Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.Author summary: Mechanisms of biological processes that explain and predict biological behaviors continue to be challenging to attain. In this context, logic-based models with few parameters can be formulated to describe experimental data. However, constructing such networks based on the available evidence is often done in an ad-hoc, error-prone manner that reflects the bias of the modeler. Here we present an algorithm that infers Boolean logic models from mappings of initial states to steady states, from available experimental data, and without human supervision. Moreover, our methodology enables users to incorporate additional biological information (expert knowledge) to further refine Boolean models of cellular processes.