Abstract
Stem cells are unique in their ability to differentiate into diverse phenotypes capable of displaying radically different, yet stable, gene expression profiles. Understanding this multistable behavior is key to rationally influencing stem cell differentiation for both research and therapeutic purposes. To this end, mathematical paradigms have been adopted to simulate and explain the dynamics of complex gene networks. In this chapter, we introduce strategies for building deterministic and stochastic mathematical models of gene expression and demonstrate how analysis of these models can benefit our understanding of complex observed behaviors. Developing a mathematical understanding of biological processes is of utmost importance in understanding and controlling stem cell behavior.
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References
Menn DJ, Wang X (2001) Stochastic and deterministic decision in cell fate. eLS. https://doi.org/10.1002/9780470015902.a0025319
Menn DJ, Su R-Q, Wang X (2017) Control of synthetic gene networks and its applications. Quant Biol 5:124–135. https://doi.org/10.1007/s40484-017-0106-5
Wang L-Z, Wu F, Flores K et al (2016) Build to understand: synthetic approaches to biology. Integr Biol 8:394–408. https://doi.org/10.1039/C5IB00252D
Ellis T, Wang X, Collins JJ (2009) Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol 27:465–471. https://doi.org/10.1038/nbt.1536
Faucon PC, Pardee K, Kumar RM et al (2014) Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions. PLoS One 9:e102873. https://doi.org/10.1371/journal.pone.0102873
Ma W, Trusina A, El-Samad H et al (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138:760–773. https://doi.org/10.1016/j.cell.2009.06.013
Wu M, Su R-Q, Li X et al (2013) Engineering of regulated stochastic cell fate determination. Proc Natl Acad Sci 110:10610–10615. https://doi.org/10.1073/pnas.1305423110
Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403:339–342
Wu F, Su R-Q, Lai Y-C, Wang X (2017) Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination. eLife 6:e23702. https://doi.org/10.7554/eLife.23702
Johnson KA, Goody RS (2011) The original michaelis constant: translation of the 1913 Michaelis–Menten paper. Biochemistry (Mosc) 50:8264–8269. https://doi.org/10.1021/bi201284u
Weiss JN (1997) The hill equation revisited: uses and misuses. FASEB J 11:835–841. https://doi.org/10.1096/fasebj.11.11.9285481
Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403:335–338. https://doi.org/10.1038/35002125
Li H, Cao Y, Petzold LR, Gillespie DT (2008) Algorithms and software for stochastic simulation of biochemical reacting systems. Biotechnol Prog 24:56–61. https://doi.org/10.1021/bp070255h
Adalsteinsson D, McMillen D, Elston TC (2004) Biochemical network stochastic simulator (BioNetS): software for stochastic modeling of biochemical networks. BMC Bioinformatics 5:24. https://doi.org/10.1186/1471-2105-5-24
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Menn, D., Wang, X. (2019). Modeling Gene Networks to Understand Multistability in Stem Cells. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_8
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DOI: https://doi.org/10.1007/978-1-4939-9224-9_8
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