Fábricas Celulares Microbianas

Objectives

The course aims at familiarizing students with state of the art approaches and technologies used to engineer microbes for application in biotechnological processes with a strong societal impact. In specific, the use of genetic/genomic engineering approaches, as well as metabolic engineering strategies and high-throughput screening methods of best-performing microbial strains, will be detailed. Emphasis will be put on the use of computational tools to support the strain engineering strategies, the implementation of "new-to-nature" prospection pathways and the strain screening.The advantages, applications and limitations of the different approaches will be put in context with their real application through invited seminars that will be lectured by researchers coming preferably from the industrial setting.

Syllabus

Overview on key metabolic pathways with impact in microbe-based biotechnology. Principles of in silico metabolic simulation: constraint-based modelling based on flux-balance analysis and optimization algorithms used for strain engineering (bi-level and bi-objective frameworks). Exploration of genetic networks' modelling in microbe engineering. Synthetic biology tools for in silico pathway/enzyme prospecting/evolution to implement microbe-based synthesis of "new-to-nature" chemicals. Genetic/genomic engineering approaches to improve performance of microbial hosts: directed evolution, genome shuffling, phenomics and metabolomics as means to obtain information that can be used for the rational engineering of microbial catalysts. Metabolic engineering approaches to improve balance of cofactors, redox potential, substrate-to-product ratio and transport engineering. Design and optimization of biosensors as tools for high-throughput screening of best performing microbial strains.

Prerequisites

Students taking this course should have basic knowledge on Molecular Biology, Biochemistry and Genetics, as well as basic computational skills in MatLab. For students holding a BSc degree not related to Life Sciences, the frequency of the CU "Introduction to Biological Sciences" is highly recommended.

Cross Competence Component

This CU aims to promote the before detailed soft skills, the evalution of these accounting for 15% of the course grade : 1- critical thinking and problem solving: the practical classes and the research project developed by the students will put them in contact with relevant biotechnological bottlenecks for which they will need to devise, together with the supervising teacher, suitable strategies that will be further discussed in terms of implementation, advantadges and disadvantages. 2- Interpersonal skills: the lab classes and research project will be conducted in groups of 3 students, requiring the development of leadership, team work and conflict management skills. Oral and writen communication skills will be stimulated by the writen documents and the oral presentation predicted for the UC. 4 - Global citizenship: The proposed problems addressed in the course aim at solving problems with a strong societal impact including environmental sustainability or new healthcare challenges.

Laboratorial Component

The laboratory component will include "in silico" and "wet lab" classes. L1. Introduction to modeling of metabolic networks in model systems (3 computational classes) L2. Optimization of ethanol production by S. cerevisiae cells, as suggested by optimization algorithms for strain engineering and metabolic simulation (1 computational class+3 wet lab classes) L3. In silico tools for pathway/enzyme prospecting (2 computational classes) L4. Modelling of genetic networks and its use in microbe engineering (1 computational class) L5. Use of biosensors (based on fluorescence) to phenotype libraries of yeast and bacterial strains for increased production of add-value chemicals (2 wet lab classes) L6. Free wet-lab for validation of the results obtained in the research projects developed by the students (2 classes)

Programming And Computing Component

The course is settled in the utilization of Matlab for constraint-based modelling of the microbial metabolic networks. The use of multiple optimization algorithms is also envisaged working in a bi-level (e.g. Optknock or OptGene) or in a bi-objective (e.g. MoMo) framework. Computational tools for pathway (e.g. ReactPred or MetaCyc) and for enzyme prospecting (e.g. Mines or Brenda) is also predicted.

Ethical Principles

All members of a group are responsible for the group’s work In any assessment every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be alble to present and answer questions about the entire assignment and solution.