Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Abstract

Background: A novel data-driven Boolean model, namely, the fundamental Boolean model (FBM), has been proposed to draw genetic regulatory insights into gene activation, inhibition, and protein decay, published in 2018. This novel Boolean model facilitates the analysis of the activation and inhibition pathways. However, the novel model does not handle the situation well, where genetic regulation might require more time steps to complete.

Methods: Here, we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others. We denoted this extension model as the temporal fundamental Boolean model (TFBM) and related networks as the temporal fundamental Boolean networks (TFBNs). The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.

Results: We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets, which are in the form of short-time series. The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia, and each sample has three time points: 0 hour (before GC treatment), 6/8 hours (after GC treatment) and 24 hours (after GC treatment).

Conclusion: We conclude that the proposed TFBM unlocks their predecessor’s limitation, i.e., FBM, that could help pharmaceutical agents identify any side effects on clinic-related data. New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.

Graphical abstract

Keywords

Boolean modelling / Boolean network / time series data / network inference / data-driven boolean modelling / fundamental boolean model / fundamental boolean networks / orchard cube

References

[1] Chen,L., Kulasiri,D. ( 2018). A novel data-driven Boolean model for genetic regulatory networks. Front. Physiol., 9 : 1328

[2] Schmidt,S., Rainer,J., Riml,S., Ploner,C., Jesacher,S., ller,C., Presul,E., Skvortsov,S., Crazzolara,R., Fiegl,M. . ( 2006). Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia. Blood, 107 : 2061– 2069

[3] Shmulevich,I., Dougherty,E. R., Kim,S. ( 2002a). Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18 : 261– 274

[4] Saboury,A. ( 2009). Enzyme inhibition and activation: A general theory. J. Iran. Chem. Soc., 6 : 219– 229

[5] Fontes,R., Ribeiro,J. M. ( 2000). Inhibition and activation of enzymes. The effect of a modifier on the reaction rate and on kinetic parameters. Acta Biochim. Pol., 47 : 233– 257

[6] Naldi,A., Chaouiya,C. ( 2006). Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics, 22 : e124– e131

[7] Ruz,G. A., Goles,E., Montalva,M. Fogel,G. ( 2014). Dynamical and topological robustness of the mammalian cell cycle network: a reverse engineering approach. Biosystems, 115 : 23– 32

[8] Hwang,W. ( 2010). Cell signaling dynamics analysis in leukemia with switching Boolean networks. Comput. Syst. Biol., 13 : 168– 175

[9] Saadatpour,A., Wang,R. S., Liao,A., Liu,X., Loughran,T. P., Albert,I. ( 2011). Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLOS Comput. Biol., 7 : e1002267

[10] udo,J. G. ( 2013). An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks. Chaos, 23 : 025111

[11] Saadatpour,A., Albert,R. Reluga,T. ( 2013). A reduction method for Boolean network models proven to conserve attractors. SIAM J. Appl. Dyn. Syst., 12 : 1997– 2011

[12] Campbell,C. ( 2014). Stabilization of perturbed Boolean network attractors through compensatory interactions. BMC Syst. Biol., 8 : 53

[13] Saez-Rodriguez,J., Simeoni,L., Lindquist,J. A., Hemenway,R., Bommhardt,U., Arndt,B., Haus,U. U., Weismantel,R., Gilles,E. D., Klamt,S. . ( 2007). A logical model provides insights into T cell receptor signaling. PLOS Comput. Biol., 3 : e163

[14] Wittmann,D. M., Krumsiek,J., Saez-Rodriguez,J., Lauffenburger,D. A., Klamt,S. Theis,F. ( 2009). Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst. Biol., 3 : 98

[15] Polyanin, A. D. and Zaitsev, V. F. (2003) Handbook of Exact Solutions for Ordinary Differential Equations (2nd ed.). Boca Raton: Chapman & Hall/CRC Press

[16] Ling,H., Samarasinghe,S. ( 2013). Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics. Biosystems, 114 : 191– 205

