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Lei Y, Wang YW, Liu XK, Liu ZW. Distributed Event-Triggered Synchronization of Interconnected Linear Two-Time-Scale Systems With Switching Topology. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:13714-13726. [PMID: 34665756 DOI: 10.1109/tcyb.2021.3119067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.
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Lei Y, Wang YW, Morarescu IC, Xiao JW. Guaranteed Cost for an Event-Triggered Consensus Strategy for Interconnected Two Time-Scales Systems With Structured Uncertainty. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:4370-4380. [PMID: 33108305 DOI: 10.1109/tcyb.2020.3026352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article proposes the design of an event-triggered control strategy for consensus of interconnected two-time scales systems with structured uncertainty. The control design under consideration ensures also that consensus is achieved with an overall guaranteed cost. Since each system involves processes evolving on both fast and slow time scales, two Zeno-free event-triggered mechanisms are designed to independently decide the sampling and transmission instants for the slow and fast states, respectively. As the first step, we design an event-triggering consensus protocol in the ideal/nominal case when the interconnected systems are not affected by uncertainties and the interactions happen over a fixed interaction network. Next, the results are extended in order to take into account structured uncertainties affecting the systems' dynamics. At this step, we go further and we provide sufficient conditions for event-triggering consensus with a guaranteed overall cost. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.
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Further results on asymptotic and finite-time stability analysis of fractional-order time-delayed genetic regulatory networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.088] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Morrison M, Kutz JN. Nonlinear control of networked dynamical systems. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021; 8:174-189. [PMID: 33997094 PMCID: PMC8117950 DOI: 10.1109/tnse.2020.3032117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.
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Affiliation(s)
- Megan Morrison
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195 USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195 USA
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Jennawasin T, Lin CL, Banjerdpongchai D. Parameter-dependent linear matrix inequality approach to robust state estimation of noisy genetic networks. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
The authors examine collective rhythms in a general multicell system with both linearly diffusive and nondiffusive couplings. The effect of coupling on synchronization through intercellular signaling in a population of Escherichia coli cells is studied. In particular, a synchronization solution is given through the auxiliary individual system for 2 types of couplings. The sufficient conditions for the global synchronization of such a coupled system are derived based on the Lyapunov function method. The authors show that an appropriate design of the coupling and the inner-linking matrix can ensure global synchronization of the coupled synthetic biological system. Moreover, they demonstrate that the dynamics of an individual cell with coupling and without coupling may be qualitatively different; one is oscillatory, and the other is steady state. The change from a nonoscillatory state to an oscillatory one is induced by appropriate coupling, which also entrains all cells to synchronization. These results establish not only a theoretical foundation but also a quantitative basis for understanding the essential cooperative dynamics, such as collective rhythms or synchronization, in a population of cells.
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Affiliation(s)
- Ruiqi Wang
- Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, Japan
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Cai C, Wang Z, Xu J, Liu X, Alsaadi FE. An Integrated Approach to Global Synchronization and State Estimation for Nonlinear Singularly Perturbed Complex Networks. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:1597-1609. [PMID: 25265621 DOI: 10.1109/tcyb.2014.2356560] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper aims to establish a unified framework to handle both the exponential synchronization and state estimation problems for a class of nonlinear singularly perturbed complex networks (SPCNs). Each node in the SPCN comprises both "slow" and "fast" dynamics that reflects the singular perturbation behavior. General sector-like nonlinear function is employed to describe the nonlinearities existing in the network. All nodes in the SPCN have the same structures and properties. By utilizing a novel Lyapunov functional and the Kronecker product, it is shown that the addressed SPCN is synchronized if certain matrix inequalities are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that dynamics (both slow and fast) of the estimation error is guaranteed to be globally asymptotically stable. Again, a matrix inequality approach is developed for the state estimation problem. Two numerical examples are presented to verify the effectiveness and merits of the proposed synchronization scheme and state estimation formulation. It is worth mentioning that our main results are still valid even if the slow subsystems within the network are unstable.
