Abstract
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng Y, Sriram G (2010) Mathematical modeling: bridging the gap between concept and realization in synthetic biology. J Biomed Biotechnol 2010:541609. https://doi.org/10.1155/2010/541609
Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335–338. https://doi.org/10.1038/35002125
Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–342. https://doi.org/10.1038/35002131
Zehnder T, Benner P, Vingron M (2019) Predicting enhancers in mammalian genomes using supervised hidden Markov models. BMC Bioinf 20(1):157. https://doi.org/10.1186/s12859-019-2708-6
Elnitski L, Jin VX, Farnham PJ, Jones SJ (2006) Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques. Genome Res 16(12):1455–1464. https://doi.org/10.1101/gr.4140006
Verbic A, Praznik A, Jerala R (2021) A guide to the design of synthetic gene networks in mammalian cells. FEBS J 288(18):5265–5288. https://doi.org/10.1111/febs.15652
di Bernardo D, Marucci L, Menolascina F, Siciliano V (2012) Predicting synthetic gene networks. Methods Mol Biol 813:57–81. https://doi.org/10.1007/978-1-61779-412-4_4
Mathur M, Xiang JS, Smolke CD (2017) Mammalian synthetic biology for studying the cell. J Cell Biol 216(1):73–82. https://doi.org/10.1083/jcb.201611002
Brandman O, Meyer T (2008) Feedback loops shape cellular signals in space and time. Science 322(5900):390–395. https://doi.org/10.1126/science.1160617
Marucci L (2017) Nanog dynamics in mouse embryonic stem cells: results from systems biology approaches. Stem Cells Int 2017:7160419. https://doi.org/10.1155/2017/7160419
Kramer BP, Fussenegger M (2005) Hysteresis in a synthetic mammalian gene network. Proc Natl Acad Sci U S A 102(27):9517–9522. https://doi.org/10.1073/pnas.0500345102
Chilov D, Fussenegger M (2004) Toward construction of a self-sustained clock-like expression system based on the mammalian circadian clock. Biotechnol Bioeng 87(2):234–242. https://doi.org/10.1002/bit.20143
Tigges M, Denervaud N, Greber D, Stelling J, Fussenegger M (2010) A synthetic low-frequency mammalian oscillator. Nucleic Acids Res 38(8):2702–2711. https://doi.org/10.1093/nar/gkq121
Toettcher JE, Mock C, Batchelor E, Loewer A, Lahav G (2010) A synthetic-natural hybrid oscillator in human cells. Proc Natl Acad Sci U S A 107(39):17047–17052. https://doi.org/10.1073/pnas.1005615107
Santorelli M, Perna D, Isomura A, Garzilli I, Annunziata F, Postiglione L et al (2018) Reconstitution of an ultradian oscillator in mammalian cells by a synthetic biology approach. ACS Synth Biol 7(5):1447–1455. https://doi.org/10.1021/acssynbio.8b00083
Black JB, Perez-Pinera P, Gersbach CA (2017) Mammalian synthetic biology: engineering biological systems. Annu Rev Biomed Eng 19:249–277. https://doi.org/10.1146/annurev-bioeng-071516-044649
Bloom RJ, Winkler SM, Smolke CD (2015) Synthetic feedback control using an RNAi-based gene-regulatory device. J Biol Eng 9:5. https://doi.org/10.1186/s13036-015-0002-3
Townshend B, Kennedy AB, Xiang JS, Smolke CD (2015) High-throughput cellular RNA device engineering. Nat Methods 12(10):989–994. https://doi.org/10.1038/nmeth.3486
Jones TS, Oliveira SMD, Myers CJ, Voigt CA, Densmore D (2022) Genetic circuit design automation with Cello 2.0. Nat Protoc 17(4):1097–1113. https://doi.org/10.1038/s41596-021-00675-2
Chen Y, Zhang S, Young EM, Jones TS, Densmore D, Voigt CA (2020) Genetic circuit design automation for yeast. Nat Microbiol 5(11):1349–1360. https://doi.org/10.1038/s41564-020-0757-2
