohamed Ibrahim | Office Hours / Room / Phone | Monday: 12:00-15:00 Tuesday: 10:00-12:00 Wednesday: 10:00-12:00 Thursday: 10:00-12:00 Friday: 10:00-12:00 |
|||||||
| mragab@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The course aims : - Apply and use computer Bioinformatics softwares and databases website in order to solve problems related to the protein engineering and bioinformatics - Apply knowledge to solve Biological problems in different fields of interest, - Use the knowledge as base for further inquiry and exploration, - Have substantial competency in content, skills, and awareness within the field of bioinformatics |
|||||||||
| Textbook | Understanding bioinformatics, Zvelebil and O. Baum - Garland Science. 2007. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class presentations and discussions with examples will be held online through Microsoft TEAMS. online Active tutorial sessions for engaged learning. Team assignments. Team projects that involve real data, computer analysis, summary, interpretation and reporting. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Motivation and introduction to biology | Ch.1, Ch.2 | ||||||||
| Week 2 | Sequence analysis | Ch.3 | ||||||||
| Week 3 | Sequence analysis | Ch.4 | ||||||||
| Week 4 | Sequence analysis | Ch.4 | ||||||||
| Week 5 | Pairwise sequence alignment | Ch.5 | ||||||||
| Week 6 | Multiple sequence alignment | Ch.6 | ||||||||
| Week 7 | Recovering Evolutionary History | Ch.7 | ||||||||
| Week 8 | Phylogenetic trees, clustering methods | Ch.8 | ||||||||
| Week 9 | Protein structures | Ch.11 | ||||||||
| Week 10 | Protein structures | Ch.11 | ||||||||
| Week 11 | Protein structures | Ch.12 | ||||||||
| Week 12 | Structure prediction (secondary, tertiary) | Ch.13 | ||||||||
| Week 13 | Motifs, active sites, docking | Ch.14 | ||||||||
| Week 14 | analysing structure - function relationships | Ch.14 | ||||||||
| Week 15 | Revision | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Active tutorials | 7 | 15 | 1,2,4 | Not Allowed | |
| In-term exam | 1 | 25 | 1,2,3,4 | Not Allowed | |
| Term project and presentation | 2 | 10 | 2,4 | Not Allowed | |
| Assignments | 2 | 10 | 2,3,4 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Assignments | 5 | 2 | 10 | |||
| Active tutorials | 1 | 14 | 14 | Home study | 3 | 14 | 42 | |||
| In-term Exam Study | 10 | 1 | 10 | Final exam study | 12 | 1 | 12 | |||
| Term project/presentation | 10 | 2 | 20 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 06/10/2025 | |||||||||