[17] Wang,Z., Huang,D., Meng,H. ( 2013). A new fast algorithm for solving the minimum spanning tree problem based on DNA molecules computation. Biosystems, 114 : 1– 7

[18] Kim,S. Y., Imoto,S. ( 2003). Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform., 4 : 228– 235

[19] Akutsu,T., Miyano,S. ( 1999). Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput., 1999 : 17– 28

[20] Liu,F., Zhang,S. W., Guo,W. F., Wei,Z. G. ( 2016). Inference of gene regulatory network based on local Bayesian networks. PLOS Comput. Biol., 12 : e1005024

[21] Wu,H. C., Zhang,L. Chan,S. ( 2014). Reconstruction of gene regulatory networks from short time series high throughput data: Review and New Findings. In:19th International Conference on Digital Signal Processing (DSP), HongKong IEEE

[22] ekA.. ( 2012) Mathematical modelling of gene regulatory networks. Applied Biological Engineering‒ Principles and Practice, Naik, G. R. (ed.) Vol. 5. London

[23] Liang,S., Fuhrman,S. ( 1998). Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput., 1998 : 18– 29

[24] Traynard,P., Monteiro,P. T., Saez-Rodriguez,J., Helikar,T., Thieffry,D. ( 2016). Logical modeling and dynamical analysis of cellular networks. Front. Genet., 7 : 94

[25] Barberis,M., Todd,R. G. ( 2017). Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models. FEMS Yeast Res., 17 : fow103

[26] Traynard,P., Tobalina,L., Eduati,F., Calzone,L. ( 2017). Logic modeling in quantitative systems pharmacology. CPT Pharmacometrics Syst. Pharmacol., 6 : 499– 511

[27] Wang,R. S., Saadatpour,A. ( 2012). Boolean modeling in systems biology: an overview of methodology and applications. Phys. Biol., 9 : 055001

[28] Xiao,Y. ( 2009). A tutorial on analysis and simulation of Boolean gene regulatory network models. Curr. Genomics, 10 : 511– 525

[29] SamuelssonB.. (2006) Dynamics in random Boolean networks. In: Department of Theoretical Physics. Lund: Lund University

[30] Silverbush,D., Grosskurth,S., Wang,D., Powell,F., Gottgens,B., Dry,J. ( 2017). Cell-specific computational modeling of the PIM pathway in acute myeloid leukemia. Cancer Res., 77 : 827– 838

[31] Kauffman,S. ( 1969). Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol., 22 : 437– 467

[32] Kauffman,S., Peterson,C., Samuelsson,B. ( 2003). Random Boolean network models and the yeast transcriptional network. Proc. Natl. Acad. Sci. USA, 100 : 14796– 14799

[33] Jacob,F. ( 1961). Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol., 3 : 318– 356

[34] ShmulevichI.. and Dougherty, E. (2005) Modeling genetic regulatory networks with probabilistic Boolean networks. New York: Hindawi

[35] Russo,G., Zegar,C. ( 2003). Advantages and limitations of microarray technology in human cancer. Oncogene, 22 : 6497– 6507

[36] Taub,F. E., DeLeo,J. M. Thompson,E. ( 1983). Sequential comparative hybridizations analyzed by computerized image processing can identify and quantitate regulated RNAs. DNA, 2 : 309– 327

[37] Gautier,L., Cope,L., Bolstad,B. M. Irizarry,R. ( 2004). affy‒analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20 : 307– 315

[38] Silvescu,A. ( 2001). Temporal Boolean network models of genetic networks and their inference from gene expression time series. Complex Syst., 13 : 61– 78

[39] Ernst,J. ( 2006). STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics, 7 : 191

[40] Ernst,J., Nau,G. J. ( 2005). Clustering short time series gene expression data. Bioinformatics, 21 : i159– i168

[41] Chaiboonchoe,A. ( 2010). Identification of glucocorticoid-regulated genes and inferring their network focused on the glucocorticoid receptor in childhood leukaemia, based on microarray data and pathway databases, pp. 170. Lincoln University, New Zealand