Nielsen AA, Der BS, Shin J, Vaidyanathan P, Paralanov V, Strychalski EA et al (2016) Genetic circuit design automation. Science 352(6281):aac7341. https://doi.org/10.1126/science.aac7341
Muldoon JJ, Kandula V, Hong M, Donahue PS, Boucher JD, Bagheri N et al (2021) Model-guided design of mammalian genetic programs. Sci Adv 7(8). https://doi.org/10.1126/sciadv.abe9375
Donahue PS, Draut JW, Muldoon JJ, Edelstein HI, Bagheri N, Leonard JN (2020) The COMET toolkit for composing customizable genetic programs in mammalian cells. Nat Commun 11(1):779. https://doi.org/10.1038/s41467-019-14147-5
Frei T, Cella F, Tedeschi F, Gutierrez J, Stan GB, Khammash M et al (2020) Characterization and mitigation of gene expression burden in mammalian cells. Nat Commun 11(1):4641. https://doi.org/10.1038/s41467-020-18392-x
Cella F, Perrino G, Tedeschi F, Viero G, Bosia C, Stan GB et al (2023) MIRELLA: a mathematical model explains the effect of microRNA-mediated synthetic genes regulation on intracellular resource allocation. Nucleic Acids Res 51(7):3452–3464. https://doi.org/10.1093/nar/gkad151
Jones RD, Qian Y, Siciliano V, DiAndreth B, Huh J, Weiss R et al (2020) An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nat Commun 11(1):5690. https://doi.org/10.1038/s41467-020-19126-9
Santorelli M, Lam C, Morsut L (2019) Synthetic development: building mammalian multicellular structures with artificial genetic programs. Curr Opin Biotechnol 59:130–140. https://doi.org/10.1016/j.copbio.2019.03.016
Aydin O, Passaro AP, Raman R, Spellicy SE, Weinberg RP, Kamm RD et al (2022) Principles for the design of multicellular engineered living systems. APL Bioeng 6(1):010903. https://doi.org/10.1063/5.0076635
Wieland M, Fussenegger M (2012) Engineering molecular circuits using synthetic biology in mammalian cells. Annu Rev Chem Biomol Eng 3:209–234. https://doi.org/10.1146/annurev-chembioeng-061010-114145
Matsuda M, Koga M, Woltjen K, Nishida E, Ebisuya M (2015) Synthetic lateral inhibition governs cell-type bifurcation with robust ratios. Nat Commun 6:6195. https://doi.org/10.1038/ncomms7195
Matsuda M, Koga M, Nishida E, Ebisuya M (2012) Synthetic signal propagation through direct cell-cell interaction. Sci Signal 5(220):ra31. https://doi.org/10.1126/scisignal.2002764
Morsut L, Roybal KT, Xiong X, Gordley RM, Coyle SM, Thomson M et al (2016) Engineering customized cell sensing and response behaviors using synthetic notch receptors. Cell 164(4):780–791. https://doi.org/10.1016/j.cell.2016.01.012
Li P, Markson JS, Wang S, Chen S, Vachharajani V, Elowitz MB (2018) Morphogen gradient reconstitution reveals Hedgehog pathway design principles. Science 360(6388):543–548. https://doi.org/10.1126/science.aao0645
Sprinzak D, Lakhanpal A, LeBon L, Garcia-Ojalvo J, Elowitz MB (2011) Mutual inactivation of Notch receptors and ligands facilitates developmental patterning. PLoS Comput Biol 7(6):e1002069. https://doi.org/10.1371/journal.pcbi.1002069
Shaya O, Binshtok U, Hersch M, Rivkin D, Weinreb S, Amir-Zilberstein L et al (2017) Cell-cell contact area affects notch signaling and Notch-dependent patterning. Dev Cell 40(5):505–11 e6. https://doi.org/10.1016/j.devcel.2017.02.009
Montes-Olivas S, Marucci L, Homer M (2019) Mathematical models of organoid cultures. Front Genet 10:873. https://doi.org/10.3389/fgene.2019.00873
Gorochowski TE, Hauert S, Kreft JU, Marucci L, Stillman NR, Tang TD et al (2020) Toward engineering biosystems with emergent collective functions. Front Bioeng Biotechnol 8:705. https://doi.org/10.3389/fbioe.2020.00705
