Alexander Gates1,2 and Luis M. Rocha1,2,3

1School of Informatics, Indiana University, Bloomington IN, USA
2 Cognitive Science Program, Indiana University, Bloomington IN, USA
3Instituto Gulbenkian de Ciencia, Portugal

Citation: Gates, A. and L.M. Rocha. [2016] "Control of complex networks requires both structure and dynamics." Scientific Reports. 6:24456. doi: 10.1038/srep24456. PMID: 27087469. PMCID: PMC4834509

The full text and pdf re-print are available from the Nature Scientific Reports site. Watch "Redundancy and control in complex networks", talk about the work at the Mathematical Biosciences Institute. The arXiv: 1509.08409 preprint is also available.


The study of network structure has uncovered organizational principles in complex systems. However, there is also a need to understand how to control them; for example, to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex multivariate systems can be predicted solely from the graph of interactions between variables, without considering variable dynamics: structural controllability and minimum dominating sets. Both methodologies utilize idealized assumptions about multivariate dynamics, yet most accurate models of real-world systems do not abide by these assumptions. Here, we study the relationship between network structure and the control of multivariate dynamics using three distinct measures of controllability in Boolean Networks. We demonstrate that structure-only methods fail to properly characterize controllability in these nonlinear systems; even in very simple networks, a large variation of possible dynamics can occur for the same structure, each with different control profiles. Our methodology is also used to characterize critical control variables in three models of biochemical regulation: the Drosophila melanogaster single-cell segment polarity network, the eukaryotic cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. Structure-only methods both undershoot and overshoot the number and which sets of variables actually control these models, highlighting the importance of the system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays a role in the extent to which structure predicts dynamics.

Keywords: complex systems, complex networks, control, systems biology, dynamics, dynamical systems