Teaching Case Study in Discrete Mathematics: Application of Clustering Algorithms in Brucella Traceability Research

Abstract

In this study, we combined discrete mathematics with computational biology to identify Brucella species using clustering algorithms. By analyzing the Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra of 44 Brucella isolates, which included 21 Brucella melitensis, 12 Brucella suis, and 11 Brucella abortus, we utilized a feature selection strategy to pinpoint 22 key characteristics critical for species differentiation. We then developed a spectral clustering-based model for Brucella traceability. This model offers a rapid and cost-effective alternative to traditional, labor-intensive identification methods, significantly improving the efficiency and accuracy of Brucella strain identification. Additionally, it aids in monitoring disease transmission trends, identifying outbreak sources, and formulating effective control strategies to mitigate risks. Our findings demonstrate the practical application of discrete mathematics in computational biology, contributing significantly to both scientific research and educational methodologies. This approach illustrates how mathematical concepts can be effectively applied to solve real-world biological problems, providing valuable insights for future interdisciplinary studies and innovative solutions.

Keywords

Brucella / mass spectrometry / clustering / discrete mathematics

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