Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-08T22:09:08.976Z Has data issue: false hasContentIssue false

Boolean Networks as Predictive Models of Emergent Biological Behaviors

Published online by Cambridge University Press:  04 March 2024

Jordan C. Rozum
Affiliation:
Binghamton University, State University of New York
Colin Campbell
Affiliation:
University of Mount Union
Eli Newby
Affiliation:
Pennsylvania State University
Fatemeh Sadat Fatemi Nasrollahi
Affiliation:
Indiana University
Réka Albert
Affiliation:
Pennsylvania State University

Summary

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.
Get access
Type
Element
Information
Online ISBN: 9781009292955
Publisher: Cambridge University Press
Print publication: 28 March 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Stuart, A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22(3):437467, March 1969. https://doi.org/10.1016/0022-5193(69)90015-0. www.sciencedirect.com/science/article/pii/0022519369900150.Google Scholar
Thomas, René. Boolean formalization of genetic control circuits. Journal of Theoretical Biology, 42(3):563585, 1973. https://doi.org/10.1016/0022-5193(73)90247-6. www.sciencedirect.com/science/article/pii/0022519373902476.CrossRefGoogle ScholarPubMed
Hopfield, J J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8):25542558, April 1982. https://doi.org/10.1073/pnas.79.8.2554. www.pnas.org/doi/abs/10.1073/pnas.79.8.2554.CrossRefGoogle ScholarPubMed
El Houssine Snoussi and René Thomas. Logical identification of all steady states: The concept of feedback loop characteristic states. Bulletin of Mathematical Biology, 55(5):973991, 1993.CrossRefGoogle Scholar
James, P. Crutchfield. The calculi of emergence: Computation, dynamics and induction. Physica D: Nonlinear Phenomena, 75(1):1154, August 1994. https://doi.org/10.1016/0167-2789(94)90273-9. www.sciencedirect.com/science/article/pii/0167278994902739.Google Scholar
Feo, Thomas A. and Resende, Mauricio G. C.. Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2):109133, March 1995. https://doi.org/10.1007/BF01096763.CrossRefGoogle Scholar
Memmott, Jane. The structure of a plant–pollinator food web. Ecology Letters, 2(5):276280, 1999. https://doi.org/10.1046/j.1461-0248.1999.00087.x. https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1461-0248.1999.00087.x.CrossRefGoogle ScholarPubMed
von Dassow, George, Meir, Eli, Munro, Edwin M., and Odell, Garrett M.. The segment polarity network is a robust developmental module. Nature, 406(6792):188–192, July 2000. https://doi.org/10.1038/35018085. www.nature.com/articles/35018085.CrossRefGoogle ScholarPubMed
Thomas, René and Kaufman, Marcelle. Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. Chaos: An Interdisciplinary Journal of Nonlinear Science, 11(1):180195, March 2001a. https://doi.org/10.1063/1.1349893. https://aip.scitation.org/doi/abs/10.1063/1.1349893.CrossRefGoogle ScholarPubMed
Thomas, René and Kaufman, Marcelle. Multistationarity, the basis of cell differentiation and memory. I. Structural conditions of multistationarity and other nontrivial behavior. Chaos: An Interdisciplinary Journal of Nonlinear Science, 11(1):170179, March 2001b. https://doi.org/10.1063/1.1350439. https://aip.scitation.org/doi/abs/10.1063/1.1350439.CrossRefGoogle ScholarPubMed
