Molecular network control through boolean canalization

Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.

[1]  S. Kauffman,et al.  Critical Dynamics in Genetic Regulatory Networks: Examples from Four Kingdoms , 2008, PloS one.

[2]  B. Stigler,et al.  Boolean Models Can Explain Bistability in the lac Operon , 2008, J. Comput. Biol..

[3]  Seda Arat,et al.  Modeling stochasticity and variability in gene regulatory networks , 2012, EURASIP J. Bioinform. Syst. Biol..

[4]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  L. O’Driscoll Gene Expression Profiling , 2011, Methods in Molecular Biology.

[6]  Matthew Macauley,et al.  Nested Canalyzing Depth and Network Stability , 2011, Bulletin of mathematical biology.

[7]  C. Waddington Canalization of Development and the Inheritance of Acquired Characters , 1942, Nature.

[8]  R. Laubenbacher,et al.  Regulatory patterns in molecular interaction networks. , 2011, Journal of theoretical biology.

[9]  H. Othmer,et al.  The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. , 2003, Journal of theoretical biology.

[10]  M. Kaufman,et al.  From structure to dynamics: frequency tuning in the p53-Mdm2 network I. Logical approach. , 2009, Journal of theoretical biology.

[11]  Alan Veliz-Cuba,et al.  Identification of control targets in Boolean molecular network models via computational algebra , 2015, BMC Systems Biology.

[12]  Zhihui Wang,et al.  Mathematical modeling in cancer drug discovery. , 2014, Drug discovery today.

[13]  Luis Mendoza,et al.  A network model for the control of the differentiation process in Th cells. , 2006, Bio Systems.

[14]  R. Linding,et al.  Network Medicine Strikes a Blow against Breast Cancer , 2012, Cell.

[15]  R. Laubenbacher,et al.  The number of multistate nested canalyzing functions , 2011, 1108.0206.

[16]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[17]  Abdul Salam Jarrah,et al.  Nested Canalyzing, Unate Cascade, and Polynomial Functions. , 2006, Physica D. Nonlinear phenomena.

[18]  A. Datta,et al.  From biological pathways to regulatory networks , 2010, 49th IEEE Conference on Decision and Control (CDC).

[19]  Tomáš Helikar,et al.  A Comprehensive, Multi-Scale Dynamical Model of ErbB Receptor Signal Transduction in Human Mammary Epithelial Cells , 2013, PloS one.

[20]  H. Parthasarathy,et al.  NemaFootPrinter: a web based software for the identification of conserved non-coding genome sequence regions between C. elegans and C. briggsae , 1981, Nature Immunology.

[21]  Edward R. Dougherty,et al.  Probabilistic Boolean Networks - The Modeling and Control of Gene Regulatory Networks , 2010 .

[22]  R. Albert,et al.  Network model of survival signaling in large granular lymphocyte leukemia , 2008, Proceedings of the National Academy of Sciences.

[23]  T. Helikar,et al.  Emergent decision-making in biological signal transduction networks , 2008, Proceedings of the National Academy of Sciences.

[24]  Colin Campbell,et al.  Stabilization of perturbed Boolean network attractors through compensatory interactions , 2014, BMC Systems Biology.

[25]  Yuan Li,et al.  Boolean nested canalizing functions: A comprehensive analysis , 2012, Theor. Comput. Sci..

[26]  Aurélien Naldi,et al.  Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle , 2006, ISMB.

[27]  Q. Ouyang,et al.  The yeast cell-cycle network is robustly designed. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Sui Huang Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery , 1999, Journal of Molecular Medicine.

[29]  Xin Liu,et al.  Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia , 2011, PLoS Comput. Biol..

[30]  S. Bornholdt,et al.  Boolean Network Model Predicts Cell Cycle Sequence of Fission Yeast , 2007, PloS one.

[31]  M. Kaufman,et al.  From structure to dynamics: frequency tuning in the p53-Mdm2 network. II Differential and stochastic approaches. , 2010, Journal of theoretical biology.

[32]  Kensaku Mizuno,et al.  β-Arrestin–Dependent Activation of the Cofilin Pathway Is Required for the Scavenging Activity of the Atypical Chemokine Receptor D6 , 2013, Science Signaling.

[33]  P. Sorger,et al.  Sequential Application of Anticancer Drugs Enhances Cell Death by Rewiring Apoptotic Signaling Networks , 2012, Cell.

[34]  Wei Wang,et al.  Therapeutic Hints from Analyzing the Attractor Landscape of the p53 Regulatory Circuit , 2013, Science Signaling.

[35]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[36]  Kwang-Hyun Cho,et al.  Attractor Landscape Analysis Reveals Feedback Loops in the p53 Network That Control the Cellular Response to DNA Damage , 2012, Science Signaling.

[37]  Matthew Macauley,et al.  Stratification and enumeration of Boolean functions by canalizing depth , 2015, ArXiv.

[38]  S. Kauffman,et al.  Genetic networks with canalyzing Boolean rules are always stable. , 2004, Proceedings of the National Academy of Sciences of the United States of America.