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

Abstract

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.

Understanding How Cells Probe the World: A Preliminary Step towards Modeling Cell Behavior?

“…In conclusion, currently available experimental and theoretical methods make it a realistic goal to achieve a detailed description of the state of individual cells and predict transitions triggered by environmental cues. Inner working mechanisms should be obtained in a next step [73]. However, while much effort has been made to achieve a multimodal description of cell states, fewer reports are devoted to a similarly multimodal description of the environmental cues that trigger cell state transition.…”

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators

“…More recently, database listings of such interactions [9,10] and experimental information retrieval techniques on a bigger number of components were subjected to many advancements. Combined with the design of automatic methods for Boolean formulae inference from the constraints encoded in the knowledge and the experimental data [11][12][13][14], these new developments allows the construction of large Boolean models. While this effort faces many challenges, we believe it is a promising way to study the large-scale complexity of biological systems.…”