von Dassow, George and Odell, Garrett M.. Design and constraints of theDrosophila segment polarity module: Robust spatial patterning emerges from intertwined cell state switches. Journal of Experimental Zoology, 294(3):179215, October 2002. https://doi.org/10.1002/jez.10144. http://doi.wiley.com/10.1002/jez.10144.CrossRefGoogle Scholar
Raeymaekers, Luc. Dynamics of Boolean networks controlled by biologically meaningful functions. Journal of Theoretical Biology, 218(3):331341, 2002. https://doi.org/10.1006/jtbi.2002.3081.CrossRefGoogle ScholarPubMed
Harris, Stephen E., Sawhill, Bruce K., Wuensche, Andrew, and Kauffman, Stuart. A model of transcriptional regulatory networks based on biases in the observed regulation rules. Complexity, 7(4):2340, 2002. https://doi.org/10.1002/cplx.10022. https://onlinelibrary.wiley.com/doi/abs/10.1002/cplx.10022.CrossRefGoogle Scholar
Bascompte, Jordi, Jordano, Pedro, Melián, Carlos J., and Olesen, Jens M.. The nested assembly of plant–animal mutualistic networks. Proceedings of the National Academy of Sciences, 100(16):93839387, 2003.CrossRefGoogle Scholar
Aldana, Maximino and Cluzel, Philippe. A natural class of robust networks. Proceedings of the National Academy of Sciences, 100(15):87108714, 2003. https://doi.org/10.1073/pnas.1536783100. www.pnas.org/doi/abs/10.1073/pnas.1536783100.CrossRefGoogle ScholarPubMed
Albert, Réka and Othmer, Hans G.. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. Journal of Theoretical Biology, 223(1): 118, July 2003. https://doi.org/10.1016/S0022-5193(03)00035-3. https://linkinghub.elsevier.com/retrieve/pii/S0022519303000353.CrossRefGoogle ScholarPubMed
Song, Li, Assmann, Sarah M., and Albert, Réka. Predicting essential components of signal transduction networks: A dynamic model of guard cell abscisic acid signaling. PLoS Biology, 4(10): e312, September 2006. https://doi.org/10.1371/journal.pbio.0040312. https://dx.plos.org/10.1371/journal.pbio.0040312.Google Scholar
Salam Jarrah, Abdul, Raposa, Blessilda, and Laubenbacher, Reinhard. Nested canalyzing, unate cascade, and polynomial functions. Physica D: Nonlinear Phenomena, 233(2):167174, 2007. https://doi.org/10.1016/j.physd.2007.06.022. www.sciencedirect.com/science/article/pii/S0167278907002035.CrossRefGoogle Scholar
Zhang, Ranran, Shah, Mithun V., Yang, Jun et al. Network model of survival signaling in large granular lymphocyte leukemia. Proceedings of the National Academy of Sciences, 105(42):1630816313, October 2008. https://doi.org/10.1073/pnas.0806447105. www.pnas.org/cgi/doi/10.1073/pnas.0806447105.CrossRefGoogle ScholarPubMed
Albert, István, Thakar, Juilee, Song, Li, Zhang, Ranran, and Albert, Réka. Boolean network simulations for life scientists. Source Code for Biology and Medicine, 3(1):16, November 2008. https://doi.org/10.1186/1751-0473-3-16.CrossRefGoogle ScholarPubMed
Zhirov, A. O., Zhirov, O. V., and Shepelyansky, D. L.. Two-dimensional ranking of wikipedia articles. The European Physical Journal B, 77(4):523531, October 2010. https://doi.org/10.1140/epjb/e2010-10500-7.CrossRefGoogle Scholar
Shmulevich, Ilya and Edward, R. Dougherty. Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. Society of Industrial and Applied Mathematics, 2010.Google Scholar
Saadatpour, Assieh, Albert, István, and Albert, Réka. Attractor analysis of asynchronous Boolean models of signal transduction networks. Journal of Theoretical Biology, 266(4):641656, 2010. https://doi.org/10.1016/j.jtbi.2010.07.022. www.sciencedirect.com/science/article/pii/S0022519310003796.CrossRefGoogle ScholarPubMed
