Spreadsheet Modeling
Spreadsheet Modeling - Related Videos
09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Cited by 6 •
2017
We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.
09:54
A Model to Simulate Clinically Relevant Hypoxia in Humans
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Cited by 1 •
2016
Hypoxia simulation in humans has usually been performed by inhaling hypoxic gas mixtures. For this study, apneic divers were used to simulate dynamic hypoxia in humans. Additionally, physiological changes in desaturation and re-saturation kinetics were evaluated with non-invasive tools such as Near-Infrared-Spectroscopy (NIRS) and peripheral oxygenation saturation (SpO2).
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Cited by 66 •
2013
Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.
12:26
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Cited by 34 •
2016
We demonstrate the utility of remotely sensed data and the newly developed Software for Assisted Habitat Modeling (SAHM) in predicting invasive species occurrence on the landscape. An ensemble of predictive models produced highly accurate maps of tamarisk (Tamarix spp.) invasion in Southeastern Colorado, USA when assessed with subsequent field validations.
07:41
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
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Cited by 2 •
2017
Fast-scan cyclic voltammetry can monitor in vivo dopamine neurotransmission in the context of drugs, disease, and other experimental manipulations. This work describes the implementation of QNsim1.0, a software to model electrically stimulated dopamine responses according to the quantitative neurobiological model to quantify estimates of dopamine release and reuptake dynamics.
FAQs
Linked assumptions create dependencies among cells, so changing one biological parameter can update downstream calculations. Formulas translate measurements or parameter values into outputs, while organized tables keep inputs and results traceable. This structure helps researchers test relationships systematically and identify which assumptions drive a model's prediction.
Sensitivity analysis reveals how strongly outputs respond when inputs or conditions vary. In a biological spreadsheet, researchers can change parameters such as population values, enzyme measurements, or gene expression inputs and compare recalculated results. The pattern shows which variables most influence conclusions, supporting more focused investigation and clearer interpretation of uncertainty.
Spreadsheet modeling can provide an accessible, transparent stage before more complex computational methods are applied. Tables, formulas, linked assumptions, charts, and sensitivity analyses expose how results were produced, allowing researchers to inspect relationships and test preliminary predictions. This makes the approach useful for hypothesis development and data exploration before extending the analysis with more advanced tools.
Researchers first organize experimental measurements or biological parameters in structured cells. They then enter assumptions, connect relevant cells, apply formulas for calculated outputs, and examine results with charts or sensitivity analyses. This workflow links inputs to interpretable quantities and allows conditions to be varied systematically, supporting preliminary prediction or comparison of alternative scenarios.
Spreadsheet models can address population growth, enzyme activity, gene expression, laboratory workflows, and resource allocation. The layout can be organized around calculating biological outputs, comparing conditions, organizing measurements, or supporting a quantitative decision. These uses show how a cell-based approach can connect experimental data with planning and interpretation across biological research.
By linking biological measurements or parameters to calculated outputs, a spreadsheet can support choices about laboratory workflows and resource allocation. Researchers can organize relevant inputs, adjust assumptions, and examine how resulting values change under different conditions. This creates a transparent record of the quantitative reasoning behind a decision and supports preliminary quantitative planning before more complex analysis is applied.