research-article

Systems biology

Author:

Jean KrivineAuthors Info & Claims

ACM SIGLOG News, Volume 4, Issue 3

Pages 43 - 61

https://doi.org/10.1145/3129173.3129182

Published: 28 July 2017 Publication History

Abstract

The unequal race between technological and theoretical innovation in Computer Science has a mirror image in Systems Biology where the balance is even more biased toward the experimental side. The aim of Systems Biology is to produce a specification of natural systems, when artificial systems are usually already equipped with one¹. As a consequence, Systems Biology is almost exclusively an experimental science where most of the innovation effort is geared toward the conception of devices able to extract more facts from biological systems. It has resulted in a modern trend of Systems Biology where high-throughput experiments are now producing big data and a massive inflation of publication volume (Figure 1). The lack of comprehensive integration of biological knowledge is hidden by the success stories of synthetic design using bio material: the initial aim of understanding how the basic components of the cell conspire in a complex and changing environment is modestly replaced by the science of hijacking cellular and genetic components in order to implement intelligently designed tasks. In turn these bio-devices enable biologists to set up more experiments and accumulate more data. This life cycle between technological innovation and data accumulation is encouraged by high impact journals in biology, which tend to give more credit to technological breakthroughs than (disputable) progress in understanding the ways of the cell.

References

[1]

Andrei and H. Kirchner. 2007. Graph Rewriting and Strategies for Modeling Biochemical Networks. In Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007). 407--414.

Digital Library

[2]

Mehmet Alper Arslan, Ozgur Kutuk, and Huveyda Basaga. 2006. Protein Kinases as Drug Targets in Cancer. Current Cancer Drug Targets 6, 7 (2006), 623 -- 634.

[3]

Adrien Basso-Blandin, Walter Fontana, and Russ Harmer. 2016. A knowledge representation meta-model for rule-based modelling of signalling networks. In Proceedings of the Eleventh International Workshop on Developments in Computational Models, Cali, Colombia, October 28, 2015 (Electronic Proceedings in Theoretical Computer Science), César A. Muñoz and Jorge A. Pérez (Eds.), Vol. 204. Open Publishing Association, 47--59.

[4]

Pierre Boutillier, Thomas Ehrhard, and Jean Krivine. 2017a. Incremental Update for Graph Rewriting. Springer Berlin Heidelberg, Berlin, Heidelberg, 201--228.

Digital Library

[5]

Pierre Boutillier, Jérôme Feret, Jean Krivine, and Kim Quyên Lý. 2017b. KaSim user manual - http://dev.executableknowledge.org/docs/KaSim-manual-master/KaSim_manual.htm. (2017).

[6]

Nathalie Chabrier and François Fages. 2003. Symbolic model checking of biochemical networks. In Proc. CMSB'03 (LNCS), Vol. 2602. 146--162.

Digital Library

[7]

Saikat Chowdhury and Ram Rup Sarkar. 2015. Comparison of human cell signaling pathway databases---evolution, drawbacks and challenges. Database: The Journal of Biological Databases and Curation 2015 (2015), bau126.

[8]

F. Ciocchetta and J. Hillston. 2009. Bio-PEPA: a Framework for the Modelling and Analysis of Biochemical Networks. TCS 410, 33--34 (2009), 3056--84.

Digital Library

[9]

Jean-Paul Comet, Mathilde Noual, Adrien Richard, Julio Aracena, Laurence Calzone, Jacques Demongeot, Marcelle Kaufman, Aurélien Naldi, El Houssine Snoussi, and Denis Thieffry. 2013. On Circuit Functionality in Boolean Networks. Bulletin of Mathematical Biology 75, 6 (2013), 906--919.

[10]

Troels C. Damgaard, Espen Højsgaard, and Jean Krivine. 2012. Formal Cellular Machinery. Electronic Notes in Theoretical Computer Science 284 (2012), 55 -- 74. Proceedings of the 2nd International Workshop on Static Analysis and Systems Biology (SASB 2011).

