Abstract
Most biological systems are difficult to analyse due to a multitude of interacting components and the concomitant lack of information about the essential dynamics. Finding appropriate models that provide a systematic description of such biological systems and that help to identify their relevant factors and processes can be challenging given the sheer number of possibilities. Model selection algorithms that evaluate the performance of a multitude of different models against experimental data provide a useful tool to identify appropriate model structures. However, many algorithms addressing the analysis of complex dynamical systems, as they are often used in biology, compare a preselected number of models or rely on exhaustive searches of the total model space which might be unfeasible dependent on the number of possibilities. Therefore, we developed an algorithm that is able to perform model selection on complex systems and searches large model spaces in a dynamical way. Our algorithm includes local and newly developed non-local search methods that can prevent the algorithm from ending up in local minima of the model space by accounting for structurally similar processes. We tested and validated the algorithm based on simulated data and showed its flexibility for handling different model structures. We also used the algorithm to analyse experimental data on the cell proliferation dynamics of CD4+ and CD8+ T cells that were cultured under different conditions. Our analyses indicated dynamical changes within the proliferation potential of cells that was reduced within tissue-like 3D ex vivo cultures compared to suspension. Due to the flexibility in handling various model structures, the algorithm is applicable to a large variety of different biological problems and represents a useful tool for the data-oriented evaluation of complex model spaces.
Highlights
Experiments often allow time-resolved measurements of individual components of a biological system
Even under the assumption that the evaluation of one model would take only one second, evaluating 3.3 × 107 different models would require more than 1 year in computational run time, and evaluation times will increase with the complexity of the model structure
We developed a Flexible and dynamic Algorithm for Model Selection (FAMoS) that was designed for the analysis of complex systems dynamics within large model spaces, but is able to handle many diverse mathematical model structures
Summary
Experiments often allow time-resolved measurements of individual components of a biological system. Revealing the most appropriate systematic representation to describe the connectivity and dynamical interactions of these individual components based on such measurements represents a major challenge in order to identify important processes that determine the dynamics. Even under the assumption that the evaluation of one model would take only one second, evaluating 3.3 × 107 different models would require more than 1 year in computational run time, and evaluation times will increase with the complexity of the model structure Most studies address this problem by testing only a reduced number of models, i.e., limiting the number of tested hypotheses based on prior biological knowledge or assumptions [1, 7]. There is an increasing need for computational algorithms that efficiently search large model spaces to provide the best systematic description of experimentally observed dynamics
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