An experimental design to reduce dynamics uncertainty in genomic networks

In systems biology, network models are often used as a promising tool to study interactions among cellular components (e.g., genes or proteins). However, these models are typically too complex and biological data is very limited which leads to model uncertainty. Network dynamics involves the evolution of entities over time which is central in developing cancer drugs whose aim is to change the dynamical behavior of the network to avoid cancerous phenotypes. In the presence of uncertainty, network dynamics can be updated in different ways giving multiple dynamic trajectories. In this paper, we propose an experimental design method that can effectively reduces the dynamics uncertainty and improve the performance of the interventions. We use the concept of mean objective cost of uncertainty (MOCU) to quantify dynamics uncertainty. We also incorporate the error of the potential experiments in such a way that the objective from the experimental design is taken into account. As a byproduct of the proposed objective-based experimental design method, we also develop a mathematical framework for applying interventions to interaction-based genomic networks. Furthermore, our proposed approach is well-suited for laboratory tests as the results have biological correspondence. A software package is also programmed based on the proposed experimental design method.