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Ren F, Cao F, Cao J. Mittag–Leffler stability and generalized Mittag–Leffler stability of fractional-order gene regulatory networks. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.02.049] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Sun Y, Feng G, Cao J. Robust stochastic stability analysis of genetic regulatory networks with disturbance attenuation. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.09.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Liu Y, Jiang H. Exponential stability of genetic regulatory networks with mixed delays by periodically intermittent control. Neural Comput Appl 2011. [DOI: 10.1007/s00521-011-0551-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Ghaffari N, Ivanov I, Qian X, Dougherty ER. A CoD-based reduction algorithm for designing stationary control policies on Boolean networks. ACTA ACUST UNITED AC 2010; 26:1556-63. [PMID: 20421196 DOI: 10.1093/bioinformatics/btq225] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
MOTIVATION Gene regulatory networks serve as models from which to derive therapeutic intervention strategies, in particular, stationary control policies over time that shift the probability mass of the steady state distribution (SSD) away from states associated with undesirable phenotypes. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, network reduction is a fundamental issue for intervention. RESULTS The network model that has been most used for the study of intervention in gene regulatory networks is the probabilistic Boolean network (PBN), which is a collection of constituent Boolean networks (BNs) with perturbation. In this article, we propose an algorithm that reduces a BN with perturbation, designs a control policy on the reduced network and then induces that policy to the original network. The coefficient of determination (CoD) is used to choose a gene for deletion, and a reduction mapping is used to rewire the remaining genes. This CoD-reduction procedure is used to construct a reduced network, then either the previously proposed mean first-passage time (MFPT) or SSD stationary control policy is designed on the reduced network, and these policies are induced to the original network. The efficacy of the overall algorithm is demonstrated on networks of 10 genes or less, where it is possible to compare the steady state shifts of the induced and original policies (because the latter can be derived), and by applying it to a 17-gene gastrointestinal network where it is shown that there is substantial beneficial steady state shift. AVAILABILITY The code for the algorithms is available at: http://gsp.tamu.edu/Publications/supplementary/ghaffari10a/ Please Contact Noushin Ghaffari at nghaffari@tamu.edu for further questions. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Noushin Ghaffari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
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Wang Z, Liu G, Sun Y, Wu H. Robust stability of stochastic delayed genetic regulatory networks. Cogn Neurodyn 2009; 3:271-80. [PMID: 19642023 DOI: 10.1007/s11571-009-9077-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2008] [Revised: 01/31/2009] [Accepted: 01/31/2009] [Indexed: 11/29/2022] Open
Abstract
Gene regulation is an intrinsically noisy process, which is subject to intracellular and extracellular noise perturbations and environment fluctuations. In this paper, we consider the robust stability analysis problem of genetic regulatory networks with time-varying delays and stochastic perturbation. Different from other papers, the genetic regulate system considers not only stochastic perturbation but also parameter disturbances, it is in close proximity to the real gene regulation process than determinate model. Based on the Lyapunov functional theory, sufficient conditions are given to ensure the stability of the genetic regulatory networks. All the stability conditions are given in terms of LMIs which are easy to be verified. Illustrative examples are presented to show the effectiveness of the obtained results.
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Affiliation(s)
- Zhengxia Wang
- School of Science, Chongqing Jiaotong University, Chongqing, China,
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Robust stability of stochastic genetic regulatory networks with discrete and distributed delays. Soft comput 2009. [DOI: 10.1007/s00500-009-0417-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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The impact of time delays on the robustness of biological oscillators and the effect of bifurcations on the inverse problem. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2008:327503. [PMID: 19079585 PMCID: PMC3192793 DOI: 10.1155/2009/327503] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2008] [Accepted: 08/14/2008] [Indexed: 12/18/2022]
Abstract
Differential equation models for biological oscillators are often not robust with respect to parameter variations. They are based on chemical reaction kinetics, and solutions typically converge to a fixed point. This behavior is in contrast to real biological oscillators, which work reliably under varying conditions. Moreover, it complicates network inference from time series data. This paper investigates differential equation models for biological oscillators from two perspectives. First, we investigate the effect of time delays on the robustness of these oscillator models. In particular, we provide sufficient conditions for a time delay to cause oscillations by destabilizing a fixed point in two-dimensional systems. Moreover, we show that the inclusion of a time delay also stabilizes oscillating behavior in this way in larger networks. The second part focuses on the inverse problem of estimating model parameters from time series data. Bifurcations are related to nonsmoothness and multiple local minima of the objective function.
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Robust simplifications of multiscale biochemical networks. BMC SYSTEMS BIOLOGY 2008; 2:86. [PMID: 18854041 PMCID: PMC2654786 DOI: 10.1186/1752-0509-2-86] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2008] [Accepted: 10/14/2008] [Indexed: 12/21/2022]
Abstract
Background Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. Results We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in [1]. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-κB pathway. Conclusion Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.
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Zhou T, Zhang J, Yuan Z, Chen L. Synchronization of genetic oscillators. CHAOS (WOODBURY, N.Y.) 2008; 18:037126. [PMID: 19045500 DOI: 10.1063/1.2978183] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Synchronization of genetic or cellular oscillators is a central topic in understanding the rhythmicity of living organisms at both molecular and cellular levels. Here, we show how a collective rhythm across a population of genetic oscillators through synchronization-induced intercellular communication is achieved, and how an ensemble of independent genetic oscillators is synchronized by a common noisy signaling molecule. Our main purpose is to elucidate various synchronization mechanisms from the viewpoint of dynamics, by investigating the effects of various biologically plausible couplings, several kinds of noise, and external stimuli. To have a comprehensive understanding on the synchronization of genetic oscillators, we consider three classes of genetic oscillators: smooth oscillators (exhibiting sine-like oscillations), relaxation oscillators (displaying jump dynamics), and stochastic oscillators (noise-induced oscillation). For every class, we further study two cases: with intercellular communication (including phase-attractive and repulsive coupling) and without communication between cells. We find that an ensemble of smooth oscillators has different synchronization phenomena from those in the case of relaxation oscillators, where noise plays a different but key role in synchronization. To show differences in synchronization between them, we make comparisons in many aspects. We also show that a population of genetic stochastic oscillators have their own synchronization mechanisms. In addition, we present interesting phenomena, e.g., for relaxation-type stochastic oscillators coupled to a quorum-sensing mechanism, different noise intensities can induce different periodic motions (i.e., inhomogeneous limit cycles).
