Biological Signal Analysis
Biological Signal Analysis - Related Videos
13:56
Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises
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Cited by 15 •
2011
This is a demonstration of how biological membranes can be understood using electrical models. We also demonstrate procedures for recording action potentials from the ventral nerve cord of the crayfish for student orientated laboratories.
10:36
Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
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Cited by 5 •
2016
Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.
11:17
Acquisition and Analysis of an ECG (electrocardiography) Signal
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient's torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall...
10:09
Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration
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Cited by 10 •
2013
Tissue-specific analysis of a hair follicle regeneration model using lentivirus to mediate gain- or loss-of-function.
01:23
Small-Signal Analysis of MOSFET Amplifiers
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2025
In small-signal analysis, a MOSFET transistor amplifier acts as a linear amplifier when operating in its saturation region. The gate-to-source voltage (VGS) of the MOSFET is the sum of the DC biasing voltage and the small time-varying input signal. This combination sets up the operating point and modulates the drain current (ID) that flows from the drain to the source. When a small AC signal is superimposed on the DC bias voltage at the gate, the instantaneous drain current comprises three...
FAQs
The analytical value comes from relating changes in measurable signal properties to changes in the underlying biological process. Researchers examine amplitude, frequency, timing, or spatial distribution rather than treating a measurement as an isolated value. This relationship can reveal whether experimental conditions are associated with altered cellular activity, cytokine responses, pathogen-associated effects, or host-microbe interactions.
Noise reduction helps distinguish biologically meaningful variation from unwanted fluctuations in a measurement. Without this step, apparent changes in amplitude, timing, frequency, or distribution could obscure the pattern produced by the biological system. Cleaner signals support more reliable feature extraction and make comparisons between experimental conditions easier to interpret.
The most informative feature depends on how the biological response changes. Amplitude can describe the magnitude of a signal, frequency can capture recurring behavior, timing can indicate when an event occurs, and spatial distribution can show where activity is concentrated. Comparing these properties across conditions allows researchers to characterize distinct response patterns rather than relying on a single measurement.
Distinct conditions may produce different combinations of signal features, even when they affect the same biological system. Examining several properties together, such as magnitude and timing or frequency and distribution, provides a broader pattern for comparison. In immunology and infection studies, this approach can help relate altered measurements to immune activation, cytokine responses, or pathogen-associated changes.
A typical workflow begins by acquiring measurements generated by the biological system. Researchers then reduce noise, extract features such as amplitude, frequency, timing, or distribution, and compare those features across experimental conditions. The resulting patterns are interpreted in relation to the biological process under study, supporting experimental validation or further characterization.
It is useful when researchers need to quantify dynamic or spatial changes associated with immune-cell activation, cytokine responses, pathogen-associated effects, or host-microbe interactions. By converting these changes into comparable signal features, the analysis can support disease characterization and help evaluate whether experimental conditions produce distinct biological responses.
The resulting measurements can contribute to biomarker identification, disease characterization, and experimental validation. Signal patterns may provide measurable indicators associated with a biological condition, while comparisons across conditions can test whether an observed response is consistent with the expected process. In immunology and infection research, these outcomes also support more precise diagnostic and therapeutic strategy development.