Montes-Olivas S, Legge D, Lund A et al (2023) In-silico and in-vitro morphometric analysis of intestinal organoids. PLoS Comput Biol 19(8):e1011386. https://doi.org/10.1371/journal.pcbi.1011386
Monk JM, Lloyd CJ, Brunk E, Mih N, Sastry A, King Z et al (2017) iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 35(10):904–908. https://doi.org/10.1038/nbt.3956
Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C (2021) Genome-scale metabolic modeling enables in-depth understanding of big data. Meta 12(1). https://doi.org/10.3390/metabo12010014
Fang X, Lloyd CJ, Palsson BO (2020) Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 18(12):731–743. https://doi.org/10.1038/s41579-020-00440-4
Gu C, Kim GB, Kim WJ, Kim HU, Lee SY (2019) Current status and applications of genome-scale metabolic models. Genome Biol 20(1):121. https://doi.org/10.1186/s13059-019-1730-3
Chen K, Gao Y, Mih N, O'Brien EJ, Yang L, Palsson BO (2017) Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc Natl Acad Sci U S A 114(43):11548–11553. https://doi.org/10.1073/pnas.1705524114
Lloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E, O’Brien EJ et al (2018) COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Comput Biol 14(7):e1006302. https://doi.org/10.1371/journal.pcbi.1006302
Landon S, Rees-Garbutt J, Marucci L, Grierson C (2019) Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering. Essays Biochem 63(2):267–284. https://doi.org/10.1042/EBC20180045
Lv X, Hueso-Gil A, Bi X, Wu Y, Liu Y, Liu L et al (2022) New synthetic biology tools for metabolic control. Curr Opin Biotechnol 76:102724. https://doi.org/10.1016/j.copbio.2022.102724
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE et al (2021) Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A 118(30). https://doi.org/10.1073/pnas.2102344118
Robinson JL, Kocabas P, Wang H, Cholley PE, Cook D, Nilsson A et al (2020) An atlas of human metabolism. Sci Signal 13(624). https://doi.org/10.1126/scisignal.aaz1482
Kachhawaha K, Singh S, Joshi K, Nain P, Singh SK (2022) Bioprocessing of recombinant proteins from Escherichia coli inclusion bodies: insights from structure-function relationship for novel applications. Prep Biochem Biotechnol 53:1–25. https://doi.org/10.1080/10826068.2022.2155835
Strain B, Morrissey J, Antonakoudis A, Kontoravdi C (2023) Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems. Comput Struct Biotechnol J 21:1543–1549. https://doi.org/10.1016/j.csbj.2023.02.011
Karr JR, Phillips NC, Covert MW (2014) WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database (Oxford) 2014. https://doi.org/10.1093/database/bau095
Karr JR, Sanghvi JC, Macklin DN, Arora A, Covert MW (2013) WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res 41(Database issue):D787–D792. https://doi.org/10.1093/nar/gks1108
Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr et al (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150(2):389–401. https://doi.org/10.1016/j.cell.2012.05.044
Ahn-Horst TA, Mille LS, Sun G, Morrison JH, Covert MW (2022) An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control. NPJ Syst Biol Appl 8(1):30. https://doi.org/10.1038/s41540-022-00242-9
Carrera J, Covert MW (2015) Why build whole-cell models? Trends Cell Biol 25(12):719–722. https://doi.org/10.1016/j.tcb.2015.09.004
Macklin DN, Ahn-Horst TA, Choi H, Ruggero NA, Carrera J, Mason JC et al (2020) Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 369(6502):eaav3751. https://doi.org/10.1126/science.aav3751