[42] Wang,Z., Yang,F., Ho,D. W., Swift,S., Tucker,A. ( 2008). Stochastic dynamic modeling of short gene expression time-series data. IEEE Trans. Nanobioscience, 7 : 44– 55

[43] Tchagang,A. B., Phan,S., Famili,F., Shearer,H., Fobert,P., Huang,Y., Zou,J., Huang,D., Cutler,A., Liu,Z. . ( 2012). Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm. BMC Bioinformatics, 13 : 54

[44] Bockmayr, A. (2009) Logic-based modeling in systems biology. In: Logic Programming and Nonmonotonic Reasoning, Erdem, E., Lin, F. and Schaub, T. (eds.). Heidelberg: Springer

[45] Irizarry,R. A., Wu,Z. Jaffee,H. ( 2006). Comparison of Affymetrix GeneChip expression measures. Bioinformatics, 22 : 789– 794

[46] Affymetrix, Inc. (2002) Statistical algorithms description document. Part number 701137 Rev 3

[47] Irizarry,R. A., Hobbs,B., Collin,F., Beazer-Barclay,Y. D., Antonellis,K. J., Scherf,U. Speed,T. ( 2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4 : 249– 264

[48] Wu,Z., Irizarry,R. A., Gentleman,R., Martinez-Murillo,F. ( 2004). A Model-Based Background Adjustment for Oligonucleotide Expression Arrays. J. Am. Stat. Assoc., 99 : 909– 917

[49] WeinbergR.. (2007) The biology of cancer. New York: Garland Science

[50] Hornberg,J. J., Bruggeman,F. J., Westerhoff,H. V. ( 2006). Cancer: a systems biology disease. Biosystems, 83 : 81– 90

[51] Hanahan,D. Weinberg,R. ( 2000). The hallmarks of cancer. Cell, 100 : 57– 70

[52] Martinez,J. D. . ( 2003). Molecular Biology of Cancer. Canada: John Wiley & Sons, Inc

[53] Banjar,H., Adelson,D., Brown,F. ( 2017). Intelligent techniques using molecular data analysis in leukaemia: An opportunity for personalized medicine support system. BioMed Res. Int., 2017 : 3587309

[54] Inaba,H., Greaves,M. Mullighan,C. ( 2013). Acute lymphoblastic leukaemia. Lancet, 381 : 1943– 1955

[55] Hunger,S. P. Mullighan,C. ( 2015). Acute lymphoblastic leukemia in children. N. Engl. J. Med., 373 : 1541– 1552

[56] GreenD.. R. (2011) Means to an End: Apoptosis and Other Cell Death Mechanisms. Cold Spring Harbor Laboratory Press

[57] Lakna. Difference Between Apoptosis and Necrosis. (2017) Available from the website of PEDIAA

[58] Schmidt,S., Rainer,J., Ploner,C., Presul,E., Riml,S. ( 2004). Glucocorticoid-induced apoptosis and glucocorticoid resistance: molecular mechanisms and clinical relevance. Cell Death Differ., 11 : S45– S55

[59] Thompson,E. B. Johnson,B. ( 2003). Regulation of a distinctive set of genes in glucocorticoid-evoked apoptosis in CEM human lymphoid cells. Recent Prog. Horm. Res., 58 : 175– 197

[60] Planey,S. L., Abrams,M. T., Robertson,N. M. ( 2003). Role of apical caspases and glucocorticoid-regulated genes in glucocorticoid-induced apoptosis of pre-B leukemic cells. Cancer Res, 63 : 172– 178

[61] Schmidt,S., Irving,J. A., Minto,L., Matheson,E., Nicholson,L., Ploner,A., Parson,W., Kofler,A., Amort,M., Erdel,M. . ( 2006). Glucocorticoid resistance in two key models of acute lymphoblastic leukemia occurs at the level of the glucocorticoid receptor. FASEB J., 20 : 2600– 2602

