Limits...
Control of complex networks requires both structure and dynamics.

Gates AJ, Rocha LM - Sci Rep (2016)

Bottom Line: Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets.We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana.Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

View Article: PubMed Central - PubMed

Affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, USA.

ABSTRACT
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies 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 systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

No MeSH data available.


Directed network structure motifs used in ensemble study: (A) Feed-Forward motif, (B) Chain motif, (C) Loop motif, (D) Loop motif with self-interactions, (E) Fan motif, (F) Co-regulated motif, (G) Co-regulating motif, (H) BiParallel motif, (I) BiFan motif and (J) Dominated Loop motif.
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f2: Directed network structure motifs used in ensemble study: (A) Feed-Forward motif, (B) Chain motif, (C) Loop motif, (D) Loop motif with self-interactions, (E) Fan motif, (F) Co-regulated motif, (G) Co-regulating motif, (H) BiParallel motif, (I) BiFan motif and (J) Dominated Loop motif.

Mentions: We first consider the entire ensemble of BNs with simple structural graphs known as network motifs51. These prototype networks have been useful for exploring the relationship between structure and dynamics of complex networks5253. The motifs considered in our analysis are depicted in Fig. 2.


Control of complex networks requires both structure and dynamics.

Gates AJ, Rocha LM - Sci Rep (2016)

Directed network structure motifs used in ensemble study: (A) Feed-Forward motif, (B) Chain motif, (C) Loop motif, (D) Loop motif with self-interactions, (E) Fan motif, (F) Co-regulated motif, (G) Co-regulating motif, (H) BiParallel motif, (I) BiFan motif and (J) Dominated Loop motif.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834509&req=5

f2: Directed network structure motifs used in ensemble study: (A) Feed-Forward motif, (B) Chain motif, (C) Loop motif, (D) Loop motif with self-interactions, (E) Fan motif, (F) Co-regulated motif, (G) Co-regulating motif, (H) BiParallel motif, (I) BiFan motif and (J) Dominated Loop motif.
Mentions: We first consider the entire ensemble of BNs with simple structural graphs known as network motifs51. These prototype networks have been useful for exploring the relationship between structure and dynamics of complex networks5253. The motifs considered in our analysis are depicted in Fig. 2.

Bottom Line: Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets.We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana.Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

View Article: PubMed Central - PubMed

Affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, USA.

ABSTRACT
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies 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 systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

No MeSH data available.