Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After completing the course students will be able to:
1. build kinetic simulation models of the cell growth and product formation
2. connect different models together to build a bioprocess model
3. define parameters for kinetic and static bioprocess models
4. create experimental designs for bioprocess screening and optimization tests
5. create response surface models and define optimum variable values thereof
6. create multivariate models from various data sources including e.g. raw materials, cultivation conditions, product properties, expression data
7. utilize certain chemometric modelling approaches for bioprocess estimation and simulation simulations
8. arrange simple experiments to find out certain kinetic and optimization parameters of a bioprocess
9. estimate the model validity in various cases

Credits: 5

Schedule: 08.09.2020 - 22.10.2020

Teacher in charge (valid 01.08.2020-31.07.2022): Tero Eerikäinen

Teacher in charge (applies in this implementation): Tero Eerikäinen

Contact information for the course (valid 13.08.2020-21.12.2112):

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    Bioprocess behavior in different modes and modeling principles combined to experimental works. Computer-aided bioprocess modeling and simulation. Creating bioprocess models in MATLAB and Simulink environment. Linear and non-linear estimation of the kinetic parameters for types and models. Multivariate modeling possibilities and limitations. Response surface modeling, principal component analysis, neural networks. Use of models as a part of Quality control as process analytical technique. Creating a bioprocess simulation model and validating parameter values from experimental data.

  • Applies in this implementation:

    Due to corona situation, experimental work means data handling and simulations with real experimental data.

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Lectures, computer exercises, experimental work, assignments and independent studying

  • Valid 01.08.2020-31.07.2022:

    Total 135 h = 5cr
    Lectures and exercises 24 h, 2x2 h per week
    Experimental work 35 h
    Assignments 12 h
    Independent studying 60 h
    Exam 4 h


Study Material
  • Valid 01.08.2020-31.07.2022:

    Material to be announced

  • Valid 01.08.2020-31.07.2022:

    CHEM-E3130 Biolab II or similar

    Laboratory safety course CHEM-A1010 or CHEM-E0140 (or, alternatively, occupational safety section, which has been taught courses CHEM-A1000 or CHEM-E0100 before the academic year 2017-2018) must be completed before starting the laboratory work.

SDG: Sustainable Development Goals

    6 Clean Water and Sanitation

    7 Affordable and Clean Energy

    9 Industry, Innovation and Infrastructure

    13 Climate Action