A novel method for signal transduction network inference from indirect experimental Evidence

Réka Albert; Bhaskar DasGupta; Riccardo Dondi; Sema Kachalo; Eduardo Sontag; Alexander Zelikovsky; Kelly Westbrooks

doi:10.1007/978-3-540-74126-8_38

A novel method for signal transduction network inference from indirect experimental Evidence

Réka Albert, Bhaskar DasGupta, Riccardo Dondi, Sema Kachalo, Eduardo Sontag, Alexander Zelikovsky, Kelly Westbrooks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: on the theoretical and algorithmic side, we formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach. On the application side, we validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. [20] and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

Original language	English (US)
Title of host publication	Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings
Publisher	Springer Verlag
Pages	407-419
Number of pages	13
ISBN (Print)	9783540741251
DOIs	https://doi.org/10.1007/978-3-540-74126-8_38
State	Published - 2007
Event	7th International Workshop on Algorithms in Bioinformatics, WABI 2007 - PhiIadelphia, PA, United States Duration: Sep 8 2007 → Sep 9 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4645 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Workshop on Algorithms in Bioinformatics, WABI 2007
Country/Territory	United States
City	PhiIadelphia, PA
Period	9/8/07 → 9/9/07

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-74126-8_38

Cite this

Albert, R., DasGupta, B., Dondi, R., Kachalo, S., Sontag, E., Zelikovsky, A., & Westbrooks, K. (2007). A novel method for signal transduction network inference from indirect experimental Evidence. In Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings (pp. 407-419). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4645 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-540-74126-8_38

Albert, Réka ; DasGupta, Bhaskar ; Dondi, Riccardo et al. / A novel method for signal transduction network inference from indirect experimental Evidence. Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings. Springer Verlag, 2007. pp. 407-419 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A novel method for signal transduction network inference from indirect experimental Evidence",

abstract = "In this paper we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: on the theoretical and algorithmic side, we formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach. On the application side, we validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. [20] and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.",

author = "R{\'e}ka Albert and Bhaskar DasGupta and Riccardo Dondi and Sema Kachalo and Eduardo Sontag and Alexander Zelikovsky and Kelly Westbrooks",

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booktitle = "Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings",

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Albert, R, DasGupta, B, Dondi, R, Kachalo, S, Sontag, E, Zelikovsky, A & Westbrooks, K 2007, A novel method for signal transduction network inference from indirect experimental Evidence. in Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4645 LNBI, Springer Verlag, pp. 407-419, 7th International Workshop on Algorithms in Bioinformatics, WABI 2007, PhiIadelphia, PA, United States, 9/8/07. https://doi.org/10.1007/978-3-540-74126-8_38

A novel method for signal transduction network inference from indirect experimental Evidence. / Albert, Réka; DasGupta, Bhaskar; Dondi, Riccardo et al.

Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings. Springer Verlag, 2007. p. 407-419 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4645 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Albert, Réka

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AU - Kachalo, Sema

AU - Sontag, Eduardo

AU - Zelikovsky, Alexander

AU - Westbrooks, Kelly

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AB - In this paper we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: on the theoretical and algorithmic side, we formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach. On the application side, we validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. [20] and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

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Albert R, DasGupta B, Dondi R, Kachalo S, Sontag E, Zelikovsky A et al. A novel method for signal transduction network inference from indirect experimental Evidence. In Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings. Springer Verlag. 2007. p. 407-419. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-74126-8_38

A novel method for signal transduction network inference from indirect experimental Evidence

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