Rees-Garbutt J, Chalkley O, Landon S, Purcell O, Marucci L, Grierson C (2020) Designing minimal genomes using whole-cell models. Nat Commun 11(1):836. https://doi.org/10.1038/s41467-020-14545-0
Rees-Garbutt J, Rightmyer J, Chalkley O, Marucci L, Grierson C (2021) Testing theoretical minimal genomes using whole-cell models. ACS Synth Biol 10(7):1598–1604. https://doi.org/10.1021/acssynbio.0c00515
Marucci L, Barberis M, Karr J, Ray O, Race PR, de Souza AM et al (2020) Computer-aided whole-cell design: taking a holistic approach by integrating synthetic with systems biology. Front Bioeng Biotechnol 8:942. https://doi.org/10.3389/fbioe.2020.00942
Landon S, Chalkley O, Breese G, Grierson C, Marucci L (2021) Understanding metabolic flux behaviour in whole-cell model output. Front Mol Biosci 8:732079. https://doi.org/10.3389/fmolb.2021.732079
Szigeti B, Roth YD, Sekar JAP, Goldberg AP, Pochiraju SC, Karr JR (2018) A blueprint for human whole-cell modeling. Curr Opin Syst Biol 7:8–15. https://doi.org/10.1016/j.coisb.2017.10.005
Elsemman IE, Rodriguez Prado A, Grigaitis P, Garcia Albornoz M, Harman V, Holman SW et al (2022) Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies. Nat Commun 13(1):801. https://doi.org/10.1038/s41467-022-28467-6
Wilkinson MD, Dumontier M, Jan Aalbersberg I, Appleton G, Axton M, Baak A et al (2019) Addendum: the FAIR guiding principles for scientific data management and stewardship. Sci Data. 6(1):6. https://doi.org/10.1038/s41597-019-0009-6
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18
Goldberg AP, Szigeti B, Chew YH, Sekar JA, Roth YD, Karr JR (2018) Emerging whole-cell modeling principles and methods. Curr Opin Biotechnol 51:97–102. https://doi.org/10.1016/j.copbio.2017.12.013
Gherman IM, Abdallah ZS, Pang W, Gorochowski TE, Grierson CS, Marucci L (2023) Bridging the gap between mechanistic biological models and machine learning surrogates. PLoS Comput Biol 19(4):e1010988. https://doi.org/10.1371/journal.pcbi.1010988
Gherman IM, Rees-Garbutt J, Pang W et al (2023) Accelerated design of Escherichia coli genomes with reduced size using a whole-cell model and machine learning surrogate. bioRxiv 2023.10.30.564402. https://doi.org/10.1101/2023.10.30.564402
Thornburg ZR, Bianchi DM, Brier TA, Gilbert BR, Earnest TM, Melo MCR et al (2022) Fundamental behaviors emerge from simulations of a living minimal cell. Cell 185(2):345–60 e28. https://doi.org/10.1016/j.cell.2021.12.025
Scholzel C, Blesius V, Ernst G, Dominik A (2021) Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. NPJ Syst Biol Appl. 7(1):27. https://doi.org/10.1038/s41540-021-00182-w
Del Vecchio D, Dy AJ, Qian Y (2016) Control theory meets synthetic biology. J R Soc Interface 13(120):20160380. https://doi.org/10.1098/rsif.2016.0380
Filo M, Chang CH, Khammash M (2023) Biomolecular feedback controllers: from theory to applications. Curr Opin Biotechnol 79:102882. https://doi.org/10.1016/j.copbio.2022.102882
Briat C, Gupta A, Khammash M (2016) Antithetic integral feedback ensures robust perfect adaptation in noisy biomolecular networks. Cell Syst 2(2):133. https://doi.org/10.1016/j.cels.2016.02.010
Anastassov S, Filo M, Chang CH, Khammash M (2023) A cybergenetic framework for engineering intein-mediated integral feedback control systems. Nat Commun 14(1):1337. https://doi.org/10.1038/s41467-023-36863-9
Filo M, Kumar S, Khammash M (2022) A hierarchy of biomolecular proportional-integral-derivative feedback controllers for robust perfect adaptation and dynamic performance. Nat Commun 13(1):2119. https://doi.org/10.1038/s41467-022-29640-7
