Reconciling periodic rhythms of large-scale biological networks by optimal control

Abstract

Periodic rhythms are ubiquitous phenomena that illuminate the underlying mechanism of cyclic activities in biological systems, which can be represented by cyclic attractors of the related biological network. Disorders of periodic rhythms are detrimental to the natural behaviours of living organisms. Previous studies have shown that the state transition from one to another attractor can be accomplished by regulating external signals. However, most of these studies until now have mainly focused on point attractors while ignoring cyclic ones. The aim of this study is to investigate an approach for reconciling abnormal periodic rhythms, such as diminished circadian amplitude and phase delay, to the regular rhythms of complex biological networks. For this purpose, we formulate and solve a mixed-integer nonlinear dynamic optimization problem simultaneously to identify regulation variables and to determine optimal control strategies for state transition and adjustment of periodic rhythms. Numerical experiments are implemented in three examples including a chaotic system, a mammalian circadian rhythm system and a gastric cancer gene regulatory network. The results show that regulating a small number of biochemical molecules in the network is sufficient to successfully drive the system to the target cyclic attractor by implementing an optimal control strategy.

Link prediction based on the powerful combination of endpoints and neighbors

“…Link prediction possesses significant application value in practical fields. For example, exploring the relationships between food and metabolic organization in biological networks, 11 finding new friends in social networks, 12 recommending the favorite goods in E-commerce networks, 13 as well as suppressing disease spreading in the multiplex networks. 14 Many literatures offer the topological similarity-based link prediction models.…”