International business machines corporation (20240194288). GENERALIZED NESTEDNESS DETECTION IN MICROBIAL COMMUNITIES simplified abstract

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GENERALIZED NESTEDNESS DETECTION IN MICROBIAL COMMUNITIES

Organization Name

international business machines corporation

Inventor(s)

Niina Haiminen of Tampere (FI)

Laxmi Parida of Mohegan Lake NY (US)

Filippo Utro of Pleasantville NY (US)

GENERALIZED NESTEDNESS DETECTION IN MICROBIAL COMMUNITIES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240194288 titled 'GENERALIZED NESTEDNESS DETECTION IN MICROBIAL COMMUNITIES

Abstract: Topological data analysis is used to detect general nestedness in microbial communities through the generation and application of filtration matrices and persistent homology barcodes. From a starting point of an input matrix with 1s and 0s, a filtration matrix is generated with a mathematical function, such as a Jaccard similarity, overlap coefficient, or a min(com1/sum(1)) computation. The persistent homology of the filtration matrix is then calculated in at least two dimensions using a mathematical function, such as a simplicial complex, a cubical complex, an alpha complex, a Cech complex, or a Vietris-Rips complex, that is displayed in a barcode that provides a visualization of the nestedness within the microbial community. P-values for the microbial community nestedness can be calculated by comparing the shape and length of the persistent homology barcodes for the input matrix against persistent homology barcodes for a completely randomized input matrix.

Key Features and Innovation:

  • Detection of general nestedness in microbial communities using topological data analysis.
  • Generation of filtration matrices and calculation of persistent homology barcodes.
  • Application of mathematical functions such as Jaccard similarity to analyze microbial community structures.
  • Visualization of nestedness within microbial communities through barcodes.
  • Calculation of p-values for microbial community nestedness.

Potential Applications:

  • Environmental monitoring and analysis of microbial communities.
  • Biomedical research for understanding microbial interactions.
  • Agriculture for optimizing soil microbial communities.
  • Ecology for studying biodiversity and ecosystem dynamics.

Problems Solved:

  • Difficulty in detecting nestedness patterns in microbial communities.
  • Lack of efficient visualization tools for complex community structures.
  • Limited understanding of the relationships within microbial populations.

Benefits:

  • Improved insights into microbial community structures.
  • Enhanced ability to compare and analyze different community compositions.
  • Facilitates the identification of key species and interactions within microbial communities.

Commercial Applications: Microbial community analysis software for research institutions, environmental agencies, and biotech companies.

Prior Art: Researchers have previously used topological data analysis for studying complex systems such as neural networks and social networks.

Frequently Updated Research: Ongoing studies are exploring the application of persistent homology in various fields, including biology, computer science, and social sciences.

Questions about Topological Data Analysis in Microbial Communities: 1. How does persistent homology help in understanding the nestedness of microbial communities? 2. What are the potential limitations of using topological data analysis in microbial community research?


Original Abstract Submitted

topological data analysis is used to detect general nestedness in microbial communities through the generation and application of filtration matrices and persistent homology barcodes. from a starting point of an input matrix with 1s and 0s, a filtration matrix is generated with a mathematical function, such as a jaccard similarity, overlap coefficient, or a min(com1/sum(1)) computation. the persistent homology of the filtration matrix is then calculated in at least two dimensions using a mathematical function, such as a simplicial complex, a cubical complex, an alpha complex, a c�ech complex, or a vietris-rips complex, that is displayed in a barcode that provides a visualization of the nestedness within the microbial community. p-values for the microbial community nestedness can be calculated by comparing the shape and length of the persistent homology barcodes for the input matrix against persistent homology barcodes for a completely randomized input matrix.

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