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Related Concept Videos

Upstream Processing01:27

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
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Microbial leaching, also known as bioleaching, is an environmentally favorable method for extracting metals from low-grade ores using specific microorganisms. This biotechnological approach is particularly valuable for mining operations targeting copper, gold, and uranium, where traditional extraction methods may be economically or environmentally impractical.Copper Leaching and Microbial CatalysisIn copper bioleaching, crushed ore is arranged into heaps and irrigated with a dilute sulfuric...
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Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly monitored using...
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Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Mining bioprocess data: opportunities and challenges.

Salim Charaniya1, Wei-Shou Hu, George Karypis

  • 1Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA.

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|November 4, 2008
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Summary

Modern biotechnology plants generate vast data. Data mining can unlock insights into process variations and improve productivity and product quality using advanced methods for biological data.

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Data Science

Background:

  • Biotechnology production relies on sophisticated control and data logging systems.
  • Plant data contains valuable information on process outcome fluctuations (productivity, quality).
  • Data can reveal causes of variability and suggest process improvements.

Purpose of the Study:

  • To describe steps in process data mining for biotechnology.
  • To highlight recent data mining advances for biological data.
  • To enable data-driven knowledge discovery for robust bioprocesses.

Main Methods:

  • Review of data mining methodologies.
  • Emphasis on methods suitable for complex biological process data.
  • Discussion of data preprocessing and pattern recognition techniques.

Main Results:

  • Data mining can uncover hidden patterns in bioprocess data.
  • Predictive models can forecast process outcomes.
  • Identification of key factors influencing productivity and quality.

Conclusions:

  • Data mining is essential for optimizing biotechnology production.
  • Advanced data mining techniques enhance understanding of biological processes.
  • Leveraging plant data leads to more robust and efficient biomanufacturing.