[62] Smith,L. K. Cidlowski,J. ( 2010). Glucocorticoid-induced apoptosis of healthy and malignant lymphocytes. Prog. Brain Res., 182 : 1– 30

[63] Rhen,T. Cidlowski,J. ( 2005). Antiinflammatory action of glucocorticoids‒new mechanisms for old drugs. N. Engl. J. Med., 353 : 1711– 1723

[64] Rainer,J., Lelong,J., Bindreither,D., Mantinger,C., Ploner,C., Geley,S. ( 2012). Research resource: transcriptional response to glucocorticoids in childhood acute lymphoblastic leukemia. Mol. Endocrinol., 26 : 178– 193

[65] Yoshida,N. L., Miyashita,T., U,M., Yamada,M., Reed,J. C., Sugita,Y. ( 2002). Analysis of gene expression patterns during glucocorticoid-induced apoptosis using oligonucleotide arrays. Biochem. Biophys. Res. Commun., 293 : 1254– 1261

[66] Bachmann,P. S., Gorman,R., Papa,R. A., Bardell,J. E., Ford,J., Kees,U. R., Marshall,G. M. Lock,R. ( 2007). Divergent mechanisms of glucocorticoid resistance in experimental models of pediatric acute lymphoblastic leukemia. Cancer Res., 67 : 4482– 4490

[67] Carlet,M., Janjetovic,K., Rainer,J., Schmidt,S., mayer,R., Mann,G., Prelog,M., Meister,B., Ploner,C. ( 2010). Expression, regulation and function of phosphofructo-kinase/fructose-biphosphatases (PFKFBs) in glucocorticoid-induced apoptosis of acute lymphoblastic leukemia cells. BMC Cancer, 10 : 638

[68] Lee,W. P. Tzou,W. ( 2009). Computational methods for discovering gene networks from expression data. Brief. Bioinform., 10 : 408– 423

[69] Ay,A. Arnosti,D. ( 2011). Mathematical modeling of gene expression: a guide for the perplexed biologist. Crit. Rev. Biochem. Mol. Biol., 46 : 137– 151

[70] Wang,Y., Zhang,X. S. ( 2011). Computational systems biology: integration of sequence, structure, network, and dynamics. BMC Syst. Biol., 5 : S1

[71] Hood,L. ( 2013). Systems biology and p4 medicine: past, present, and future. Rambam Maimonides Med. J., 4 : e0012

[72] Barry,M. ( 1990). Enzyme induction and inhibition. Pharmacol. Ther., 48 : 71– 94

[73] Lazzarini,N., Widera,P., Williamson,S., Heer,R., Krasnogor,N. ( 2016). Functional networks inference from rule-based machine learning models. BioData Min., 9 : 28

[74] Albert,R. ( 2004). Boolean modeling of genetic regulatory networks. Lect. Notes Phys, 650 : 459– 481

[75] Hopfensitz,M., ssel,C., Maucher,M. Kestler,H. ( 2013). Attractors in Boolean networks: a tutorial. Comput. Stat., 28 : 19– 36

[76] HanJ.., Kamber, M. and Pei, J. (2012) Data Mining-concepts and Techniques, 3rd ed. Morgan Kaufmann Publishers

[77] RefSeq. ( 2020) Available from the website of NIH

[78] Calvo,F., Ranftl,R., Hooper,S., Farrugia,A. J., Moeendarbary,E., Bruckbauer,A., Batista,F., Charras,G. ( 2015). Cdc42EP3/BORG2 and septin network enables mechano-transduction and the emergence of cancer-associated fibroblasts. Cell Rep., 13 : 2699– 2714

[79] Genecards. org. F13A1 Gene. ( 2020) Available from the website of GeneCards

[80] Genecards. org. TUBA4A. ( 2020) Available from the website of GeneCards

[81] Zhang,H., Duan,J., Qu,Y., Deng,T., Liu,R., Zhang,L., Bai,M., Li,J., Ning,T., Ge,S. . ( 2016). Onco-miR-24 regulates cell growth and apoptosis by targeting BCL2L11 in gastric cancer. Protein Cell, 7 : 141– 151

