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

TL;DR

This paper develops a pipeline to translate Boolean network interventions from single-cell models to multicellular agent-based models, enabling better understanding of cellular control in complex biological systems.

Contribution

It introduces a method to map discrete Boolean network interventions to continuous-time multicellular models, addressing limitations of single-cell approaches in multicellular contexts.

Findings

Interventions effective at single-cell level can differ in impact at multicellular scale.

Differences in internal dynamics influence population and spatial distribution outcomes.

The approach highlights the importance of internal network dynamics in multicellular simulations.

Abstract

Intracellular networks process cellular-level information and control cell fate. They can be computationally modeled using Boolean networks, which are implicit-time causal models of discrete binary events. These networks can be embedded in computational agents to drive cellular behavior. To explore this integration, we construct 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 inherent algorithmic limitations, these interventions are most suitable for cell-level determinations, not the more realistic multicellular setting. To address these limitations, we treat the target control solutions as putative targets for therapeutic interventions 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 situations in experimental and translational biology. These include a series of simulations: constant substrate gradients, global substrate pulses, and time-varying boundary conditions. We find that interventions that perform equally well in the implicit-time single-cell setting are separable in the multiscale setting in ability to impact population growth and spatial distribution. Further analysis shows that the population and spatial distribution differences arise from differences in internal dynamics (stable motif controls versus target controls) and network distance between the intervention and output nodes. This proof of concept work demonstrates the importance of accounting for internal dynamics in multicellular simulations as well as impacts on understanding of Boolean network control.