Omics Technologies
Omics Technologies - Related Videos
09:20
Fabrication of Low Temperature Carbon Nanotube Vertical Interconnects Compatible with Semiconductor Technology
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Cited by 1 •
2015
A method for the growth of low temperature vertically-aligned carbon nanotubes, and the subsequent fabrication of vertical interconnect electrical test structures using semiconductor fabrication is presented.
01:26
Technological Change
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2024
Total Factor Productivity (TFP) measures the efficiency with which inputs are transformed into outputs in production. It is the essence of economic growth, driven by technological advancement. Consider the agricultural sector, where production requires vast amounts of human labor and work animals. Today, modern farm machinery and agriculture technologies have revolutionized how we cultivate crops and produce food much more efficiently. TP = A*f(K, L) TFP is represented as a multiplier 'A' in...
01:31
Hybridoma Technology
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2023
Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy. Hybridoma Selection Commonly used fusion techniques — electroporation, polyethylene glycol...
07:06
Principles of Site-Specific Recombinase (SSR) Technology
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Cited by 14 •
2008
The advent of site-specific recombinase (SSR) technology and the Cre/lox system has led to numerous advances in molecular biology, and has proven itself as a valuable tool for assessing gene function in transgenic animals. This interview discusses the mechanism of site specific recombination by Cyclization recombinase (Cre) and how the use of this enzyme has led to the development of conditional mutagenesis, which has significant advantages over traditional knock out strategies.
10:12
The MPLEx Protocol for Multi-omic Analyses of Soil Samples
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Cited by 31 •
2018
A protocol is presented for simultaneously extracting metabolites, proteins, and lipids from a single soil sample, allowing reduced sample preparation times and enabling multi-omic mass spectrometry analyses of samples with limited quantities.
FAQs
Measuring several molecular layers can reveal relationships that remain hidden when genes, transcripts, proteins, or metabolites are examined separately. Comparing these data helps connect molecular changes with biological function and distinguish coordinated host responses from pathogen-associated changes. This broader view supports more integrated interpretations of how microbes interact with infected tissues and how immune activity changes during infection.
DNA or RNA sequencing provides information about genetic material and transcripts, whereas mass spectrometry measures proteins or metabolites. These approaches therefore examine different molecular features of the same biological system. Computational analysis can bring the resulting measurements together, allowing researchers to relate changes across molecular layers rather than interpreting each dataset in isolation.
Computational analysis converts large-scale molecular measurements into detectable patterns, quantitative comparisons, and biological interpretations. It helps identify which genes, transcripts, proteins, or metabolites change and whether those changes relate to biological function. In infection studies, this analysis is essential for connecting molecular data with host responses, pathogen factors, and interactions within infected tissues.
The approach can characterize molecular patterns from the host, the pathogen, and infected tissues, then relate those patterns to one another. This makes it possible to examine immune responses alongside pathogen factors instead of treating infection as a single molecular signal. The resulting comparisons help researchers study interactions between microbes and host tissue at system scale.
Omics findings can support biomarker discovery, disease classification, vaccine development, and therapeutic development. Molecular patterns may help identify features associated with disease states or immune responses, while broader comparisons can guide investigation of candidate targets. These applications use measured molecular changes as evidence for distinguishing biological conditions and prioritizing areas for further research.
By combining information about genes, transcripts, proteins, or metabolites with observations of host immunity and pathogen behavior, researchers can construct a more connected view of infection. Such models relate molecular patterns to biological function and tissue interactions. This context is valuable for explaining infection biology beyond isolated measurements and for linking mechanisms with potential interventions.