Estimating hidden influences in metabolic and gene regulatory networks

Florian Blöchl; Fabian J. Theis

doi:10.1007/978-3-642-00599-2_49

Estimating hidden influences in metabolic and gene regulatory networks

Florian Blöchl, Fabian J. Theis

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

We address the applicability of blind source separation (BSS) methods for the estimation of hidden influences in biological dynamic systems such as metabolic or gene regulatory networks. In simple processes obeying mass action kinetics, we find the emergence of linear mixture models. More complex situations as well as hidden influences in regulatory systems with sigmoidal input functions however lead to new classes of BSS problems.

Original language	English
Pages (from-to)	387-394
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5441
DOIs	https://doi.org/10.1007/978-3-642-00599-2_49
State	Published - 2009
Externally published	Yes
Event	8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil Duration: 15 Mar 2009 → 18 Mar 2009

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10.1007/978-3-642-00599-2_49

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title = "Estimating hidden influences in metabolic and gene regulatory networks",

abstract = "We address the applicability of blind source separation (BSS) methods for the estimation of hidden influences in biological dynamic systems such as metabolic or gene regulatory networks. In simple processes obeying mass action kinetics, we find the emergence of linear mixture models. More complex situations as well as hidden influences in regulatory systems with sigmoidal input functions however lead to new classes of BSS problems.",

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doi = "10.1007/978-3-642-00599-2_49",

language = "English",

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AU - Theis, Fabian J.

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AB - We address the applicability of blind source separation (BSS) methods for the estimation of hidden influences in biological dynamic systems such as metabolic or gene regulatory networks. In simple processes obeying mass action kinetics, we find the emergence of linear mixture models. More complex situations as well as hidden influences in regulatory systems with sigmoidal input functions however lead to new classes of BSS problems.

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U2 - 10.1007/978-3-642-00599-2_49

DO - 10.1007/978-3-642-00599-2_49

M3 - Conference article

AN - SCOPUS:67149102168

SN - 0302-9743

VL - 5441

SP - 387

EP - 394

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009

Y2 - 15 March 2009 through 18 March 2009

ER -

Estimating hidden influences in metabolic and gene regulatory networks

Abstract

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