John Metzcar, Indiana Univ: Translating and applying Boolean network control theory in the multiscale setting.

John Metzcar, Indiana University, will discuss: Translating and applying Boolean network control theory in the multiscale setting.

Intracellular systems process cellular-level information and control cell fate. Understanding and controlling their dynamics is a main goal of computational systems biology. Often these systems are represented with Boolean networks due to their amenability for using relatively sparse data and fast simulation times. In our proof of concept work, we solve the Boolean network target control problem for the T-LGL leukemia cell survival network of Zhang et al (2008), yielding multiple node- and edge-level strategies to control cell fate (induce apoptosis). However, due to inherent limitations of the algorithms, these interventions are only suitable for cell-level determinations, contrasting with the more typical multicellular settings of both experiments and tissue. To address this, we developed a pipeline to translate these cell-level models to agent-based models and computationally explore interventions in high-throughput. Putting this all together produced a computational laboratory to develop phenotype control strategies and evaluate their robustness in the multicellular setting. Furthermore, we found interesting differences in the dynamics between the attractor and target controls we uncovered. Attractor controls acted in a “slow but steady” way to inhibit population growth while target controls sometimes quickly controlled the population, but were unable to control it in the long run. We also saw differences in agents’ spatial distribution with larger regions of efficacy corresponding to both the type of control (attractor versus target) and decreasing network distance between the intervention target and the controlled node. Overall, this work expands the toolkit of multiscale modeling, highlights aspects of network control that may be overlooked in the single-cell setting, and advances techniques for understanding and controlling multicellular systems biology.