Pandey, Sona, Wang, Rui-Sheng, Wilson, Liza et al. Boolean modeling of transcriptome data reveals novel modes of heterotrimeric G-protein action. Molecular Systems Biology, 6(1):372, 2010. https://doi.org/10.1038/msb.2010.28. www.embopress.org/doi/abs/10.1038/msb.2010.28.CrossRefGoogle ScholarPubMed
Vilà, Montserrat, Espinar, José L, Hejda, Martin et al. Ecological impacts of invasive alien plants: A meta-analysis of their effects on species, communities and ecosystems. Ecology Letters, 14(7):702708, 2011.CrossRefGoogle ScholarPubMed
Saadatpour, Assieh, Wang, Rui-Sheng, Liao, Aijun et al. Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLOS Computational Biology, 7(11):115, November 2011. https://doi.org/10.1371/journal.pcbi.1002267.CrossRefGoogle Scholar
Naldi, Aurélien, Remy, Elisabeth, Thieffry, Denis, and Chaouiya, Claudine. Dynamically consistent reduction of logical regulatory graphs. Theoretical Computer Science, 412(21):22072218, May 2011. https://doi.org/10.1016/j.tcs.2010.10.021. https://linkinghub.elsevier.com/retrieve/pii/S0304397510005839.CrossRefGoogle Scholar
Campbell, Colin, Yang, Suann, Albert, Réka, and Shea, Katriona. A network model for plant–pollinator community assembly. Proceedings of the National Academy of Sciences, 108(1):197202, 2011. https://doi.org/10.1073/pnas.1008204108. www.pnas.org/content/108/1/197.CrossRefGoogle ScholarPubMed
Campbell, Colin, Yang, Suann, Shea, Katriona, and Albert, Réka. Topology of plant–pollinator networks that are vulnerable to collapse from species extinction. Physical Review E, 86(2):021924, 2012.CrossRefGoogle ScholarPubMed
Allesina, Stefano and Tang, Si. Stability criteria for complex ecosystems. Nature, 483:205208, 2012. https://doi.org/10.1038/nature10832. www.nature.com/articles/nature10832.CrossRefGoogle ScholarPubMed
Gomez Tejeda Zañudo, Jorge and Albert, Réka. An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(2):eabf8124, June 2013. https://doi.org/10.1063/1.4809777. https://aip.scitation.org/doi/abs/10.1063/1.4809777.Google Scholar
Staniczenko, Phillip P. A., Kopp, Jason C., and Allesina, Stefano. The ghost of nestedness in ecological networks. Nature Communications, 4:1391, 2013. https://doi.org/10.1038/ncomms2422. www.nature.com/articles/ncomms2422.CrossRefGoogle ScholarPubMed
Russo, Laura, DeBarros, Nelson, Yang, Suann, Shea, Katriona, and Mortensen, David. Supporting crop pollinators with floral resources: Network-based phenological matching. Ecology and Evolution, 3(9):31253140, 2013. https://doi.org/10.1002/ece3.703. https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.703.CrossRefGoogle ScholarPubMed
Mochizuki, Atsushi, Fiedler, Bernold, Kurosawa, Gen, and Saito, Daisuke. Dynamics and control at feedback vertex sets. II: A faithful monitor to determine the diversity of molecular activities in regulatory networks. Journal of Theoretical Biology, 335:130146, October 2013. https://doi.org/10.1016/j.jtbi.2013.06.009. www.sciencedirect.com/science/article/pii/S0022519313002816.CrossRefGoogle Scholar
LaBar, Thomas, Campbell, Colin, Yang, Suann, Albert, Réka, and Shea, Katriona. Global versus local extinction in a network model of plant–pollinator communities. Theoretical Ecology, 6(4):495503, 2013.CrossRefGoogle Scholar
