Name
Abstract | ||
|---|---|---|
Constructing kinetic models that describe the time-dependent behavior of every enzyme-catalyzed reaction in a genome-scale model is a daunting task. Mechanistic knowledge of enzyme kinetics is often unavailable, and estimating a consistent set of rate parameters from time-series data requires a large experimental effort for even a moderately-sized network. Model construction techniques that derive computationally efficient, biochemically meaningful, accurate dynamic models are needed. A new method, Abstraction of Kinetic Models, explores constructing predictive kinetic models of modules, where the primary goal is preserving module-level behavior instead of developing accurate kinetic expressions for each reaction within the module. When eliminating a module variable (e.g. concentration of a particular metabolite), our method compensates by shifting the roles of other metabolites within the module as activators and inhibitors, if needed, and by calculating a new set of parameter values. AKM provides a systematic method for exploring accuracy vs. simplicity tradeoffs during abstract model construction. Validation efforts on two test cases demonstrate such tradeoffs, and show that modest loss of accuracy is attainable when some internal metabolite concentrations are eliminated and when the newly constructed network model compensates for missing variables. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2506583.2512386 | BCB |
Keywords | Field | DocType |
model construction technique,network model,genome-scale model,predictive kinetic model,accurate dynamic model,module variable,accurate kinetic expression,kinetic model,new method,biochemical networks,kinetic models,abstract model construction,modeling,parameter estimation,genetic algorithms | Abstraction,Expression (mathematics),Computer science,Artificial intelligence,Dynamic models,Test case,Estimation theory,Machine learning,Network model,Genetic algorithm,Kinetic energy | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Calvin Hopkins | 1 | 2 | 0.74 |
| Soha Hassoun | 2 | 535 | 241.27 |