Show simple item record

dc.contributor.author
August, Elias
dc.contributor.author
Papachristodoulou, Antonis
dc.date.accessioned
2019-04-15T16:31:55Z
dc.date.available
2017-06-09T05:18:03Z
dc.date.available
2019-04-15T16:31:55Z
dc.date.issued
2009
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/1752-0509-3-25
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/21931
dc.identifier.doi
10.3929/ethz-b-000021931
dc.description.abstract
Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections) while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational) and uncertainties in the data are taken into account. It also produces a network structure that can explain the real experimental data of L. lactis and is similar to the one found in the literature. Numerical methods based on Linear Programming can therefore help determine efficiently the network structure of biological systems from large data sets. The overall objective of this work is to provide methods to increase our understanding of complex biochemical systems, particularly through their interconnection and their non-equilibrium behavior.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Lactis
en_US
dc.subject
Gene Regulatory Network
en_US
dc.subject
Glycolytic Pathway
en_US
dc.subject
Sparsity Structure
en_US
dc.subject
Mass Action Kinetic
en_US
dc.title
Efficient, Sparse Biological Network Determination
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
dc.date.published
2009-02-23
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
3
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
25
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
005468370
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-09T05:18:11Z
ethz.source
ECIT
ethz.identifier.importid
imp59364cf66963593740
ethz.ecitpid
pub:36717
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T17:54:32Z
ethz.rosetta.lastUpdated
2020-02-15T18:28:03Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Efficient,%20Sparse%20Biological%20Network%20Determination&rft.jtitle=BMC%20Systems%20Biology&rft.date=2009&rft.volume=3&rft.spage=25&rft.issn=1752-0509&rft.au=August,%20Elias&Papachristodoulou,%20Antonis&rft.genre=article&
 Search via SFX

Files in this item

Thumbnail

Publication type

Show simple item record