Fiedler, Bernold, Mochizuki, Atsushi, Kurosawa, Gen, and Saito, Daisuke. Dynamics and control at feedback vertex sets. I: Informative and determining nodes in regulatory networks. Journal of Dynamics and Differential Equations, 25(3):563604, September 2013. https://doi.org/10.1007/s10884-013-9312-7. http://link.springer.com/10.1007/s10884-013-9312-7.CrossRefGoogle Scholar
Nathaniel Steinway, Steven, Zañudo, Jorge G. T., Ding, Wei et al. Network modeling of TGF signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and wnt pathway activation. Cancer Research, 74(21):59635977, November 2014.CrossRefGoogle Scholar
LaBar, Thomas, Campbell, Colin, Yang, Suann, Albert, Réka, and Shea, Katriona. Restoration of plant–pollinator interaction networks via species translocation. Theoretical Ecology, 7(2):209220, 2014.CrossRefGoogle Scholar
Albert, Réka and Thakar, Juilee. Boolean modeling: A logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions. WIREs Systems Biology and Medicine, 6(5):353369, 2014. https://doi.org/10.1002/wsbm.1273. https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wsbm.1273.CrossRefGoogle ScholarPubMed
Jorge, G. T. Zañudo and Albert, Réka. Cell fate reprogramming by control of intracellular network dynamics. PLOS Computational Biology, 11(4):e1004193, April 2015. https://doi.org/10.1371/journal.pcbi.1004193. https://dx.plos.org/10.1371/journal.pcbi.1004193.Google Scholar
Nathaniel Steinway, Steven, Gómez Tejeda Zañudo, Jorge, Michel, Paul J. et al. Combinatorial interventions inhibit TGF -driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes. npj Systems Biology and Applications, 1(1):15014, November 2015. https://doi.org/10.1038/npjsba.2015.14.Google Scholar
Campbell, Colin, Yang, Suann, Albert, Réka, and Shea, Katriona. Plant–pollinator community network response to species invasion depends on both invader and community characteristics. Oikos, 124(4):406413, 2015.CrossRefGoogle Scholar
Mackey, Michael C., Santillán, Moisés, Tyran-Kamińska, Marta, and Zeron, Eduardo S.. Simple Mathematical Models of Gene Regulatory Dynamics. Lecture Notes on Mathematical Modelling in the Life Sciences. Springer International, 2016. https://doi.org/10.1007/978-3-319-45318-7. http://link.springer.com/10.1007/978-3-319-45318-7.Google Scholar
Liu, Yang-Yu and Barabási, Albert-Laszló. Control principles of complex networks. Reviews of Modern Physics, 88(3):035006, September 2016. https://doi.org/10.1103/RevModPhys.88.035006. http://arxiv.org/abs/1508.05384.CrossRefGoogle Scholar
Koorehdavoudi, Hana and Bogdan, Paul. A statistical physics characterization of the complex systems dynamics: Quantifying complexity from spatio-temporal interactions. Scientific Reports, 6(1):27602, June 2016. https://doi.org/10.1038/srep27602. www.nature.com/articles/srep27602.CrossRefGoogle ScholarPubMed
Klarner, Hannes, Streck, Adam, and Siebert, Heike. PyBoolNet: A python package for the generation, analysis and visualization of Boolean networks. Bioinformatics, 33(5):770772, October 2016. https://doi.org/10.1093/bioinformatics/btw682. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw682.CrossRefGoogle Scholar
Kivelson, Sophia and Steven, A. Kivelson. Defining emergence in physics. npj Quantum Materials, 1(1):12, November 2016. https://doi.org/10.1038/npjquantmats.2016.24. www.nature.com/articles/npjquantmats201624.CrossRefGoogle Scholar
Deritei, Dávid, Aird, William C., Ercsey-Ravasz, Mária, and Ravasz Regan, Erzsébet. Principles of dynamical modularity in biological regulatory networks. Scientific Reports, 6(1):21957, April 2016. https://doi.org/10.1038/srep21957. www.nature.com/articles/srep21957.CrossRefGoogle ScholarPubMed
