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Bioprocess Design

Code 12480
Year 1
Semester S2
ECTS Credits 6
Workload OT(30H)/PL(30H)
Scientific area Biotecnologia
Entry requirements There are no prerequisites in the Bioprocess Design Curricular Unit.
Mode of delivery Master's presentation of the classical theoretical foundations, experimental design methodologies and stratified resolution of the process from the selection of inputs/outputs, optimization to validation of proposed problems. Individual papers will be used to study the emerging tools of experimental design and neural networks, with subsequent presentation and theoretical discussion. Additionally, practical examples will be demonstrated, simulating an industrial platform context, in the construction of mathematical models of the usptream and downstream stage. At this stage it appeals to the creative and integrative development of the students.
Work placements Not applicable
Learning outcomes Identify and describe the fundamental principles that define typical bioprocesses. Understand the sustainable and global integration of a bioprocess. Understand the selection of the calculation basis. Model
the optimization and validation of biological systems by DOE and MATLAB. Identify and apply the main tools of experimental design in the upstream stage and downstream of a biotechnological process.
Learning outcomes of the course unit:
- Knowledge about modulation in biotechnology and be able to solve problems resultant of optimization and validation.
- Personal and professional attitudes like: thought, hypothesis construction, systemic, creative and critical thinking in order to operate programming tools in MATLAB and DOE.
- To know all the steps of the design and development of bioprocesses.
Syllabus OT: 1- The classic structure of a biotechnological process: upstream, downstream and final polishing.
2- Scale-up of fermenters focusing on the classical inputs (pH, temperature, culture medium, aeration and mass transfer coefficient (KLa), among others) and dimensioning to maximize the target output applying factorial design.
3- Scale-up of the downstream step: target parameters and sizing, applying factorial design in order to increase the yield and purity of the target product.
4- Bio informatics in the modeling, optimization and validation of the expression of bioproducts in typical biological systems. Application of
factorial design, neural networks and MATLAB, in order to increase the mass and volumetric productivities.
PL: Detailed analysis of case studies integration of bioprocesses: Design of penicillin production, production of chiral molecules of harmaceutical interest; Production of vitamins, among other examples.
Main Bibliography (1) Scientific articles in the fields of modeling and validation of the various stages of a bioprocess.
(2) Doran, P.M.; “BIOPROCESS ENGINEERING PRINCIPLES”; Academic Press, 1995.
(3) Hanselman and Littlefield, Mastering MATLAB 6: “A Comprehensive Tutorial and Reference”, Prentice Hall, 2001.
(4) Montgomery, D. C., Design and Analysis of Experiments, 5.ª ed., John Wiley & Sons, New York. 2001.
(5) Pedro AQ, Martins LM, Dias JM, Bonifácio MJ, Queiroz JA, Passarinha LA. An artificial neural network for membrane-bound catechol-O-methyltransferase biosynthesis with Pichia pastoris methanol-induced cultures. Microb Cell Factories., 2015, 7; 14:113. doi: 10.1186/s12934-015-0304-7.
(6) Almeida AM, Queiroz JA, Sousa F, Sousa A. Optimization of supercoiled HPV-16 E6/E7 plasmid DNA purification with arginine monolith using design of experiments. J Chromatogr B Analyt Technol Biomed Life Sci., 2015, 26:145-50. doi: 10.1016/j.jchromb.2014.12.004.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-05-18

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