Annunziata F, Matyjaszkiewicz A, Fiore G, Grierson CS, Marucci L, di Bernardo M et al (2017) An orthogonal multi-input integration system to control gene expression in Escherichia coli. ACS Synth Biol 6(10):1816–1824. https://doi.org/10.1021/acssynbio.7b00109
Fiore G, Matyjaszkiewicz A, Annunziata F, Grierson C, Savery NJ, Marucci L et al (2017) In-silico analysis and implementation of a multicellular feedback control strategy in a synthetic bacterial consortium. ACS Synth Biol 6(3):507–517. https://doi.org/10.1021/acssynbio.6b00220
McCarty NS, Ledesma-Amaro R (2019) Synthetic biology tools to engineer microbial communities for biotechnology. Trends Biotechnol 37(2):181–197. https://doi.org/10.1016/j.tibtech.2018.11.002
Menolascina F, Fiore G, Orabona E, De Stefano L, Ferry M, Hasty J et al (2014) In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 10(5):e1003625. https://doi.org/10.1371/journal.pcbi.1003625
Lugagne JB, Sosa Carrillo S, Kirch M, Kohler A, Batt G, Hersen P (2017) Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nat Commun 8(1):1671. https://doi.org/10.1038/s41467-017-01498-0
Shannon B, Zamora-Chimal CG, Postiglione L, Salzano D, Grierson CS, Marucci L et al (2020) In vivo feedback control of an antithetic molecular-titration motif in Escherichia coli using microfluidics. ACS Synth Biol 9(10):2617–2624. https://doi.org/10.1021/acssynbio.0c00105
Pedone E, de Cesare I, Zamora-Chimal CG, Haener D, Postiglione L, La Regina A et al (2021) Cheetah: a computational toolkit for cybergenetic control. ACS Synth Biol 10(5):979–989. https://doi.org/10.1021/acssynbio.0c00463
Postiglione L, Napolitano S, Pedone E, Rocca DL, Aulicino F, Santorelli M et al (2018) Regulation of gene expression and signaling pathway activity in mammalian cells by automated microfluidics feedback control. ACS Synth Biol 7(11):2558–2565. https://doi.org/10.1021/acssynbio.8b00235
Khazim M, Pedone E, Postiglione L, di Bernardo D, Marucci L (2021) A microfluidic/microscopy-based platform for on-chip controlled gene expression in mammalian cells. Methods Mol Biol 2229:205–219. https://doi.org/10.1007/978-1-0716-1032-9_10
de Cesare I, Zamora-Chimal CG, Postiglione L, Khazim M, Pedone E, Shannon B et al (2021) ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices. ACS Omega 6(4):2473–2476. https://doi.org/10.1021/acsomega.0c03906
de Cesare I, Salzano D, di Bernardo M, Renson L, Marucci L (2022) Control-based continuation: a new approach to prototype synthetic gene networks. ACS Synth Biol 11(7):2300–2313. https://doi.org/10.1021/acssynbio.1c00632
Smart B, De Cesare I, Renson L, Marucci L (2022) Model predictive control of cancer cellular dynamics: a new strategy for therapy design. Front Control Eng 3(2022). https://doi.org/10.3389/fcteg.2022.935018
Acknowledgments
LM was funded by the Engineering and Physical Sciences Research Council (EPSRC, EP/S01876X/1) and the Biotechnology and Biological Sciences Research Council (BBSRC, Bristol Centre for Engineering Biology, BB/W013959/1; Breakthrough Award BB/W012235/1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Chew, Y.H., Marucci, L. (2024). Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology. In: Ceroni, F., Polizzi, K. (eds) Mammalian Synthetic Systems. Methods in Molecular Biology, vol 2774. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3718-0_6
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3718-0_6
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3717-3
Online ISBN: 978-1-0716-3718-0
eBook Packages: Springer Protocols