Contemporary computational approaches to equation discovery enable automated design of dynamic systems and thus extend their application scope towards the field of synthetic biology. It deploys engineering approaches to manage and build new, artificial biological systems. The contribution presents a new approach to design or automatic construction of in-silica models, the properties of which are in accordance with predefined user requirements. The approach combines processbased modeling and multi-criteria optimization. First, it makes it easy to determine a plethora of alternative system designs using the library of components (components) and a template plan, while multi-criteria optimization is used to select the most appropriate design by taking into account multiple criteria based on the required model properties. The new approach is shown to be extremely useful for designing deterministic and stochastic systems with different required properties (e.g., oscillatory behavior). It has been successfully used for reconstructing known designs from the related literature and proposing new (so far unknown) designs. The results indicate that the approach is applicable to relevant design problems, such as planning an in-silica model of targeted and timely delivery of drugs to cells.
COBISS.SI-ID: 29806119
Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.
COBISS.SI-ID: 29437223