Motivation: Supporting the evolutionary modeling process of dynamic
biochemical networks based on sampled in vivo data requires more than
just simulation. In the course of the modeling process, the modeler is
typically concerned not only with a single model but also with
sequences, alternatives and structural variants of models. Powerful
automatic methods are then required to assist the modeler in the
organization and the evaluation of alternative models. Moreover, the
structure and peculiarities of the data require dedicated tool support.
Summary: To support all stages of an evolutionary modeling process, a
new general formalism for the combinatorial specification of large model
families is introduced. It allows for automatic navigation in the space
of models and excludes biologically meaningless models on the basis of
elementary flux mode analysis. An incremental usage of the measured data
is supported by using splined data instead of state variables. With
MMT2, a versatile tool has been developed as a computational engine
intended to be built into a tool chain. Using automatic code generation,
automatic differentiation for sensitivity analysis and grid computing
technology, a high performance computing environment is achieved. MMT2
supplies XML model specification and several software interfaces. The
performance of MMT2 is illustrated by several examples from ongoing
research projects.
%0 Journal Article
%1 ISI:000228401800048
%A Haunschild, MD
%A Freisleben, B
%A Takors, R
%A Wiechert, W
%C GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
%D 2005
%I OXFORD UNIV PRESS
%J BIOINFORMATICS
%K myown
%N 8
%P 1617-1625
%R 10.1093/bioinformatics/bti225
%T Investigating the dynamic behavior of biochemical networks using model
families
%U https://doi.org/10.1093/bioinformatics/bti225
%V 21
%X Motivation: Supporting the evolutionary modeling process of dynamic
biochemical networks based on sampled in vivo data requires more than
just simulation. In the course of the modeling process, the modeler is
typically concerned not only with a single model but also with
sequences, alternatives and structural variants of models. Powerful
automatic methods are then required to assist the modeler in the
organization and the evaluation of alternative models. Moreover, the
structure and peculiarities of the data require dedicated tool support.
Summary: To support all stages of an evolutionary modeling process, a
new general formalism for the combinatorial specification of large model
families is introduced. It allows for automatic navigation in the space
of models and excludes biologically meaningless models on the basis of
elementary flux mode analysis. An incremental usage of the measured data
is supported by using splined data instead of state variables. With
MMT2, a versatile tool has been developed as a computational engine
intended to be built into a tool chain. Using automatic code generation,
automatic differentiation for sensitivity analysis and grid computing
technology, a high performance computing environment is achieved. MMT2
supplies XML model specification and several software interfaces. The
performance of MMT2 is illustrated by several examples from ongoing
research projects.
@article{ISI:000228401800048,
abstract = {{Motivation: Supporting the evolutionary modeling process of dynamic
biochemical networks based on sampled in vivo data requires more than
just simulation. In the course of the modeling process, the modeler is
typically concerned not only with a single model but also with
sequences, alternatives and structural variants of models. Powerful
automatic methods are then required to assist the modeler in the
organization and the evaluation of alternative models. Moreover, the
structure and peculiarities of the data require dedicated tool support.
Summary: To support all stages of an evolutionary modeling process, a
new general formalism for the combinatorial specification of large model
families is introduced. It allows for automatic navigation in the space
of models and excludes biologically meaningless models on the basis of
elementary flux mode analysis. An incremental usage of the measured data
is supported by using splined data instead of state variables. With
MMT2, a versatile tool has been developed as a computational engine
intended to be built into a tool chain. Using automatic code generation,
automatic differentiation for sensitivity analysis and grid computing
technology, a high performance computing environment is achieved. MMT2
supplies XML model specification and several software interfaces. The
performance of MMT2 is illustrated by several examples from ongoing
research projects.}},
added-at = {2018-06-08T13:09:31.000+0200},
address = {{GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND}},
affiliation = {{Wiechert, W (Reprint Author), Univ Siegen, Dept Simulat, Paul Bonatz Str 9-11, D-57068 Siegen, Germany.
Univ Siegen, Dept Simulat, D-57068 Siegen, Germany.
Univ Marburg, Dept Math \& Comp Sci, D-35032 Marburg, Germany.
Forschungszentrum Julich, Res Ctr, D-52425 Julich, Germany.}},
author = {Haunschild, MD and Freisleben, B and Takors, R and Wiechert, W},
author-email = {{wiechert@simtec.mb.uni-siegen.de}},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c8a1b01dbecce5013674c74c2c0db3d7/ralftakors},
da = {{2018-01-26}},
doc-delivery-number = {{916QX}},
doi = {{10.1093/bioinformatics/bti225}},
interhash = {6c0b269eb7c4abc5e26817c0c96848b2},
intrahash = {c8a1b01dbecce5013674c74c2c0db3d7},
issn = {{1367-4803}},
journal = {{BIOINFORMATICS}},
journal-iso = {{Bioinformatics}},
keywords = {myown},
keywords-plus = {{SYSTEMS BIOLOGY; SIMULATION; CELL}},
language = {{English}},
month = {{APR 15}},
number = {{8}},
number-of-cited-references = {{26}},
oa = {{gold}},
pages = {{1617-1625}},
publisher = {{OXFORD UNIV PRESS}},
research-areas = {{Biochemistry \& Molecular Biology; Biotechnology \& Applied
Microbiology; Computer Science; Mathematical \& Computational Biology;
Mathematics}},
times-cited = {{31}},
timestamp = {2018-06-08T11:09:31.000+0200},
title = {{Investigating the dynamic behavior of biochemical networks using model
families}},
type = {{Article}},
unique-id = {{ISI:000228401800048}},
url = {https://doi.org/10.1093/bioinformatics/bti225},
usage-count-last-180-days = {{0}},
usage-count-since-2013 = {{1}},
volume = {{21}},
web-of-science-categories = {{Biochemical Research Methods; Biotechnology \& Applied Microbiology;
Computer Science, Interdisciplinary Applications; Mathematical \&
Computational Biology; Statistics \& Probability}},
year = {{2005}}
}