Next Generation Bioprocessing and Role of AI-Automation in Bioprocessing

1. Bioprocess Equipment Design
and Economics
(BTMT-601)
Submitted By:-
Ilika Kaushik
(19PBT004)
M.Tech Biotechnology,
3rd Sem,
SoBT, GBU.
Submitted To:-
Dr. Barkha Singhal
Asst. Professor
SoBt, GBU

2. topic:-
Next Generation Bioprocessing
Role of AI and Automation in Bioprocess
Industry

3. Part I:-
Next Generation Bioprocessing

4. Introduction
• Tissue engineering is the science of repairing, replacing and enhancing
functional properties of biological tissue.
• Tissue engineered constructs can act as model systems.
• The development of in vitro tissue models allows predictions.
• When engineering a tissue, recreating and controlling the overall
cellular microenvironment is essential as this can strongly influence
cell behavior.

7. The Extra Cellular Matrix (ECM)
• A major component of tissue volume is the extracellular space, which
contains a network of extracellular matrix (ECM) proteins and
polysaccharides that surround the cells.

8. Tissue Engineering
• Tissue engineered systems can be described as the 3D porous solid
biomaterials where cells can be seeded and the scaffold construct will
act as template for tissue regeneration to guide the growth of new
tissue.

9. Scaffold Materials
Synthetic
• More stable
• Cost effective
• Can be produced under controlled
conditions, and exhibit predictable and
reproducible mechanical and physical
properties, such as tensile strength,
elastic modules and degradation rate.
• Examples of synthetic polymers used
for scaffolds include: poly vinyl
chloride, poly caprolactone, poly lactic
acid, poly lacticco- glycolic acid and
poly ethylene terephthalate.
Natural Polymer
• Relatively less stable
• Relatively expensive
• Natural polymers are
derived from living sources
such as the human body or
animals.
• Examples of natural
polymers used for scaffolds
include gelatin, collagen,
fibrinogen and elastin.

10. Scaffold Fabrication Techniques
• Solvent casting/particulate leaching process

11. • Melt Molding

12. • Freeze Drying

13. • Thermal induction phase separation

14. A) Gas Foaming
B) Electrospining
C) 3D Printing
Scaffold Fabrication Techniques

15. Bioreactors
• Bioreactors complement the use of scaffolds in tissue engineering, and
can be described as devices that utilize mechanical methods to
influence biological processes.

16. Types of Bioreactors

17. Part II:-
Role of AI and Automation in Bioprocess
Industry

18. Introduction
• The bio-manufacturing industry has now the opportunity to upgrade
its production processes to be in harmony with the latest industrial
revolution.
• One of the most significant obstacles towards the implementation of
smart bio-manufacturing is the collection of large sets of relevant data.
• Empowering the current bio-manufacturing industry to transition to
Industry 4.0 operations allows for improved productivity through
information-driven automation, not only by developing infrastructure,
but also by introducing more advanced monitoring and control
strategies.

19. Monitoring and Control of Bioprocesses
• Notwithstanding the different objectives and end products, most bio-manufacturing
processes include the cultivation of microorganisms, which implies a process
consisting of complex chemical, physical and biological phenomena.

20. • Monitoring methods and their associated sensors and analyzers, can
be further categorized according to their position regarding the
process unit as in-line, at-line or off-line.

21. Towards Real Time Data Collection
• Real-time monitoring of bioreactors is seen as a crucial part
of effective bioprocess control since it can help achieve high
efficiency, productivity and reproducibility.
• The combined analytical and data-driven approaches are
promising new developments that have the potential to
monitor key process parameters in real-time.

22. Biosensors:-
• Analytical tools consisting of an immobilized sensing material.
• They are in close contact with a suitable transducer.
• Converts the biochemical signals into quantifiable electrical signals.
• Consists of 3 parts:-
An Immobilized Biological Detection Element, a Signal Transducer Unit
and a Signal Conversion Unit.
Spectroscopic Sensors:-
• Almost all variables are accessible.
• Produce wide information little on time.

23. Chemometrics (Multivariate Data Analysis, MDVA):-
• Subset of machine learning algorithms.
• Deals with multiple variables simultaneously.
• Generally based on Principal Component Analysis (PCA).
Free Floating Wireless Sensors:-
• They follow the flow in the bioreactor and collect data along a
trajectory, transmitting it by wireless technology.
• They are deployed inside the bioreactor.
• They move around freely to measure variables.
Soft Sensors:-
• They are advanced process monitoring systems.
• Use algorithms to assess measurements.

24. References
• Shehnaz Ahmed, Veeren M. Chauhan, Amir M. Ghaemmaghami
and Jonathan W. Aylott (2019). New generation of bioreactors that
advance extracellular matrix modelling and tissue engineering.
Biotechnology Letter. 41: 1-25.
• Carina L. Gargalo, Isuru Udugama, Katrin Pontius, Pau C.
Lopez, Rasmus F. Nielsen, Aliyeh Hasanzadeh, Seyed Soheil
Mansouri, Christoph Bayer, Helena Junicke and Krist V.
Gernaey(2020). Towards smart biomanufacturing: a perspective on
recent developments in industrial measurement and monitoring
technologies for bio-based production processes. Journal of
Industrial Microbiology and Biotechnology.
(https://doi.org/10.1007/s10295-020-02308-1)