Paper Info
Title
Optimisation and landscape analysis of computational biology models: a case study.
Abstract
The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide a significant reduction in parameter numbers, whilst still providing dynamical complexity comparable to more biochemically detailed models, such as those based on differential equations. Here, we explore several approaches to optimising BDEs to timeseries data, using a simple circadian clock model as a case study. We compare the effectiveness of two optimisers on our problem: a genetic algorithm (GA) and an elite accumulative sampling (EAS) algorithm that provides robustness to data discretisation. Our results show that both methods are able to distinguish effectively between alternative architectures, yielding excellent fits to data. We also perform a landscape analysis, providing insights into the properties that determine optimiser performance (e.g. number of local optima and basin sizes). Our results provide a promising platform for the analysis of more complex GRNs, and suggest the possibility of leveraging cost landscapes to devise more efficient optimisation schemes.
Year
DOI
Venue
2017
10.1145/3067695.3084609
GECCO (Companion)
Keywords
Field
DocType
Systems biology, optimisation, landscape analysis, Boolean delay equations
Discretization,Bottleneck,Time series,Computer science,Robustness (computer science),Artificial intelligence,Genetic algorithm,Mathematical optimization,Local optimum,Algorithm,Sampling (statistics),Gene regulatory network,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Kevin Anthony James Doherty111.36
Khulood AlYahya2195.17
Ozgur E. Akman3548.69
Jonathan E. Fieldsend425026.25