Abou-Jaoudé, Wasim, Traynard, Pauline, Monteiro, Pedro et al. Logical modeling and dynamical analysis of cellular networks. Front Genet, 7:94, 2016. https://doi.org/10.3389/fgene.2016.00094.CrossRefGoogle ScholarPubMed
Gomez Tejeda Zañudo, Jorge, Yang, Gang, and Albert, Réka. Structure-based control of complex networks with nonlinear dynamics. Proceedings of the National Academy of Sciences, 114(28):72347239, July 2017. https://doi.org/10.1073/pnas.1617387114. www.pnas.org/content/114/28/7234.CrossRefGoogle Scholar
Maheshwari, Parul and Albert, Réka. A framework to find the logic backbone of a biological network. BMC Systems Biology, 11(1):122, December 2017.CrossRefGoogle ScholarPubMed
Albert, Réka, Acharya, Biswa R., Wook Jeon, Byeong et al. A new discrete dynamic model of ABA-induced stomatal closure predicts key feedback loops. PLOS Biology, 15(9):135, September 2017. https://doi.org/10.1371/journal.pbio.2003451.CrossRefGoogle ScholarPubMed
Yang, Gang, Gómez Tejeda Zañudo, Jorge, and Albert, Réka. Target control in logical models using the domain of influence of nodes. Frontiers in Physiology, 9:454, 2018. https://doi.org/10.3389/fphys.2018.00454. www.frontiersin.org/articles/10.3389/fphys.2018.00454/full.CrossRefGoogle ScholarPubMed
Santolini, Marc and Barabási, Albert-László. Predicting perturbation patterns from the topology of biological networks. Proceedings of the National Academy of Sciences, 115(27):E6375–E6383, 2018. https://doi.org/10.1073/pnas.1720589115. www.pnas.org/doi/abs/10.1073/pnas.1720589115.CrossRefGoogle ScholarPubMed
Razzaq, Misbah, Paulevé, Loïc, Siegel, Anne et al. Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data. PLOS Computational Biology, 14(10):123, October 2018. https://doi.org/10.1371/journal.pcbi.1006538.CrossRefGoogle ScholarPubMed
Muñoz, Stalin, Carrillo, Miguel, Azpeitia, Eugenio, and Rosenblueth, David A.. Griffin: A tool for symbolic inference of synchronous Boolean molecular networks. Frontiers in Genetics, 9:39, 2018. https://doi.org/10.3389/fgene.2018.00039. www.frontiersin.org/articles/10.3389/fgene.2018.00039.CrossRefGoogle Scholar
de Abril, Ildefons Magrans, Yoshimoto, Junichiro, and Doya, Kenji. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions. Neural Networks, 102:120137, 2018. https://doi.org/10.1016/j.neunet.2018.02.016. www.sciencedirect.com/science/article/pii/S0893608018300704.Google Scholar
Correia, Rion B., Gates, Alexander J., Wang, Xuan, and Rocha, Luis M.. CANA: A python package for quantifying control and canalization in Boolean networks. Frontiers in Physiology, 9:1046, 2018. www.frontiersin.org/articles/10.3389/fphys.2018.01046.CrossRefGoogle Scholar
Rozum, Jordan C. and Albert, Réka. Identifying (un)controllable dynamical behavior in complex networks. PLOS Computational Biology, 14(12):e1006630, December 2018a. https://doi.org/10.1371/journal.pcbi.1006630. https://dx.plos.org/10.1371/journal.pcbi.1006630.CrossRefGoogle ScholarPubMed
Naldi, Aurélien, Hernandez, Céline, Abou-Jaoudé, Wassim et al. Logical modeling and analysis of cellular regulatory networks with GINsim 3.0. Frontiers in Physiology, 9, 2018a. https://doi.org/10.3389/fphys.2018.00646. www.frontiersin.org/articles/10.3389/fphys.2018.00646/full.CrossRefGoogle ScholarPubMed
