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Synthetic Biology and Metabolic Engineering

Code: 43330 ECTS Credits: 6
Degree Type Year Semester
4314579 Biological and Environmental Engineering OT 1 2


Pau Ferrer Alegre

Teaching groups languages

You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.


Joan Albiol Sala
Pau Ferrer Alegre


Student must have medium level knowledge in Mathematics, Chemistry, Microbiology and basic level of Molecular biology.

Objectives and Contextualisation

The main objective of this module is to explore, acquire a high level of comprehension and be able to evaluate the different emerging methodologies in the fields of Synthetic Biology, Systems Biology and Metabolic Engineering. This includes the different '-omics' platforms for the integrated, global and quantitative analysis of cell physiology as a knowledge base for the Enzyme and Metabolic Engineering. That is for the rational design and improvement of cell lines, microorganisms or enzymes with the goal of its Industrial or Therapeutic application. 

Learning Outcomes

  • CA17 (Competence) Assess the different emerging methodologies in the fields of synthetic biology, systems biology and metabolic engineering.
  • CA18 (Competence) Combine analytical and computational methodologies and instruments for quantitative analysis, processing and modelling of big data (?omic' platforms and systems biology) on organisms or parts thereof.
  • KA16 (Knowledge) Identify emerging technologies in the fields of synthetic biology and systems biology.
  • SA20 (Skill) Develop criteria for the combined use of non-targeted and targeted engineering techniques (metabolic engineering, synthetic biology).
  • SA21 (Skill) Investigate the applicability of ?omic' platforms for the acquisition of physiological data targeted at the experimental design of strategies for the improvement of cell factories.
  • SA22 (Skill) Use one's own methodologies for the design and rational improvement (synthetic biology and metabolic engineering) of enzymes, organisms and cell lines for industrial application.


1.- Omic platforms: Application of Systems Biology '-omic' analytical tools  - from genomics, transcriptomics, metabolomics and fluxomics- to the engineering of industrial microorganisms.

2.- Metabolic Engineering and systems Biology: 'bottom-up' analysis and modelling of metabolism/cell function. Metabolic control theory.  In-silico design of directed genetic modifications. 'top-down', analysis from analytical 'omic' platforms, including data treatment and its multilevel analysis. Global metabolic analysis using in-silico genome-scale metabolic models. Study cases: Applications of metabolic engineering and systems biotechnology to the improvement of strains producing small molecules (amino acids, antibiotics, etc.) and/or obtain robust strains adapted to industrial conditions (high levels of toxic compounds, etc.).

3.- Applied synthetic biology: construction and design of new industrial microorganisms or their parts - for example construction of new metabolic routes- to obtain cell factories and/or biocatalyzers for the efficient production of biological compounds, new generation biofuels  (butanol, etc.), APIs, industrial enzymes or therapeutic proteins.

4.- High throughput technologies: Application of non-directed techniques (combined with metabolic engineering strategies) for the optimization of enzymes, organisms and industrial cell lines: directed evolution, mutagenesis, 'library screening', etc. Case studies: Production of enzymes tolerant to solvents, pH, high temperature etc. Obtain robust industrial strains. Case studies: Tolerance to Ethanol, phenolic compounds, high osmolarity, etc.


The teaching methodology that will be used throughout the learning process is based essentially on the work of the student and the faculty professors will be  responsible for helping them both with regards to the acquisition and interpretation of information related to the subject as well as in the direction of their work. In general, training activities will be distributed in the following types:

Lecture classes: They will be used to provide students with the basic conceptual elements and the minimum information necessary so that they can then develop autonomous learning. IT resources will be used (ppt or pdf slides) that will be available to the student on the virtual platform.

Practical computer classes: Part of the competencies of the subject will be acquired by means of the computer practical classes. In this case, the most convenient software will be used in order to better understand the behavior of biological systems, carry out various analyses, as well as be able to design and test in-silico, various strain improvement methodologies as a prior step to its application in the laboratory. Practical exercices will be delivered trough virtual campus. If not delivered, the practical graded exercices mark will be penalyzed.

Group work: Small groups of students will also be assigned a group work based on a scientific publication that will be presented to classmates.

Individual tutor appointments: Individual tutor appointments can be done at the request of the students. The objective of these will be, for example, the clarification of doubts and/or orientation on the available sources of information.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Title Hours ECTS Learning Outcomes
Type: Directed      
Lecture classes 22 0.88 CA17, CA18, KA16, SA20, SA21, SA22, CA17
Practical classes (computer) 16 0.64 CA18, SA22, CA18
Type: Supervised      
Preparation of team work, solving exercises or practical cases 7 0.28 CA17, KA16, SA20, SA21, CA17
Type: Autonomous      
Personal study 39 1.56 CA17, KA16, SA20, SA21, SA22, CA17
Preparation of team work, solution of exercises or practical cases 58 2.32 CA17, CA18, KA16, SA20, SA21, SA22, CA17


Assessment activities:

- Activity A: Delivery of practical graded exercises with computer: 30% on the final grade

- Activity B: Oral defense of team work: 30% on the final grade

- Activity C: Theory or practical exams: 30% on the final grade

- Activity D: Attendance and active participation to the classes: 10% on the final grade

Keep in mind that activity D is not recoverable. Therefore, any recovery activity will not allow achieving the maximum grade.