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Affiliation(s)
- Tianshou Zhou
- State Key Laboratory of Biocontrol Guangzhou Center for Bioinformatics, School of Life Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
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Cao J, Ren F. Exponential Stability of Discrete-Time Genetic Regulatory Networks With Delays. ACTA ACUST UNITED AC 2008; 19:520-3. [PMID: 18334369 DOI: 10.1109/tnn.2007.911748] [Citation(s) in RCA: 153] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jinde Cao
- Department of Mathematics, Southeast University, Nanjing 210096, China.
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Ren F, Cao J. Asymptotic and robust stability of genetic regulatory networks with time-varying delays. Neurocomputing 2008. [DOI: 10.1016/j.neucom.2007.03.011] [Citation(s) in RCA: 241] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhang S, Jin G, Zhang XS, Chen L. Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 2007; 7:2856-69. [PMID: 17703505 DOI: 10.1002/pmic.200700095] [Citation(s) in RCA: 96] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.
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Affiliation(s)
- Shihua Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
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Li C, Chen L, Aihara K. Stability of Genetic Networks With SUM Regulatory Logic: Lur'e System and LMI Approach. ACTA ACUST UNITED AC 2006. [DOI: 10.1109/tcsi.2006.883882] [Citation(s) in RCA: 259] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Clyde RG, Bown JL, Hupp TR, Zhelev N, Crawford JW. The role of modelling in identifying drug targets for diseases of the cell cycle. J R Soc Interface 2006; 3:617-27. [PMID: 16971330 PMCID: PMC1664649 DOI: 10.1098/rsif.2006.0146] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2006] [Accepted: 07/11/2006] [Indexed: 01/20/2023] Open
Abstract
The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.
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Affiliation(s)
- Robert G Clyde
- SIMBIOS, University of Abertay DundeeKydd Building, Bell Street, Dundee DD1 1HG, UK
| | - James L Bown
- SIMBIOS, University of Abertay DundeeKydd Building, Bell Street, Dundee DD1 1HG, UK
| | - Ted R Hupp
- CRUK Cell Signalling Unit, University of EdinburghSouth Crewe Road, Edinburgh EH4 2XR, UK
| | - Nikolai Zhelev
- SIMBIOS, University of Abertay DundeeKydd Building, Bell Street, Dundee DD1 1HG, UK
| | - John W Crawford
- SIMBIOS, University of Abertay DundeeKydd Building, Bell Street, Dundee DD1 1HG, UK
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Zhou T, Chen L, Aihara K. Molecular communication through stochastic synchronization induced by extracellular fluctuations. PHYSICAL REVIEW LETTERS 2005; 95:178103. [PMID: 16383875 DOI: 10.1103/physrevlett.95.178103] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2004] [Revised: 03/17/2005] [Indexed: 05/05/2023]
Abstract
We model a synthetic gene regulatory network in a microbial cell, and investigate the effect of noises on cell-cell communication in a well-mixed multicellular system. A biologically plausible model is developed for cellular communication in an indirectly coupled multicellular system. Without extracellular noises, all cells, in spite of interaction among them, behave irregularly due to independent intracellular noises. On the other hand, extracellular noises that are common to all cells can induce collective dynamics and stochastically synchronize the multicellular system by actively enhancing the integrated interchange of signaling molecules.
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Affiliation(s)
- Tianshou Zhou
- School of Mathematics and Computational Sciences, Zhongshan University, Guangzhou 510275, China.
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Abstract
MOTIVATION All cell components exhibit intracellular noise on account of random births and deaths of individual molecules, and extracellular noise because of environment perturbations. Gene regulation in particular, is an inherently noisy process with transcriptional control, alternative splicing, translation, diffusion and chemical modification reactions, all of which involve stochastic fluctuations. Such stochastic noises may not only affect the dynamics of the entire system but may also be exploited by living organisms to actively facilitate certain functions, such as cooperative behavior and communication. RESULTS We have provided a general model and an analytic tool to examine the cooperative behavior of a multicell system with both intracellular and extracellular stochastic fluctuations. A multicell system with a synthetic gene network is adopted to demonstrate the effects of noises and coupling on collective dynamics. These results establish not only a theoretical foundation but also a quantitative basis for understanding essential roles of noises on cooperative dynamics, such as synchronization and communication among cells.
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Affiliation(s)
- Luonan Chen
- Department of Electrical Engineering and Electronics, Osaka Sangyo University, Daito, Osaka 574-8530, Japan.
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