Abstract
Synthetic Biology is characterized by a forward engineering approach to the design of biological systems implementing desired functionalities. The Synthetic Biology design cycle benefits from the understanding and the proper representation of the underling biological complexity, allowing predicting the behavior of the target system. Considering the intrinsic nature of the systems to be designed with a Systems Biology perspective is a key requirement to support the Synthetic Biology design cycle. In particular, good models for synthetic biological systems must express hierarchy, encapsulation, selective communication, spatiality, quantitative mechanisms, and stochasticity. Computational models in general not only properly handle such modeling requirements. They can also manage heterogeneous information in compositional processes, support formal analysis and simulation, and can further be exploited for knowledge interchange among the scientific community. In particular, the nets-within-nets formalism expresses all of these features providing high flexibility in the modeling task. The formalism is well suited to represent heterogeneous systems and in general provide an extraordinary expressivity. This is achieved thanks its capability of tuning the abstraction level in each part of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We are not considering here more complex active movement mechanisms or other regulations.
References
Bardini R, Benso A, Di Carlo S, Politano G, Savino A (2016) Using nets-within-nets for modeling differentiating cells in the epigenetic landscape. Lect Notes Comput Sci 9656:315–321
Bardini R, Politano G, Benso A, Di Carlo S (2017a) Multi-level and hybrid modelling approaches for systems biology. Comput Struct Biotechnol J 15:396–402
Bardini R, Politano G, Benso A, Di Carlo S (2017b) Using multi-level Petri nets models to simulate microbiota resistance to antibiotics. IEEE Int Conf Bioinforma Biomed
Benso A, Di Carlo S, Politano G, Savino A, Bucci E (2014) Alice in “Bio-Land”: engineering challenges in the world of life sciences. IT Prof 16:38–47
Bonzanni N, Feenstra KA, Fokkink W, Heringa J (2014) Petri nets are a biologist’s best friend. In: Fages F, Piazza C (eds) Formal methods in macro-biology. FMMB 2014. Lecture notes in computer science, vol 8738. Springer, Cham
Cabac L, Haustermann M, Mosteller D (2016) Renew 2.5–towards a comprehensive integrated development environment for petri net-based applications. In: International conference on applications and theory of Petri nets and concurrency. pp 101–112
Cameron DE, Bashor CJ, Collins JJ (2014) A brief history of synthetic biology. Nat Rev Microbiol 12:381–390
Cull P, Flahive M, Robson R (2005) Difference equations: from rabbits to chaos. Springer, New York
Duchier D, Klutter C (2006) Biomolecular agents as multi-behavioural concurrent objects ENTCS, p 150
Esvelt KM, Whang HH (2013) Genome-scale engineering for systems and synthetic biology. Mol Syst Biol 9:641
Goers L, Freemont P, Polizzi KM (2014) Co-culture systems and technologies: taking synthetic biology to the next level. J R Soc Interface 11
Gorochowski TE (2016) Agent-based modelling in synthetic biology. Essays Biochem 60(4):325–336
Heiner M, Gilbert D (2011) How might petri nets enhance your systems biology toolkit. In: Applications and theory of Petri nets, pp 17–37
Heiner M, Gilbert D, Donaldson R (2008) Petri nets for systems and synthetic biology formal methods for computational. Syst Biol 5016:215–264
Heiner M, Herajy M, Liu F, Rohr C, Schwarick M (2012) Snoopy – a unifying Petri net tool. Lect Notes Comput Sci 7347:398–407
Jensen K (2013) Colored Petri nets: basic concepts, analysis methods and practical use, vol 1. Springer, Heidelberg
Jones DS, Plank M, Sleeman BD (2009) Differential equations and mathematical biology. CRC, London
Maus C, Rybacki S, Uhrmacher AM (2011) Rule-based multi-level modeling of cell biological systems. BMC Syst Biol 5:166
North MJ, Collier NT, Vos JR (2006) Experiences creating three implementations of the repast agent modeling toolkit. ACM Trans Model Comput Simul 16-1:1–25
Purnick PEM, Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol 10:410–422
Qaisar U, Yousaf S, Rehman T, Zainab A, Tayyeb A (2017) Transcriptome analysis and genetic engineering. In: Cirillo P (ed) Applications of RNA-Seq and omics strategies – from microorganisms to human health. InTech. https://doi.org/10.5772/intechopen.69372
Regev A, Silverma W, Shapiro E (2001) Representation and simulation of biochemical processes using the pi-calculus process algebra. Pac Symp Biocomput 6:459–470
Swat M, Thomas GL, Belmonte JM, Shirinifard A, Hmeljak D, Glazier JA (2012) Multi-scale modeling of tissues using CompuCell3D. Comput Methods Cell Biol, Methods Cell Biol 110:325–366
Uhrmacher AM, Degenring D, Zeigler B (2005) Discrete event multi-level models for systems biology. Springer, Berlin, pp 66–89
Valk R (2004) Object Petri nets: using the nets-within-nets paradigm. Lect Notes Comput Sci 3098:819–848
Wang W, Chen Z, Kang B, Li R (2008) Construction of an artificial intercellular communication network using the nitric oxide signaling elements in mammalian cells. Exp Cell Res 314:699–706
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Bardini, R., Politano, G., Benso, A., Di Carlo, S. (2018). Computational Tools for Applying Multi-level Models to Synthetic Biology. In: Singh, S. (eds) Synthetic Biology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8693-9_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-8693-9_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8692-2
Online ISBN: 978-981-10-8693-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)