Automatic Parameterization of the Purine Metabolism Pathway through Discrete Event-based Simulation
Authors: 
Simone Caligola, Tommaso Carlucci, Franco Fummi, Carlo Laudanna, Gabriela Constantin, Nicola Bombieri, Rosalba GiugnoAuthors Info & Claims
Simone Caligola, Tommaso Carlucci, Franco Fummi, Carlo Laudanna, Gabriela Constantin, Nicola Bombieri, Rosalba GiugnoAuthors Info & ClaimsPages 1 - 4
Abstract
Stochastic Petri Nets (SPN)are recognized as one of the standard formalisms to model metabolic networks. They allow incorporating randomness in the model and taking into account possible fluctuations and noise due to molecule interactions in the environment. Even though some frameworks have been proposed to implement and simulate SPN (e.g., Snoopy, Monalisa), they do not allow for automatic model parameterization, which is a crucial task to identify the network configurations that lead the model to satisfy certain biological properties. We present a framework to synthesize the SPN model of a metabolic network into executable code that can be simulated through a discrete event-based simulator. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, through Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to model the purine metabolism and to reproduce the metabolomics data obtained from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders. We show system parameterization extrapolated by the framework to reproduce the experimental results and to simulate the model under different conditions.
References
[1]
M. Heiner and I. Koch, “Petri net based model validation in systems biology,” in ICATPN, vol. 3099. Springer, 2004, pp. 216–237.
[2]
A. Sackmann, D. Formanowicz, P. Formanowicz, I. Koch, and J. Blazewicz, “An analysis of the Petri net based model of the human body iron homeostasis process,” Computational Biology and Chemistry, vol. 31, no. 1, pp. 1–10, 2007.
[3]
S. K. Hahl and A. Kremling, “A comparison of deterministic and stochastic modeling approaches for biochemical reaction systems: On fixed points, means, and modes,” Frontiers in genetics, vol. 7, p. 157, 2016.
[4]
L. Albergante, J. Timmis, L. Beattie, and P. M. Kaye, “A petri net model of granulomatous inflammation: implications for i1–10 mediated control of leishmania donovani infection,” PLoS computational biology, vol. 9, no. 11, p. e1003334, 2013.
[5]
L. Napione et al., “On the use of stochastic petri nets in the analysis of signal transduction pathways for angiogenesis process,” in International Conference on Computational Methods in Systems Biology. Springer, 2009, pp. 281–295.
[6]
M. Heiner, M. Herajy, F. Liu, C. Rohr, and M. Schwarick, “Snoopy-a unifying petri net tool,” in Int. Conf. on Application and Theory of Petri Nets and Concurrency. Springer, 2012, pp. 398–407.
[7]
P. Balazki, K. Lindauer, J. Einloft, J. Ackermann, and I. Koch, “Monalisa for stochastic simulations of petri net models of biochemical systems,” BMC bioinformatics, vol. 16, no. 1, p. 215, 2015.
[8]
J. Fisher and T. A. Henzinger, “Executable cell biology,” Nature Biotechnology, vol. 25, pp. 1239–1249, 2007.
[9]
N. Bombieri, R. Distefano, G. Scardoni, F. Fummi, C. Laudanna, and R. Giugno, “Dynamic modeling and simulation of leukocyte integrin activation through an electronic design automation framework,” in International Conference on Computational Methods in Systems Biology. Springer, 2014, pp. 143–154.
[10]
D. T. Gillespie, “Exact stochastic simulation of coupled chemical reactions,” The journal of physical chemistry, vol. 81, no. 25, pp. 2340–2361, 1977.
[11]
–, “A general method for numerically simulating the stochastic time evolution of coupled chemical reactions,” Journal of computational physics, vol. 22, no. 4, pp. 403–434, 1976.
[12]
C. Dittamo and D. Cangelosi, “Optimized parallel implementation of gillespie's first reaction method on graphics processing units,” in Computer Modeling and Simulation, 2009. ICCMS'09. International Conference on. IEEE, 2009, pp. 156–161.
[13]
K.-T. Cheng and A. Krishnakumar, “Automatic generation of functional vectors using the extended finite state machine model,” ACM TODAES, vol. 1, no. 1, pp. 57–79, 1996.
[14]
Accellera Systems Initiative, “IEEE 1666–2011: The standard SystemC language,” http://www.systemc.org.
[15]
C. N. Coelho Jr. and H. D. Foster, Assertion-based Verification. A: Springer, 2008, vol. 4.
[16]
N. Bombieri et al., “On the evaluation of transactor-based verification for reusing TLM assertions and testbenches at RTL,” in Proc. of ACM/IEEE DATE, vol. 1, 2006, pp. 1–6.
[17]
M. Boulé and Z. Zilic, Generating hardware assertion checkers: for hardware verification, emulation, post-fabrication debugging and online monitoring. A: Springer, 2008.
[18]
IEEE, “Property specification language - psl,” 2017, https://standards.ieee.org/findstds/standard/1850-2010.html.
[19]
Y. Abarbanel et al., “Focs-automatic generation of simulation checkers from formal specifications,” in Computer Aided Verification. Springer, 2000, pp. 538–542.
[20]
L. Piccio, B. Rossi, E. Scarpini, C. Laudanna, C. Giagulli, A. C. Issekutz, D. Vestweber, E. C. Butcher, and G. Constantin, “Molecular mechanisms involved in lymphocyte recruitment in inflamed brain microvessels: critical roles for p-selectin glycoprotein ligand-l and heterotrimeric gi-linked receptors,” The Journal of Immunology, vol. 168, no. 4, pp. 1940–1949, 2002.
[21]
T. Eleftheriadis, G. Pissas, A. Karioti, G. Antoniadi, S. Golfinopoulos, V. Liakopoulos, A. Mamara, M. Speletas, G. Koukoulis, and I. Ste-Fanidis, “Uric acid induces caspase-l activation, il-1β secretion and p2⨯7 receptor dependent proliferation in primary human lymphocytes,” Hippokratia, vol. 17, no. 2, p. 141, 2013.
Index Terms
- Automatic Parameterization of the Purine Metabolism Pathway through Discrete Event-based Simulation
Index terms have been assigned to the content through auto-classification.
Recommendations
NFB pathway analysis
Display Omitted An analysis based on cycles among genes of gene co-expression networks is proposed.Cycles are associated with feedback mechanisms, very common in biological networks.NFB pathway analysis in tumor specimens of GBM compared to normal brain ...
Identifying epigenetically dysregulated pathways from pathway-pathway interaction networks
Background: Identification of pathways that show significant difference in activity between disease and control samples have been an interesting topic of research for over a decade. Pathways so identified serve as potential indicators of aberrations in ...
Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data
Summary: We have developed a web-based system (Pathway Miner) for visualizing gene expression profiles in the context of biological pathways. Pathway Miner catalogs genes based on their role in metabolic, cellular and regulatory pathways. A Fisher ...
Comments
Information & Contributors
Information
Published In
Jul 2019
364 pages
Copyright © 2019.
Publisher
IEEE Press
Publication History
Published: 09 July 2019
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in