Rozum, Jordan C. and Albert, Réka. Self-sustaining positive feedback loops in discrete and continuous systems. Journal of Theoretical Biology, 459:3644, December 2018b. https://doi.org/10.1016/j.jtbi.2018.09.017. https://linkinghub.elsevier.com/retrieve/pii/S0022519318304533.CrossRefGoogle ScholarPubMed
Naldi, Aurélien, Hernandez, Céline, Levy, Nicolas et al. The CoLoMoTo interactive notebook: Accessible and reproducible computational analyses for qualitative biological networks. Frontiers in Physiology, 9:680, June 2018b. https://doi.org/10.3389/fphys.2018.00680. www.frontiersin.org/article/10.3389/fphys.2018.00680/full.CrossRefGoogle ScholarPubMed
Wooten, David J., Groves, Sarah M., Tyson, Darren R. et al. Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. PLOS Computational Biology, 15(10):e1007343, October 2019. https://doi.org/10.1371/journal.pcbi.1007343. journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007343.CrossRefGoogle ScholarPubMed
Russo, Laura, Albert, Réka, Campbell, Colin, and Shea, Katriona. Experimental species introduction shapes network interactions in a plant-pollinator community. Biological Invasions, 21:35053519, 2019. https://doi.org/10.1007/s10530-019-02064-z.CrossRefGoogle Scholar
Rozum, Jordan C. and Albert, Réka. Controlling the cell cycle restriction switch across the information gradient. Advances in Complex Systems, 22(07n08):1950020, November 2019. https://doi.org/10.1142/S0219525919500206. www.worldscientific.com/doi/abs/10.1142/S0219525919500206.CrossRefGoogle Scholar
Richard, Adrien. Positive and negative cycles in Boolean networks. Journal of Theoretical Biology, 463:6776, 2019. https://doi.org/10.1016/j.jtbi.2018.11.028. www.sciencedirect.com/science/article/pii/S0022519318305812.CrossRefGoogle ScholarPubMed
Maheshwari, Parul, Hao, Du, Sheen, Jen, Assmann, Sarah M., and Albert, Reka. Model-driven discovery of calcium-related protein-phosphatase inhibition in plant guard cell signaling. PLOS Computational Biology, 15(10):128, October 2019. https://doi.org/10.1371/journal.pcbi.1007429.CrossRefGoogle ScholarPubMed
Loskot, Pavel, Atitey, Komlan, and Mihaylova, Lyudmila. Comprehensive review of models and methods for inferences in bio-chemical reaction networks. Frontiers in Genetics, 10, 2019. https://doi.org/10.3389/fgene.2019.00549. www.frontiersin.org/articles/10.3389/fgene.2019.00549.CrossRefGoogle ScholarPubMed
Deritei, Dávid, Rozum, Jordan C., Ravasz Regan, Erzsébet, and Albert, Réka. A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint. Scientific Reports, 9:16430, 2019. https://doi.org/10.1038/s41598-019-52725-1.CrossRefGoogle ScholarPubMed
Campbell, Colin and Albert, Réka. Edgetic perturbations to eliminate fixed-point attractors in Boolean regulatory networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(2):023130, 2019. https://doi.org/10.1063/1.5083060.CrossRefGoogle ScholarPubMed
Bornholdt, Stefan and Kauffman, Stuart. Ensembles, dynamics, and cell types: Revisiting the statistical mechanics perspective on cellular regulation. Journal of Theoretical Biology, 467:1522, 2019. https://doi.org/10.1016/j.jtbi.2019.01.036. www.sciencedirect.com/science/article/pii/S0022519319300530.CrossRefGoogle ScholarPubMed
Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall, 2019.CrossRefGoogle Scholar