Digital Library

[11]

Vincent Danos, Jérôme Feret, Walter Fontana, Russell Harmer, Jonathan Hayman, Jean Krivine, Christopher D. Thompson-Walsh, and Glynn Winskel. 2012. Graphs, Rewriting and Pathway Reconstruction for Rule-Based Models. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2012, December 15--17, 2012, Hyderabad, India. 276--288.

[12]

Vincent Danos, Jérome Féret, Walter Fontana, and Jean Krivine. 2007. Scalable simulation of cellular signaling networks. In Proc. APLAS'07 (LNCS), Vol. 4807. 139--157.

Digital Library

[13]

Vincent Danos, Jérôme Feret, Walter Fontanta, Russ Harmer, and Jean Krivine. 2007. Rule based modeling of biological signaling. In Proceedings of CONCUR 2007 (LNCS), Luís Caires and Vasco Thudichum Vasconcelos (Eds.), Vol. 4703. Springer, 17--41.

Digital Library

[14]

Vincent Danos, Russ Harmer, and Glynn Winskel. 2013. Constraining rule-based dynamics with types. Mathematical Structures in Computer Science 23, 2 (2013), 272--289.

[15]

Vincent Danos and Jean Krivine. 2003. Formal molecular biology done in CCS-R. In Bio-Concur'03, satellite workshop of CONCUR'03 (ENTCS), Vol. 180. 31--49.

Digital Library

[16]

Vincent Danos and Cosimo Laneve. 2003. Graphs for Formal Molecular Biology. In Proc. CMSB'03 (LNCS), Vol. 2602. 34--46.

Digital Library

[17]

James R. Faeder, Mickael L. Blinov, and William S. Hlavacek. 2004. Rule Based modeling of biochemical networks. Complexity 10, 4 (2004), 22--41.

Digital Library

[18]

Jérôme Feret. 2007. Reachability Analysis of Biological Signalling Pathways by Abstract Interpretation. In Proceedings of the International Conference of Computational Methods in Sciences and Engineering, ICCMSE '2007, Corfu, Greece (American Institute of Physics Conference Proceedings). American Institute of Physics, Corfu, Greece, 619--622.

[19]

Jérôme Feret and Kim Quyên Lý. 2016. Reachability analysis via orthogonal sets of patterns. In Seventeenth International Workshop on Static Analysis and Systems Biology (SASB'16) (ENTCS). elsevier. to appear.

[20]

Jasmin Fisher and Thomas A Henzinger. 2007. Executable cell biology. Nature Biotech 25, 11 (2007), 1239--1249.

[21]

Daniel T. Gillespie. 1976. A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions. J. Comput. Phys. 22, 4 (1976), 403--434.

[22]

William S Hlavacek, James R Faeder, Michael L Blinov, Richard G Posner, Michael Hucka, and Walter Fontana. 2006. Rules for modeling signal-transduction systems. Science Signaling 2006, 344 (2006), re6.

[23]

Aileen Houston and Joe O'Connell. 2004. The Fas signalling pathway and its role in the pathogenesis of cancer. Current Opinion in Pharmacology 4, 4 (2004), 321--326.

[24]

Bernardo A. Huberman and Lada A. Adamic. 1999. Internet: Growth dynamics of the World-Wide Web. Nature 401, 6749 (09 09 1999), 131--131.

[25]

Mathias John, Cédric Lhoussaine, Joachim Niehren, and Cristian Versari. 2011. Biochemical Reaction Rules with Constraints. In Proc. ESOP 2011 (LNCS), Vol. 6602. 338--357.

Digital Library

[26]

Stuart Kauffman, Carsten Peterson, Björn Samuelsson, and Carl Troein. 2003. Random Boolean network models and the yeast transcriptional network. Proceedings of the National Academy of Sciences 100, 25 (2003), 14796--14799.

[27]

Jean Krivine, Robin Milner, and Angelo Troina. 2008. Stochastic Bigraphs. Electronic Notes in Theoretical Computer Science 218 (2008), 73 -- 96. Proceedings of the 24th Conference on the Mathematical Foundations of Programming Semantics (MFPS XXIV).

