Hybrid Modeling of Dynamic Networks: Towards Standardized Representation and Automated Model Design

Computational modeling has become an efficient tool for studying complex systems such as biological, environmental, and economic systems. For instance, computational models of intra- and inter-cellular networks can provide important insights into biological systems and guide scientists toward the missing information, and thus, speed up biological discoveries and reduce the cost and time of conducting unnecessary or impractical biochemical experiments. Creating accurate models requires the knowledge not only about the key components of the system and their causal or mechanistic relationships, but also about relative timing of events, as well as the various timescales of relevant processes. In biology, different timescales of processes can be captured with reaction rate constants and modeled with differential equations, however, these quantitative constants are often unknown or difficult to measure, especially for large models. Therefore, abstraction methods such as logical and discrete modeling have been developed to overcome the limitations of uncertain information. Although several methods to incorporate timing into discrete models have been proposed, a methodology for modeling common biological motifs at varying timescales and at different regulatory conditions is necessary. In this work, we introduce a methodology to standardize modeling of common motifs occurring in many domains, with a special focus on accurately incorporating timing information into dynamic network models. Additionally, we propose several deterministic and stochastic simulation approaches that can be used to analyze these models. Overall, the goal of this work is to facilitate automated design of hybrid models of dynamic networks. Finally, we demonstrate our efforts using existing models of intra- and inter-cellular signaling occurring in T cell differentiation and budding yeast cell cycle. We also apply the developed methods on socio-economic systems such as food insecurity in South Sudan in order to study the factors leading to humanitarian crises