A forecast for large-scale, predictive biology: Lessons from meteorology
Markus W Covert1, Taryn E Gillies1, Takamasa Kudo2
1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Cell Systems
|June 17, 2021
Summary
Quantitative systems biology uses mathematical modeling to predict biological outcomes. Lessons from meteorology can help expand its global impact on biological research and applications.
Area of Science:
- Quantitative systems biology
- Mathematical modeling in biology
Background:
- Quantitative systems biology is transitioning, with established principles but limited global impact.
- Predictive mathematical models are key for guiding experiments and forecasting outcomes.
Purpose of the Study:
- To forecast the next steps for mathematical modeling to transform biological research.
- To draw parallels from meteorology's success in weather prediction.
Main Methods:
- Reviewing lessons learned from meteorological modeling.
- Applying these insights to biological systems modeling.
Main Results:
- Identifying transferable strategies from atmospheric modeling.
- Highlighting the potential for global impact in systems biology.
Conclusions:
- Meteorology offers a roadmap for advancing biological systems modeling.
- Adopting lessons from weather prediction can significantly enhance biology's predictive power.
Related Concept Videos
01:07What is Weather?
18.7K
Overview
18.7K
01:18Steps in Outbreak Investigation
271
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
271
01:12Precipitation Processes
1.8K
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
1.8K
01:17Precipitation and Co-precipitation
3.0K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
3.0K
01:04Random Error
4.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
4.9K
01:03Prediction Intervals
2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.5K