Stabilization of perturbed Boolean network attractors through compensatory interactions

BMC Systems Biology

Réka Albert¹  Colin Campbell² 

[1] Pennsylvania State University, 152E Davey Laboratory, University Park, PA 16802, USA;Pennsylvania State University, 208 Mueller Laboratory, University Park, PA 16802, USA

关键词: Abscisic acid signaling;    T-LGL leukemia;    Interaction modification;    Network manipulation;    Attractors;    Stability;    Signal transduction;    Discrete dynamic models;    Boolean networks;   

Others  :  866405
DOI  :  10.1186/1752-0509-8-53

 received in 2014-02-07, accepted in 2014-04-22,  发布年份 2014

【 摘 要 】

Background

Understanding and ameliorating the effects of network damage are of significant interest, due in part to the variety of applications in which network damage is relevant. For example, the effects of genetic mutations can cascade through within-cell signaling and regulatory networks and alter the behavior of cells, possibly leading to a wide variety of diseases. The typical approach to mitigating network perturbations is to consider the compensatory activation or deactivation of system components. Here, we propose a complementary approach wherein interactions are instead modified to alter key regulatory functions and prevent the network damage from triggering a deregulatory cascade.

Results

We implement this approach in a Boolean dynamic framework, which has been shown to effectively model the behavior of biological regulatory and signaling networks. We show that the method can stabilize any single state (e.g., fixed point attractors or time-averaged representations of multi-state attractors) to be an attractor of the repaired network. We show that the approach is minimalistic in that few modifications are required to provide stability to a chosen attractor and specific in that interventions do not have undesired effects on the attractor. We apply the approach to random Boolean networks, and further show that the method can in some cases successfully repair synchronous limit cycles. We also apply the methodology to case studies from drought-induced signaling in plants and T-LGL leukemia and find that it is successful in both stabilizing desired behavior and in eliminating undesired outcomes. Code is made freely available through the software package BooleanNet.

Conclusions

The methodology introduced in this report offers a complementary way to manipulating node expression levels. A comprehensive approach to evaluating network manipulation should take an "all of the above" perspective; we anticipate that theoretical studies of interaction modification, coupled with empirical advances, will ultimately provide researchers with greater flexibility in influencing system behavior.