Digital Library

[28]

Carlos F Lopez, Jeremy L Muhlich, John A Bachman, and Peter K Sorger. 2013. Programming biological models in Python using PySB. Molecular Systems Biology 9, 1 (2013).

[29]

Kanae Oda, Yukiko Matsuoka, Akira Funahashi, and Hiroaki Kitano. 2005. A comprehensive pathway map of epidermal growth factor receptor signaling. Molecular Systems Biology 1 (2005), 2005.0010--2005.0010.

[30]

Loïc Paulevé and Adrien Richard. 2012. Static Analysis of Boolean Networks Based on Interaction Graphs: A Survey. ENTCS 284 (2012), 93 -- 104. Proceedings of the 2nd International Workshop on Static Analysis and Systems Biology (SASB 2011).

[31]

Michael Pedersen and Gordon Plotkin. 2008. A Language for Biochemical Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 63--82.

Digital Library

[32]

Andrew Phillips and Luca Cardelli. 2007. Efficient, Correct Simulation of Biological Processes in the Stochastic pi-calculus. In CMSB 2007. to appear.

Digital Library

[33]

Gheorghe Păun and Francisco J. Romero-Campero. 2008. Membrane computing as a modeling framework. Cellular systems case studies. In Formal Methods for Computational Systems Biology (LNCS), Vol. 5016. 168--214.

Digital Library

[34]

J.C Raoult. 1984. On graph rewriting. TCS 32 (1984), 1--24.

Digital Library

[35]

Aviv Regev and Ehud Shapiro. 2002. Cells as computation. Nature 419 (September 2002), 343.

[36]

P. Ruet. 2015. Negative local feedbacks in Boolean networks. ArXiv e-prints (2015).

[37]

René Thomas. 1973. Boolean Formalization of Genetic Control Circuits. Journal of Theoretical Biology 42 (1973), 563--585.

[38]

Peter J. M. Van Haastert and Peter N. Devreotes. 2004. Chemotaxis: signalling the way forward. Nat Rev Mol Cell Biol 5, 8 (08 2004), 626--634.

[39]

Jia You. 2015. DARPA sets out to automate research. Science 347, 6221 (2015), 465.

Cited By

Gale ELobski LZanasi F(2023)A Categorical Approach to Synthetic ChemistryTheoretical Aspects of Computing – ICTAC 202310.1007/978-3-031-47963-2_17(276-294)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-47963-2_17
Hillston J(2018)Stochastic process algebras and their markovian semanticsACM SIGLOG News10.1145/3212019.32120235:2(20-35)Online publication date: 30-Apr-2018
https://dl.acm.org/doi/10.1145/3212019.3212023

Index Terms

Systems biology
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Systems biology reveals biology of systems

In the last decades, genomic and postgenomic technologies obtained a great amount of information on molecular bases of cell physiology and organization. In spite of this, the knowledge of cells and living organisms in their entirety, is far from being ...
Systems Biology: The Challenge of Complexity
Systems biology approaches to corticosteroid pharmacogenomics and systemic inflammation

Comments

Information & Contributors

Information

Published In

cover image ACM SIGLOG News

ACM SIGLOG News Volume 4, Issue 3

July 2017

79 pages

EISSN:2372-3491

DOI:10.1145/3129173

Editor:
Andrzej Murawski
University of Warwick

Issue’s Table of Contents

Copyright © 2017 Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2017

Published in SIGLOG Volume 4, Issue 3

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
74
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gale ELobski LZanasi F(2023)A Categorical Approach to Synthetic ChemistryTheoretical Aspects of Computing – ICTAC 202310.1007/978-3-031-47963-2_17(276-294)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-47963-2_17
Hillston J(2018)Stochastic process algebras and their markovian semanticsACM SIGLOG News10.1145/3212019.32120235:2(20-35)Online publication date: 30-Apr-2018
https://dl.acm.org/doi/10.1145/3212019.3212023

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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents