Literature DB >> 23774067

Dynamics and control at feedback vertex sets. II: a faithful monitor to determine the diversity of molecular activities in regulatory networks.

Atsushi Mochizuki1, Bernold Fiedler, Gen Kurosawa, Daisuke Saito.   

Abstract

Modern biology provides many networks describing regulations between many species of molecules. It is widely believed that the dynamics of molecular activities based on such regulatory networks are the origin of biological functions. However, we currently have a limited understanding of the relationship between the structure of a regulatory network and its dynamics. In this study we develop a new theory to provide an important aspect of dynamics from information of regulatory linkages alone. We show that the "feedback vertex set" (FVS) of a regulatory network is a set of "determining nodes" of the dynamics. The theory is powerful to study real biological systems in practice. It assures that (i) any long-term dynamical behavior of the whole system, such as steady states, periodic oscillations or quasi-periodic oscillations, can be identified by measurements of a subset of molecules in the network, and that (ii) the subset is determined from the regulatory linkage alone. For example, dynamical attractors possibly generated by a signal transduction network with 113 molecules can be identified by measurement of the activity of only 5 molecules, if the information on the network structure is correct. Our theory therefore provides a rational criterion to select key molecules to control a system. We also demonstrate that controlling the dynamics of the FVS is sufficient to switch the dynamics of the whole system from one attractor to others, distinct from the original.
© 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Keywords:  Complex systems; Determining nodes; Feedback vertex set; Informative nodes; Regulatory networks

Mesh:

Year:  2013        PMID: 23774067     DOI: 10.1016/j.jtbi.2013.06.009

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  36 in total

1.  Structure-based control of complex networks with nonlinear dynamics.

Authors:  Jorge Gomez Tejeda Zañudo; Gang Yang; Réka Albert
Journal:  Proc Natl Acad Sci U S A       Date:  2017-06-27       Impact factor: 11.205

2.  Harnessing tipping points in complex ecological networks.

Authors:  Junjie Jiang; Alan Hastings; Ying-Cheng Lai
Journal:  J R Soc Interface       Date:  2019-09-11       Impact factor: 4.118

3.  Control of Intracellular Molecular Networks Using Algebraic Methods.

Authors:  Luis Sordo Vieira; Reinhard C Laubenbacher; David Murrugarra
Journal:  Bull Math Biol       Date:  2019-12-23       Impact factor: 1.758

4.  State observation and sensor selection for nonlinear networks.

Authors:  Aleksandar Haber; Ferenc Molnar; Adilson E Motter
Journal:  IEEE Trans Control Netw Syst       Date:  2017-07-17

5.  Functional observability and target state estimation in large-scale networks.

Authors:  Arthur N Montanari; Chao Duan; Luis A Aguirre; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2022-01-04       Impact factor: 11.205

6.  Influence maximization in Boolean networks.

Authors:  Thomas Parmer; Luis M Rocha; Filippo Radicchi
Journal:  Nat Commun       Date:  2022-06-16       Impact factor: 17.694

7.  NETISCE: a network-based tool for cell fate reprogramming.

Authors:  Lauren Marazzi; Milan Shah; Shreedula Balakrishnan; Ananya Patil; Paola Vera-Licona
Journal:  NPJ Syst Biol Appl       Date:  2022-06-20

Review 8.  Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.

Authors:  Jipeng Yan; Zhuo Hu; Zong-Wei Li; Shiren Sun; Wei-Feng Guo
Journal:  Front Oncol       Date:  2022-05-31       Impact factor: 5.738

9.  Nonlinear control of networked dynamical systems.

Authors:  Megan Morrison; J Nathan Kutz
Journal:  IEEE Trans Netw Sci Eng       Date:  2020-10-19

10.  Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

Authors:  Wei-Feng Guo; Xiangtian Yu; Qian-Qian Shi; Jing Liang; Shao-Wu Zhang; Tao Zeng
Journal:  PLoS Comput Biol       Date:  2021-05-06       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.