Metabolic Engineering
Metabolic engineering

KMBN05, 7.5 credits, A (Second Cycle)

Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED B/K
Date of Decision: 2024-04-17
Effective: 2024-05-08

General Information

Main field: Biotechnology Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective for: B4-mb, B4-pt, MBIO2
Language of instruction: The course will be given in English

Aim

Based on previous knowledge obtained from courses, particularly in gene technology, mathematics and microbiology, the course aims to introduce the students to the field of metabolic engineering, discuss tools and strategies used in the field and give the students insight in how the technology can be used in industrial biotechnology.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

• understand the fundamentals of metabolic engineering tools (including mass balances, metabolic flux modeling, metabolic control analysis, omics)
• understand how these tools can be applied for the analysis of metabolically engineered microorganisms
• have acquired in depth knowledge on metabolic pathways and their regulation and on how metabolic pathways can be engineered for the improvement of various bioprocesses.

Competences and skills
For a passing grade the student must

• Obtain skills to show ability to carry out relevant calculations concerning mass balances, metabolic flux analysis, metabolic control analysis in biological systems
• Obtain skills to show ability to analyse experimental work aiming at characterising genetically modified microorganisms and corresponding control organisms
• Obtain skills to show ability to understand and master the terminology in the field in verbal and written form and to organise and structure the scientific content into a whole
• Obtain skills to show ability to critically report, both in oral and writing forms results from scientific papers and/or his/her own experimental work

Judgement and approach
For a passing grade the student must

• show ability to do assessments of other student and researcher contributions in the field of metabolic engineering
• show ability to relate and distinguish between evidence and argument

Contents

• the course includes lectures on the following topics: strain engineering, advanced genetic tools, metabolic pathways, metabolic control analysis, metabolic flux analysis, omics and systems biology.
• the literature study consists of a thorough study of research strategies on a specific topic within metabolic engineering.
• in the lab simulation, the objective is to critically evaluate data arising from a set of experiments where a biofuel or bulk chemical is produced from sugars with wild-type and genetically engineered microorganisms. This includes the analysis of growth and product formation, enzyme activity and omics data, among others.
• exercises will be performed on carbon balance, redox balance, flux analysis and metabolic control analysis.

Examination details

Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment:

A written examination, a graded oral presentation based on a literature study and a graded lab simulation report and/or presentation.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Modules
Code: 0117. Name: Metabolic Engineering.
Credits: 7.5. Grading scale: TH - (U, 3, 4, 5).

Admission

Admission requirements:

• (KBK041 Gene Technology or KBKF01 Gene Technology or KBTN20 Molecular Biotechnology)
    and
  (KMB060 Microbiology or KMBA01 Microbiology or KMBF05 Food Microbiology)

Reading list

• Online compendium in metabolic engineering.

Contact

Course coordinator: Ed van Niel, ed.van_niel@tmb.lth.se
Teacher: Magnus Carlquist, magnus.carlquist@tmb.lth.se
Course homepage: https://www.tmb.lth.se

Further information

The course contains obligatory sessions: introduction lecture, lab simulation sessions, literature study presentation.

The course is based on independent work. The use of generative artificial intelligence (GAI) is permitted for self-study but may not be used for examination activities such as laboratory reports, assignments, assignments and exams.