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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Biological data visualization | 3/5 | https://en.wikipedia.org/wiki/Biological_data_visualization | reference | science, encyclopedia | 2026-05-05T14:01:39.614425+00:00 | kb-cron |
Systems biology is a branch of biological data visualization dedicated to analyzing and modeling complex biological systems. Popular computational models used in systems biology include process calculi, such as stochastic π-calculus, and constraint-based reconstruction and analysis (COBRA), a paradigm that considers physical, enzymatic, and topological constraints underlying a phenotype in a metabolic network. Most data visualization in systems biology is done using mathematically generated models. Researchers will diagram all of the protein, gene, or metabolic pathways in a given biological system, then determine the speed of the reactions in that system using mass action kinetics or enzyme kinetics. These values are used as parameters to construct differential equations representing the system, which can then be used to determine the behavior of the things within that system. Alternative mathematical modeling solutions also exist; for instance, a COBRA method such as flux balance analysis could be used to analyze the flow of metabolites through a particular metabolic network. Another key imaging method in systems biology is mass spectrometry, which can be used to visualize the spatial distribution of compounds, biomarkers, metabolites, peptides, and/or proteins within the body. This is especially helpful in metabolomics, a branch of systems biology that uses mass spectrometry to measure metabolite distribution information, then uses the measured intensity to construct an image. Popular software tools used in systems biology modeling include massPy, Cytosim, and PySB. Further examples may be found at Wikipedia's list of systems biology modeling software.
== Microscopy visualization == Other than optical and electron microscopy, other techniques like scanning probe, ultraviolet, infrared, digital holographic, laser, and amateur are also utilize on Visualization.
New approaches There is study investigates the use of two-photon microscopy, a technique capable of imaging depths up to 800 μm through two-photon absorption, for visualizing microrobotic agents beneath biological tissue, demonstrating its transformative potential for both in vitro and in vivo microrobotics applications. Researchers used bright-field light microscopy with high-intensity pulsing LED illumination to capture detailed 12-bit-per-channel images of live cells, addressing data distortions caused by optical path interactions and sensor anomalies with a comprehensive spectroscopic calibration approach, allowing for visualization with minimal information loss in 8-bit intensity depth. Researchers explored a community-driven initiative focused on improving the depiction of light microscopy data in scientific publications by adhering to the 'FAIR Data Principles,' which aim to enhance data findability, accessibility, interoperability, and reproducibility. Despite persistent challenges related to data quality and communication, the initiative emphasizes the role of global scientific collaboration in advancing imaging standards and leverages historical insights to guide and promote future advancements in biological imaging.
== Magnetic resonance imaging ==
Magnetic resonance imaging (MRI) is a common form of biological data visualization used to form pictures of internal biological processes. Different settings of radiofrequency pulses and gradients result in different image appearances; these combinations are known as MRI sequences. A particularly notable subset of MRI is magnetic resonance angiography, which is a group of techniques used to image arteries and veins. MRI's imaging utility is further expanded upon by diffusion MRI and functional MRI, which can be used to capture neuronal tracts and blood flow respectively.
Diffusion MRI further relies on diffusion tensor imaging (DTI), which measures water molecule diffusion and directionality, and diffusion basis spectrum imaging (DBSI), which extracts multiple anisotropic and isotropic diffusion tensors. Functional MRI relies on blood-oxygen-level dependent (BOLD) contrast, which measures the proportion of oxygenated hemoglobin in specific areas of the brain; this allows it to measure and model brain activity based on blood flow. Further MRI techniques include saturation pulses (used to reduce motion artifacts), gradient echo (such as dynamic contrast enhancement), spin echo, and diffusion weighting (a signal contrast generation method based on differences in Brownian motion).
To generate an observable image using MRI, the target is placed in a powerful magnetic field, such as that of an MRI machine. This causes the axes of the hydrogen protons inside the target, which are usually randomly aligned according to equilibrium, to be lined up in the same direction, creating a magnetic vector oriented along the magnet's axis. This orientation also allows the hydrogen protons' spin, or frequency of rotation, to be measured. The alignment is then disrupted using radiofrequency (RF) pulses (RF being a type of non-ionizing electromagnetic radiation). When the magnetic field is removed, the hydrogen protons return to their equilibrium states in a process known as relaxation, and in doing so they emit RF energy. Different tissues relax at different rates, which allows scientists to use specific RF pulse sequences to emphasize particular tissues or abnormalities. After a period of time following the RF pulse, the RF energy signals emitted by the protons are measured to obtain frequency information from each location in the imaged plane. Then Fourier transformation is used to convert this frequency information into intensity levels, which are displayed as shades of grey in the generated image.