A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

Abstract

BackgroundThe advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes).ResultsTherefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng.ConclusionsIn the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12864-017-4332-z) contains supplementary material, which is available to authorized users.

A novel structure-based control method for analyzing nonlinear dynamics in biological networks

“…Controllability and actual control are two key issues associated with con-515 trolling large scale nonlinear dynamic networks. In the past decades, existing control methods can be divided into two main categories: one is the linear dynamic focus control approaches, which focus on the large scale linear networks and ignore the nonlinear dynamic on complex networks[9, 49,11,18]; another is the nonlinear control methods which evaluating the actual control and con-520 trollability of small scale networks such as boolean networks [25,26,27,50 The NCUA has been evaluated on multiple synthetic SF networks and real 540 complex networks, and it has exhibited the novel control characteristics of the undirected SF networks with nonlinear dynamics. The NCUA has also been applied to investigate the networks and their nonlinear control of cancer samples from TCGA by screening known driver genes and known drug targets as controls of their phenotype transitions, as well as to provide meaningful predictions with 545 biological significance.…”

“…The analysis of complex systems from the structure-based control viewpoint 10 provides a deeper understanding of the dynamics of complex large-scale biological systems [15,16,17,18]. So far, the studies exploiting the structure-based control of complex networks can be mainly divided into two categories according to the styles of the network dynamics, that is, the approaches focusing on linear dynamic networks and the methods focusing on nonlinear dynamic 15 networks.…”

“…So far, the studies exploiting the structure-based control of complex networks can be mainly divided into two categories according to the styles of the network dynamics, that is, the approaches focusing on linear dynamic networks and the methods focusing on nonlinear dynamic 15 networks. For linear dynamic networks, many researchers have developed structural control tools including the Liu's Maximum Matching Sets (MMS) based control methods[9, 10,11,18] and the exact control method [12] and the Min-imum Dominating Sets (MDS) based control methods [13,19] to identify the minimum number of input nodes that need to be controlled by external signals 20 for the system to achieve the desired control objectives. The diagram of EC control scheme and Lius control scheme and MDS control scheme are shown in Figure S1.For the nonlinear dynamic networks, an analytical tool called a feedback vertex set based control (FC) has been shown to study the control of large networks in a reliable and nonlinear manner, where the network structure is 25 prior-known and the functional form of the governing equations is not specified, but must satisfy some properties [20,21].…”

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

“…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.…”

“…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).…”