Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology
Areti Tsigkinopoulou1, Syed Murtuza Baker1, Rainer Breitling1
1Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
Trends in Biotechnology
|January 18, 2017
Summary
Quantitative computational modeling is becoming more accessible and predictive. A new
Area of Science:
- Computational Biology
- Systems Biology
- Synthetic Biology
Background:
- Skepticism exists within the biological community regarding the value of quantitative computational modeling.
- There is a need to enhance the accessibility and predictive power of computational models.
- Current modeling approaches may lack reproducibility and rigorous confidence quantification.
Purpose of the Study:
- To introduce and advocate for a 'respectful modeling' framework.
- To address the skepticism surrounding quantitative computational modeling.
- To guide the development of higher-quality models for biological applications.
Main Methods:
- The study proposes a conceptual framework termed 'respectful modeling'.
- This framework emphasizes two key aims: respecting the models and their predictions.
- It involves facilitating model reproduction, updates, and rigorous quantification of prediction confidence.
Main Results:
- The 'respectful modeling' framework aims to increase trust and adoption of computational models.
- It promotes reproducibility and transparency in computational biology research.
- Quantifying confidence in model predictions is a key outcome.
Conclusions:
- Adopting a 'respectful modeling' approach is essential for advancing computational biology.
- This framework will enhance model quality, reproducibility, and confidence.
- It will facilitate the application of models in areas like synthetic biology and metabolic engineering.
Related Concept Videos
02:00Molecular Models
44.7K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
44.7K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
01:25Modeling with Differential Equations
138
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
138
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
313
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
313
01:10Propagation of Uncertainty from Systematic Error
1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
301
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
301