Transittability of complex networks and its applications to regulatory biomolecular networks

Chen

et al. 2017

Sci Rep

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

mentioning

confidence: 99%

Physical controllability of complex networks

Chen

et al. 2017

Sci Rep

“…Particularly for biological complex networks, structural control has been widely applied and discovered many interesting properties of biological systems. However, the existing control methods Gao et al 2014;Wu et al 2014b;Guo et al 2017;Guo et al 2018d) cannot be directly applied to the above constructed personalized transition state gene networks with a nonlinear and undirected dynamic because they are focused on linear dynamic directed networks. Therefore, an effective nonlinear network control strategy is required to characterize personalized transition state gene networks, and support selecting specific KCGs based on phenotypic changes.…”

Section: Identifying Personalized Kcgs Based On Phenotypic Transitionmentioning

confidence: 99%

“…To solve this problem, we introduced network control theory to model the control role of drugs (as controllers) on the transition state of gene co-expression networks from tumor state to normal state. Network control theory considers how to choose key network elements as drivers, the activation of which may drive the entire network towards a desired control objective or state based on proper control signals Gao et al 2014;Ruths and Ruths 2014;Wu et al 2014a;Guo et al 2017;Guo et al 2018d). Recent studies on network controllability have offered powerful mathematical frameworks to understand diverse biological systems at a network level (Jgt et al 2017;Guo et al 2018c;Li et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A novel network controllability algorithm to target personalized driver genes for discovering combinational drugs of individual cancer patient

Guo

Zhang

Zeng

et al. 2019

Preprint

Self Cite

It is a big challenge to identify patient-specific drug combinations based on cancer omics data. However, most conventional methods used for identifying personalized therapies require large sample sizes and focuse on the population as a whole and we still lack a feasible mathematical framework to circumvent this key problem in the clinical application of precision medicine. This work presents a personalized drug controller method (PDC) to identify drug combinations of individual cancer patients, by exploring the transition state information from a disease state to a normal state, thus providing novel insights into tumor heterogeneity in complex patient ecosystems.We validate the effectness of PDC in terms of the accurate prediction of drug combinations on two benchmark cancer datasets. We can also discover personalized key control genes (KCGs) even if the KCGs are hidden in transcription profiles by exploring their network and structural characteristics. Furthermore, we provide computationally derived side effect signatures for drug combinations based on patient-specific KCGs to enhance patient stratification and prognostication. The experimental results strongly support that PDC is effective for personalized drug therapy and treatment, and can fill the gap between network control theory and the personalized drug discovery problem.

2017 International Conference on Information and Communication Technology Convergence (ICTC)

“…When the number of component is too great to handle, researchers apply discrete model such as Boolean networks and write full truth table for individual interactions or establish Boolean equations for each component's input condition [4]. After modeling the system, they apply complicated control methods [5][6] [7] [8], which usually have non-polynomial computational cost.…”

Section: Introductionmentioning

confidence: 99%

Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning

Ban

Kim

2017

Modern control theories such as systems engineering approaches try to solve nonlinear system problems by revelation of causal relationship or co-relationship among the components; most of those approaches focus on control of sophisticatedly modeled white-boxed systems. We suggest an application of actor-critic reinforcement learning approach to control a nonlinear, complex and black-boxed system. We demonstrated this approach on artificial green-house environment simulator all of whose control inputs have several side effects so human cannot figure out how to control this system easily. Our approach succeeded to maintain the circumstance at least 20 times longer than PID and Deep Q Learning.