【 授权许可】

   
2014 Campbell and Albert; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Leibiger IB, Brismar K, Berggren P-O: Novel aspects on pancreatic beta-cell signal-transduction. Biochem Biophys Res Commun 2010, 396:111-115.
• [2]Muscogiuri G, Chavez AO, Gastaldelli A, Perego L, Tripathy D, Saad MJ, Velloso L, Folli F: The crosstalk between insulin and renin-angiotensin-aldosterone signaling systems and its effect on glucose metabolism and diabetes prevention. Curr Vasc Pharmacol 2008, 6:301-312.
• [3]Gordon KJ, Blobe GC: Role of transforming growth factor-beta superfamily signaling pathways in human disease. Biochim Biophys Acta 2008, 1782:197-228.
• [4]Grzmil M, Hemmings BA: Deregulated signalling networks in human brain tumours. Biochim Biophys Acta BBA - Proteins Proteomics 1804, 2010:476-483.
• [5]Ikushima H, Miyazono K: TGFβ signalling: a complex web in cancer progression. Nat Rev Cancer 2010, 10:415-424.
• [6]Stratton MR, Campbell PJ, Futreal PA: The cancer genome. Nature 2009, 458:719-724.
• [7]Kim YJ, Hwang JS, Hong YB, Bae I, Seong Y-S: Transforming growth factor beta receptor i inhibitor sensitizes drug-resistant pancreatic cancer cells to gemcitabine. Anticancer Res 2012, 32:799-806.
• [8]Campbell C, Thakar J, Albert R: Network analysis reveals cross-links of the immune pathways activated by bacteria and allergen. Phys Rev E 2011, 84:031929.
• [9]Thakar J, Pilione M, Kirimanjeswara G, Harvill ET, Albert R: Modeling systems-level regulation of host immune responses. PLoS Comput Biol 2007, 3:e109.
• [10]Balkwill F: Cancer and the chemokine network. Nat Rev Cancer 2004, 4:540-550.
• [11]Chuang H-Y, Lee E, Liu Y-T, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol 2007, 3:140. doi:10.1038/msb4100180
• [12]Creixell P, Schoof EM, Erler JT, Linding R: Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 2012, 30:842-848.
• [13]Beisel CL, Storz G: Base pairing small RNAs and their roles in global regulatory networks. FEMS Microbiol Rev 2010, 34:866-882.
• [14]Ng W-L, Bassler BL: Bacterial quorum-sensing network architectures. Annu Rev Genet 2009, 43:197-222.
• [15]Allesina S, Pascual M: Network structure, predator–prey modules, and stability in large food webs. Theor Ecol 2008, 1:55-64.
• [16]Bascompte J, Jordano P, Melián CJ, Olesen JM: The nested assembly of plant–animal mutualistic networks. Proc Natl Acad Sci 2003, 100:9383-9387.
• [17]Newman MEJ: Networks: an Introduction. New York: Oxford University Press; 2010.
• [18]Watts DJ, Dodds PS: Influentials, networks, and public opinion formation. J Consum Res 2007, 34:441-458.
• [19]Wu F, Huberman BA, Adamic LA, Tyler JR: Information flow in social groups. Phys Stat Mech Its Appl 2004, 337:327-335.
• [20]Albert R, Barabási A-L: Statistical mechanics of complex networks. Rev Mod Phys 2002, 74:47-97.
• [21]Wasserman S: Social Network Analysis: Methods and Applications. Cambridge, UK: Cambridge University Press; 1994.
• [22]Freeman LC: Centrality in social networks conceptual clarification. Soc Netw 1978, 1:215-239.
• [23]Bornholdt S: Boolean network models of cellular regulation: prospects and limitations. J R Soc Interface 2008, 5(Suppl 1):S85-S94.
• [24]Wang R-S, Saadatpour A, Albert R: Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol 2012, 9:055001.
• [25]Mason O, Verwoerd M: Graph theory and networks in Biology. IET Syst Biol 2007, 1:89-119.
• [26]Albert I, Thakar J, Li S, Zhang R, Albert R: Boolean network simulations for life scientists. Source Code Biol Med 2008, 3:16. BioMed Central Full Text
• [27]Li F, Long T, Lu Y, Ouyang Q, Tang C: The yeast cell-cycle network is robustly designed. Proc Natl Acad Sci U S A 2004, 101:4781-4786.
• [28]Campbell C, Yang S, Albert R, Shea K: A Network Model for Plant–pollinator community assembly. Proc Natl Acad Sci 2011, 108:197-202.
• [29]Shmulevich I, Kauffman SA: Activities and sensitivities in Boolean network models. Phys Rev Lett 2004, 93:048701.
• [30]Pal R, Ivanov I, Datta A, Bittner ML, Dougherty ER: Generating Boolean networks with a prescribed attractor structure. Bioinformatics 2005, 21:4021-4025.
