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In-vitro Mutagenesis01:16

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Related Experiment Video

Updated: Mar 2, 2026

Using a Fluorescent PCR-capillary Gel Electrophoresis Technique to Genotype CRISPR/Cas9-mediated Knockout Mutants in a High-throughput Format
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FOCuS: a metaheuristic algorithm for computing knockouts from genome-scale models for strain optimization.

Sarma Mutturi1

  • 1Department of Microbiology and Fermentation Technology, CSIR - Central Food Technological Research Institute, Mysuru 570 020, Karnataka, India. sarma.mutturi@gmail.com smutturi@cftri.res.in.

Molecular Biosystems
|May 23, 2017
PubMed
Summary
This summary is machine-generated.

A new computational method, FOCuS (Flower-pOllination coupled Clonal Selection algorithm), efficiently identifies optimal gene knockouts in metabolic networks. This approach enhances metabolite production by exploring search spaces more effectively than existing tools.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Constraint-based flux analysis tools for generating knockout strains often rely on bilevel-MIP.
  • Metaheuristic approaches, known for flexibility and scalability, are underexplored for this task.
  • Existing tools lack consideration for sectioning the search space to find optimal knockouts.

Purpose of the Study:

  • To develop a novel computational procedure, FOCuS (Flower-pOllination coupled Clonal Selection algorithm), for identifying optimal reaction knockouts.
  • To maximize the production of specific metabolites through reaction knockouts in metabolic networks.
  • To evaluate FOCuS performance against existing methods.

Main Methods:

  • FOCuS integrates the flower pollination algorithm and the clonal selection algorithm.
  • The procedure identifies optimal reaction knockouts from a metabolic network.
  • Search space sectioning and pooling of priority reactions were employed to reduce computational time.

Main Results:

  • FOCuS demonstrated robustness across various metabolic network sizes and knockout numbers.
  • Performance was compared with MIP and other metaheuristic-based tools in case studies.
  • The method significantly reduced computational time by optimizing the search space.

Conclusions:

  • FOCuS offers an effective and robust approach for optimizing metabolic networks through reaction knockouts.
  • The integration of metaheuristic algorithms and search space optimization provides computational advantages.
  • This method advances the field of metabolic engineering by improving the efficiency of knockout strain generation.