Schwab, Julian D., Külwein, Silke D., Ikonomi, Nensi, Kühl, Michael, and Kestler, Hans A.. Concepts in Boolean network modeling: What do they all mean? Computational and Structural Biotechnology Journal, 18:571582, 2020. https://doi.org/10.1016/j.csbj.2020.03.001. www.sciencedirect.com/science/article/pii/S200103701930460X.CrossRefGoogle ScholarPubMed
Saint-Antoine, MM and Singh, A. Network inference in systems biology: Recent developments, challenges, and applications. Current Opinion in Biotechnology, 63:8998, 2020. https://doi.org/10.1016/j.copbio.2019.12.002.CrossRefGoogle ScholarPubMed
Paulevé, Loïc, Kolčák, Juraj, Chatain, Thomas, and Haar, Stefan. Reconciling qualitative, abstract, and scalable modeling of biological networks. Nature Communications, 11(1):4256, August 2020. https://doi.org/10.1038/s41467-020-18112-5. www.nature.com/articles/s41467-020-18112-5.Google ScholarPubMed
Biella, Paolo, Akter, Asma, Ollerton, Jeff, Nielsen, Anders, and Klecka, Jan. An empirical attack tolerance test alters the structure and species richness of plant–pollinator networks. Functional Ecology, 34(11):22462258, 2020. https://doi.org/10.1111/1365-2435.13642. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2435.13642.CrossRefGoogle Scholar
Zheng, Zhigang. An introduction to emergence dynamics in complex systems. In Liu, Xiang-Yang, editor, Frontiers and Progress of Current Soft Matter Research, Soft and Biological Matter, pages 133196. Springer, 2021. https://doi.org/10.1007/978-981-15-9297-3_4.CrossRefGoogle Scholar
Rozum, Jordan C., Gómez Tejeda Zañudo, Jorge, Gan, Xiao, Deritei, Dávid, and Albert, Réka. Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks. Science Advances, 7(29):eabf8124, July 2021. https://doi.org/10.1126/sciadv.abf8124. https://advances.sciencemag.org/content/7/29/eabf8124.CrossRefGoogle ScholarPubMed
Sadat Fatemi Nasrollahi, Fatemeh, Gómez Tejeda Zañudo, Jorge, Campbell, Colin, and Albert, Réka. Relationships among generalized positive feedback loops determine possible community outcomes in plant–pollinator interaction networks. Physical Review E, 104(5):054304, 2021.Google Scholar
Subbaroyan, Ajay, Martin, Olivier C., and Samal, Areejit. Minimum complexity drives regulatory logic in Boolean models of living systems. PNAS Nexus, 1(1):pgac017, April 2022. https://doi.org/10.1093/pnasnexus/pgac017.CrossRefGoogle ScholarPubMed
Jordan, C. Rozum, Deritei, Dávid, Hyong Park, Kyu, Gómez Tejeda Zañudo, Jorge, and Albert, Réka. pystablemotifs: Python library for attractor identification and control in Boolean networks. Bioinformatics, 38(5):14651466, March 2022. https://doi.org/10.1093/bioinformatics/btab825.Google Scholar
Rozum, Jordan C. and Albert, Réka. Leveraging network structure in nonlinear control. npj Systems Biology and Applications, 8(1):36, 2022. https://doi.org/10.1038/s41540-022-00249-2.CrossRefGoogle ScholarPubMed
Newby, Eli, Gómez Tejeda Zañudo, Jorge, and Albert, Réka. Structure-based approach to identifying small sets of driver nodes in biological networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6):063102, 2022. https://doi.org/10.1063/5.0080843.CrossRefGoogle ScholarPubMed
Naldi, Aurélien, Richard, Adrien, and Tonello, Elisa. Linear cuts in Boolean networks, 2022. https://arxiv.org/abs/2203.01620.Google Scholar
Maheshwari, Parul, Assmann, Sarah M., and Albert, Reka. Inference of a Boolean network from causal logic implications. Frontiers in Genetics, 13: 836856, 2022. https://doi.org/10.3389/fgene.2022.836856. www.frontiersin.org/articles/10.3389/fgene.2022.836856.CrossRefGoogle ScholarPubMed
