Title: Translating and evaluating single-cell Boolean network interventions in the multiscale setting

Abstract: Intracellular networks process cellular-level information and control cell fate. They can be computationally modeled using Boolean networks, implicit-time causal models of discrete binary events. These networks can be embedded into cell-agents of an agent-based model to drive cellular behavior. To explore this integration, we identify a set of candidate interventions that induce apoptosis in a cell-survival network of a rare leukemia using exhaustive search simulation, stable motif control, and an individual-based mean field approach (IBMFA). Due to algorithm constraints, these interventions are well-suited for cell-level decisions but less so for multicellular agent-based contexts. To address these limitations, we treat the target control solutions as putative therapeutic targets and develop a pipeline to translate them to continuous-time multicellular, agent-based models. We set the discrete-to-continuous transitions between the Boolean network and multicellular model via thresholding and produce simple computational simulations designed to emulate aspects of experimental and translational biology. Our results reveal that interventions performing equivalently in Boolean network simulations diverge in multiscale settings in both population growth and spatial distribution. Further analysis links these differences to internal network dynamics and the intervention’s proximity to output nodes. This proof-of-concept study highlights the importance of accounting for internal dynamics in multicellular simulations and advances understanding of Boolean network control.