Programming evaluation activities: The dates of the written tests and the submission of exercises and team work will be published in the academic calendar or on the virtual campus and may be subject to possible  schedule changes for reasons of adaptation to unforeseen events. Always be informed through the virtual platform about these changes as it is understood that this is the usual platform for exchange of information between faculty professors and students.

For each type A or C assessment activities, a place, date and time in which the student will be able to review the activity with the professor will be indicated. If the student does not show up for this revision, this activity will not be reviewed later.

Recovery: In case of not passing the subject of this course by the previous procedure, a synthesis test is foreseen as a necessary assessment activity for passing the course. Keep in mind that the highest possible score in this test is ‘Notable’. In order to access the recovery test it is necessary to have attended at least two thirds of the assessment activities.

No Evaluable: Any student who fails to present at least two-thirds of the assessment activities described previously will be rated as 'No Evaluable' meaning that the progress cannot be assessed.

Awarding an honor grade (MH) is the decision of thefaculty members responsible for the course. The UAB regulations indicate that the MH can only be granted to students who have obtained a final grade equal to,or greater than, 9.0 but the professorship can fix a higher minimum level if there are more candidates than the maximum number of honors available, or request for complementary activities. The number of MH that can be given is limited up to the 5% of the total number of students enrolled in this course.

Plagiarism: Without prejudice to other disciplinary measures deemed appropriate, and in accordance with current academic regulations, any irregularities committed by a student that may lead to a variation of the grade of evaluation activity will be scored with a zero. Therefore, copying, plagiarism, cheating, allowing someone to copy, etc. in any of the assessment or evaluation activities, will involve failing it and will receive a zero mark. The evaluation activities qualified in this way and by this procedure will not be recoverable. If it is necessary to pass any of these evaluation activities to pass the subject, this subject will be failed directly, with no opportunity to recover it in the same course. In this case, the grade of the subject will be 3.5.

Evaluation of students enrolled in previous courses: As of the second enrollment, the evaluation of the subject will consist of a synthesis test. Alternatively, the final grade of the subject can be calculated as the average of activities A, B and C. In order to be eligible for this type of evaluation, the repeating student must request it to the faculty members via e-mail at the very late 8 days after the start of the classes of this course.

Please refer to the Catalan or Spanish version of this document.

This course does not offer a single assessment


Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Class participation (activity D) 10 0 0 CA17, CA18, KA16, SA20, SA21, SA22
Exercises delivered (activity A) 30 0 0 CA17, CA18
Team work oral presentation (activity B) 30 5 0.2 CA17, CA18, KA16, SA20, SA21, SA22
Theory or practical exams (activity C) 30 3 0.12 CA17, KA16, SA20, SA21, SA22


Alon, U. An Introduction to Systems Biology. Design principles of biological circuits. Second edition. Chapman & Hall/CRC. 2019. (https://web-s-ebscohost-com.are.uab.cat/ehost/ebookviewer/ebook?sid=af22999a-df3d-4af7-8dd4-c4f7a02ee229%40redis&vid=0&format=EB#)
Glieder, A., Kubicek, C.P., Mattanovich, D., Wiltschi, B., Sauer, M. (Eds). Synthetic Biollogy.  Springer e-book, 2016. (https://link-springer-com.are.uab.cat/book/10.1007/978-3-319-22708-5 )
Klipp, E., R. Herwig, A. Kowald, C. Wierling, i H. Lehrach. Systems Biology in Practice. Concepts implementation and application. Weinheim: Wiley-VCH, 2005. (https://onlinelibrary-wiley-com.are.uab.cat/doi/book/10.1002/3527603603)
Klipp, E., W. Liebermeister, C. Wierling, A. Kowald; Systems Biology. A textbook 2nd. Weinheim: Wiley-VCH, 2016.
Klipp, E., W. Liebermeister, C. Wierling, A. Kowald, H. Lehrach, Herwig R. Systems Biology. A textbook. Weinheim: Wiley-VCH, 2009.
Nielsen, J.; Hohmann, S. Systems Biology.  Wiley-Blackwell. 2017  (https://onlinelibrary-wiley-com.are.uab.cat/doi/book/10.1002/9783527696130)
Palsson, B.O. Systems Biology. Properties of reconstructed networks. Cambridge: Cambridge University Press, 2006.
Palsson, B.O. Systems Biology. Simulation of dynamic network states. Cambridge: Cambridge University Press, 2011.
Stephanopoulos G.N. Aristidou A.A. Nielsen J. Metabolic Engineering. Principles and Methodologies. Academic Press. San Diego. USA,  1998 (https://www-sciencedirect-com.are.uab.cat/book/9780126662603/metabolic-engineering)
Szallasi, Z., V. Periwal, i J. Stelling, . System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. The MIT Press, 2006.


- COPASI (http://copasi.org/)

- Optflux (http://www.optflux.org/)

- Microsoft Excel

- Matlab (https://es.mathworks.com/academia/tah-portal/universitat-autonoma-de-barcelona-40811157.html)