Abdelmonem Hemedan, Ahmed, Niarakis, Anna, Schneider, Reinhard, and Ostaszewski, Marek. Boolean modelling as a logic-based dynamic approach in systems medicine. Computational and Structural Biotechnology Journal, 20:31613172, 2022. https://doi.org/10.1016/j.csbj.2022.06.035. www.sciencedirect.com/science/article/pii/S2001037022002495.CrossRefGoogle Scholar
Beneš, Nikola, Brim, Luboš, Huvar, Ondřej et al. AEON.py: Python library for attractor analysis in asynchronous Boolean networks. Bioinformatics, 38(21):49784980, November 2022. https://doi.org/10.1093/bioinformatics/btac624.CrossRefGoogle ScholarPubMed
Trinh, Van-Giang, Benhamou, Belaid, Hiraishi, Kunihiko, and Soliman, Sylvain. Minimal trap spaces of logical models are maximal siphons of their petri net encoding. In Petre, Ion and Păun, Andrei, editors, Computational Methods in Systems Biology, volume 13447, pages 158176. Springer International, 2022a. https://doi.org/10.1007/978-3-031-15034-0_8. https://link.springer.com/10.1007/978-3-031-15034-0_8.CrossRefGoogle Scholar
Campbell, Colin, Russo, Laura, Albert, Réka, Buckling, Angus, and Shea, Katriona. Whole community invasions and the integration of novel ecosystems. PLOS Computational Biology, 18(6):e1010151, 2022. https://doi.org/10.1371/journal.pcbi.1010151. https://dx.plos.org/10.1371/journal.pcbi.1010151.CrossRefGoogle ScholarPubMed
Trinh, Van-Giang, Hiraishi, Kunihiko, and Benhamou, Belaid. Computing attractors of large-scale asynchronous Boolean networks using minimal trap spaces. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 110. ACM, August 2022b. https://doi.org/10.1145/3535508.3545520. https://dl.acm.org/doi/10.1145/3535508.3545520.CrossRefGoogle Scholar
Sadat Fatemi Nasrollahi, Fatemeh, Campbell, Colin, and Albert, Réka. Predicting cascading extinctions and efficient restoration strategies in plant–pollinator networks via generalized positive feedback loops. Scientific Reports, 13(1):902, 2023.Google Scholar
Rämö, Pauli, Kauffman, Stuart, Kesseli, Juha, and Yli-Harja, Olli. Measures for information propagation in Boolean networks. Physica D: Non-linear Phenomena, 227(1):100104, March 2007. https://doi.org/10.1016/j.physd.2006.12.005.CrossRefGoogle Scholar
Barros, João. Information Flows in Complex Networks. In Emmert-Streib, Frank and Dehmer, Matthias, editors, Information Theory and Statistical Learning, pages 267287. Springer US, Boston, MA, 2009. https://doi.org/10.1007/978-0-387-84816-7_11.CrossRefGoogle Scholar
Harush, Uzi and Barzel, Baruch. Dynamic patterns of information flow in complex networks. Nature Communications, 8(1):2181, December 2017. https://doi.org/10.1038/s41467-017-01916-3.CrossRefGoogle ScholarPubMed
Hyong Park, Kyu, Xavier Costa, Felipe, Rocha, Luis M., Albert, Réka, and Rozum, Jordan C., Models of Cell Processes are Far from the Edge of Chaos, PRX Life 1(2), 023009, 2023. https://doi.org/10.1103/PRXLife.1.023009.CrossRefGoogle Scholar

Save element to Kindle

To save this element to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Boolean Networks as Predictive Models of Emergent Biological Behaviors
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Boolean Networks as Predictive Models of Emergent Biological Behaviors
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Boolean Networks as Predictive Models of Emergent Biological Behaviors
Available formats
×