• [31]Li S, Assmann SM, Albert R: Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling. PLoS Biol 2006, 4:e312.
• [32]Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, Albert R, Loughran TP: Network model of survival signaling in large granular lymphocyte leukemia. Proc Natl Acad Sci 2008, 105:16308-16313.
• [33]Davidich MI, Bornholdt S: Boolean network model predicts cell cycle sequence of fission yeast. PLoS ONE 2008, 3:e1672.
• [34]Saadatpour A, Wang R-S, Liao A, Liu X, Loughran TP, Albert I, Albert R: Dynamical and structural analysis of a t cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLoS Comput Biol 2011, 7:e1002267.
• [35]Callaway DS, Newman MEJ, Strogatz SH, Watts DJ: Network robustness and fragility: percolation on random graphs. Phys Rev Lett 2000, 85:5468-5471.
• [36]Rohlf T, Gulbahce N, Teuscher C: Damage spreading and criticality in finite random dynamical networks. Phys Rev Lett 2007, 99:248701.
• [37]Liu Y-Y, Slotine J-J, Barabási A-L: Controllability of complex networks. Nature 2011, 473:167-173.
• [38]Jackson JBC, Kirby MX, Berger WH, Bjorndal KA, Botsford LW, Bourque BJ, Bradbury RH, Cooke R, Erlandson J, Estes JA, Hughes TP, Kidwell S, Lange CB, Lenihan HS, Pandolfi JM, Peterson CH, Steneck RS, Tegner MJ, Warner RR: Historical overfishing and the recent collapse of coastal ecosystems. Science 2001, 293:629-637.
• [39]Cornelius SP, Kath WL, Motter AE: Realistic control of network dynamics. Nat Commun 2013, 4:1942. doi:10.1038/ncomms2939
• [40]Beygelzimer A, Grinstein G, Linsker R, Rish I: Improving network robustness by edge modification. Phys Stat Mech Its Appl 2005, 357:593-612.
• [41]Dueber JE, Mirsky EA, Lim WA: Engineering synthetic signaling proteins with ultrasensitive input/output control. Nat Biotechnol 2007, 25:660-662.
• [42]Grinstead CM: Introduction to Probability. 2nd rev. ed. Providence, RI: American Mathematical Society; 1997.
• [43]Coppersmith D, Winograd S: Matrix multiplication via arithmetic progressions. J Symb Comput 1990, 9:251-280.
• [44]Saadatpour A, Albert I, Albert R: Attractor analysis of asynchronous Boolean models of signal transduction networks. J Theor Biol 2010, 266:641-656.
• [45]Lowe SW, Lin AW: Apoptosis in cancer. Carcinogenesis 2000, 21:485-495.
• [46]Shmulevich I, Dougherty ER, Zhang W: Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics 2002, 18:1319-1331.
• [47]Skerker JM, Perchuk BS, Siryaporn A, Lubin EA, Ashenberg O, Goulian M, Laub MT: Rewiring the specificity of two-component signal transduction systems. Cell 2008, 133:1043-1054.
• [48]Cheng X, Sun M, Socolar JES: Autonomous Boolean modelling of developmental gene regulatory networks. J R Soc Interface R Soc 2013, 10:20120574.
• [49]Chaves M, Sontag ED, Albert R: Methods of robustness analysis for Boolean models of gene control networks. IEE Proc - Syst Biol 2006, 153:154.
• [50]Glass L: Classification of biological networks by their qualitative dynamics. J Theor Biol 1975, 54:85-107.
• [51]Thomas R, Thieffry D, Kaufman M: Dynamical behaviour of biological regulatory networks–I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull Math Biol 1995, 57:247-276.
• [52]Murrugarra D, Veliz-Cuba A, Aguilar B, Arat S, Laubenbacher R: Modeling stochasticity and variability in gene regulatory networks. EURASIP J Bioinforma Syst Biol 2012, 2012:5. BioMed Central Full Text
• [53]Shen-Orr SS, Milo R, Mangan S, Alon U: Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 2002, 31:64-68.
• [54]Ma’ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, Eungdamrong NJ, Weng G, Ram PT, Rice JJ, Kershenbaum A, Stolovitzky GA, Blitzer RD, Iyengar R: Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science 2005, 309:1078-1083.
• [55]Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U: Network motifs: simple building blocks of complex networks. Science 2002, 298:824-827.
• [56]Von Dassow G, Meir E, Munro EM, Odell GM: The segment polarity network is a robust developmental module. Nature 2000, 406:188-192.
• [57]Atlas of Cancer Signalling Networks https://acsn.curie.fr webcite