[82] BaiT. ZhaoY. LiuY. CaiB. DongN.. ( 2019) Effect of KNL1 on the proliferation and apoptosis of colorectal cancer cells. Technol. Cancer Res. Treat., 18, 1533033819858668

[83] Zhang,R., Deng,Y., Lv,Q., Xing,Q., Pan,Y., Liang,J., Jiang,M., Wei,Y., Shi,D., Xie,B. . ( 2020). SQLE promotes differentiation and apoptosis of bovine skeletal muscle-derived mesenchymal stem cells. Cell. Reprogram., 22 : 22– 29

[84] Zou,W., Ma,X., Hua,W., Chen,B., Huang,Y., Wang,D. ( 2016). BRIP1 inhibits the tumorigenic properties of cervical cancer by regulating RhoA GTPase activity. Oncol. Lett., 11 : 551– 558

[85] Cui,H., Wang,Q., Lei,Z., Feng,M., Zhao,Z., Wang,Y. ( 2019). DTL promotes cancer progression by PDCD4 ubiquitin-dependent degradation. J. Exp. Clin. Cancer Res., 38 : 350

[86] Chaiboonchoe,A., Samarasinghe,S., Kulasiri,D. ( 2014). Integrated analysis of gene network in childhood leukemia from microarray and pathway databases. BioMed Res. Int., 2014 : 278748

[87] ChaiboonchoeA. SamarasingheS.. ( 2009) Using emergent clustering methods to analyse short time series gene expression data from childhood leukemia treated with glucocorticoids. In: 18th World IMACS/MODSIM Congress, pp. 13‒ 17. Cairns, Australia

[88] Berrar, D. P. , Dubitzky, W. and Granzow, M. (2003) Introduction to microarray data analysis. In: A Practical Approach to Microarray Data Analysis. Kluwer Academic Publishers

[89] Li,Y., Jann,T. ( 2019). Benchmarking time-series data discretization on inference methods. Bioinformatics, 35 : 3102– 3109

[90] CatlettJ.. ( 1991) On changing continuous attributes into ordered discrete attributes. In: EWSL’91: Proceedings of the 5th European Conference on European Working Session on Learning, pp. 164‒ 178. Heidelberg: Springer

[91] JiangS. LiX. Zheng Q.. ( 2009) Approximate equal frequency discretization method. In: 2009 WRI Global Congress on Intelligent Systems, pp. 514‒ 518, Xiamen, China

[92] MacQueenJ.. (1967) Some methods for classification and analysis of multivariate observations. In: Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press

[93] Gupta,A., Mehrotra,K. Mohan,C. ( 2009). A clustering based discretization for supervised learning. Statis. & Prob. Lett. 80, 816– 824

[94] Dimitrova,E. S., Licona,M. P., McGee,J. ( 2010). Discretization of time series data. J. Comput. Biol., 17 : 853– 868

[95] Schmidberger, G. and Frank, E. (2005) Unsupervised discretization using tree-based density estimation. In: Knowledge Discovery in Databases: Pkdd, 2005, Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., and Gama, J. (eds). Heidelberg: Springer

[96] ssel,C., Hopfensitz,M. Kestler,H. ( 2010). BoolNet‒an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics, 26 : 1378– 1380

[97] Win,H. M. L. Htwe,N. A. ( 2014). Comparison between edge detection and K-means clustering methods for image segmentation and merging. Inter. J. Scient. Engin. Technol. Res., 3 : 3012– 3017

[98] Wheeler,D. ( 2007). A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996−2003. Int. J. Health Geogr., 6 : 13

[99] Huang,W., Sherman,B. T. Lempicki,R. ( 2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4 : 44– 57

[100] Huang,W., Sherman,B. T. Lempicki,R. ( 2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37 : 1– 13

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