REVIEW ARTICLE
Applications and trends in systems biology in
biochemistry
Katrin Hu
¨
bner, Sven Sahle and Ursula Kummer
Department of Modeling of Biological Processes, COS Heidelberg/BioQuant, University of Heidelberg, Germany
Keywords
metabolism; modeling; quantitative
experiments; signaling; simulation; systems
biology
Correspondence
U. Kummer, Department of Modeling of
Biological Processes, COS Heidelberg/
BioQuant, University of Heidelberg, Im
Neuenheimer Feld 267, 69120 Heidelberg,
Germany
Fax: +49 6221 5451483
E-mail: ursula.kummer@bioquant.
uni-heidelberg.de
(Received 10 January 2011, revised 31 May
2011, accepted 15 June 2011)
doi:10.1111/j.1742-4658.2011.08217.x
Systems biology has received an ever increasing interest during the last
decade. A large amount of third-party funding is spent on this topic, which
involves quantitative experimentation integrated with computational
modeling. Industrial companies are also starting to use this approach more
and more often, especially in pharmaceutical research and biotechnology.
This leads to the question of whether such interest is wisely invested and
whether there are success stories to be told for basic science and/or technol-
ogy/biomedicine. In this review, we focus on the application of systems

system and it is therefore systems biology). This latter
point necessitates the definition of the term ‘systems
biology’ as we (the authors) understand it, as outlined
below.
Systems biology combines quantitative experimental
data from complex molecular networks (e.g. biochem-
istry, cell biology in the living cell) with computational
modeling. Here, computational modeling does not
refer to statistical models or models of data mining
but rather to a mathematical or ’virtual’ representation
of the living system of interest in the computer, where
Abbreviations
FBA, flux balance analysis; ODE, ordinary differential equation; PDE, partial differential equation
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2767
there is also a correspondence between parts of the
biological system and parts of the model. This
representation allows a computational analysis using
systems theoretical approaches.
This definition is probably shared by many scientists
in the field [1,2]. The actual term ‘systems biology’ was
coined in 1968 by Mesarovic
´
[3]. Soon afterward, the
first conceptional developments on the theoretical side
layed the foundation of the field, such as metabolic
control analysis [4,5] and biochemical systems theory
[6]. In the 1980s, the development of extreme currents
and elementary modes [7,8] and stochastic frameworks
[9] followed. These conceptional approaches were then
implemented in specialized software tools, as will be

cycle and of circadian rhythms have been properly
reviewed recently [10,11] and therefore we do not
include them here. With this scope in mind, we opti-
mized a keyword search for PubMed with the following
limits: year AND [in silico OR biology OR biochem*
OR bioinformatic* OR biological OR intracellular OR
biophysic* AND (modeling OR modeling OR ‘mathe-
matical model’OR‘mathematical models’OR‘kinetic
model’OR‘kinetic models’OR‘differential equation
model’OR‘multiscale model’OR‘dynamic model’OR
‘quantitative model’OR‘computational model’OR‘petri
net model’OR‘agent based model’OR‘stochastic
model’OR‘flux balance’OR‘dynamical model’OR
‘homeostatic model’OR(model AND simulation*)]
NOT ‘protein structure’ NOT ‘animal model’ NOT
review[publication type] AND (metabolism OR meta-
bolic OR
signal* OR ‘cell cycle’ORoscillation*) NOT
pharmacokinetic* NOT pharmacodynamic* NOT elec-
trophysiolog* NOT ‘molecular modeling’ NOT ‘molecu-
lar modeling’ NOT ‘homology modeling’ NOT
‘homology modeling’ NOT ‘MD simulation’ NOT
‘molecular dynamics’).
This search resulted in approximately 17 000 articles
of which we read the titles and abstracts and, in cases
of doubt, the article as such to select the relevant ones,
resulting in the approximately 400 articles that we
review. Even though we try to be as complete as possi-
ble, it is obvious that we employed heuristics with the
above strategy and also certainly and unintentionally

of news and views, articles and minireviews, and so
Systems biology in biochemical research K. Hu
¨
bner et al.
2768 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
on. The number of articles appearing annually within
the last few years is approximately four-fold greater
than in the year 2000 (Fig. 2). Before 2000, there are
only few articles that actually would fall into the above
category, as quickly checked by the same query. Of
course, many valuable modeling articles had been pub-
lished before 2000, although very few of these worked
directly with quantitative biological data. One of the
exceptions is the field of calcium signaling, where com-
putational modeling very quickly formed the basis for
deciphering the mechanism behind calcium oscillations
[18].
In addition to the general trend to use systems biol-
ogy approaches more frequently, there is also an
increasing trend in the articles to actually validate the
developed models with experimental data. This is defi-
nitely a positive development because the actual vali-
dation of the computational models aids in an
assessment of their reliability.
The number of journals publishing systems biology
work is also increasing, although there are only a few
journals that often appear in our data. The most
me
Fig. 1. Systematic tree for the navigation of Table 1. Articles are ordered according to the system studied and systems are annotated with
gene ontology (GO) numbers.

European Journal of Biochemistry), Journal of Biological
Chemistry and Metabolic Engineering. Within the last
few years, more specialized journals have established
themselves. Here, the most frequently appearing ones
are BMC Systems Biology, Molecular Systems Biology
and PLoS Computational Biology. There is a clear trend
from the more engineering-oriented journals to the basic
research-oriented ones over the years.
Often, systems biology articles are quite long, which
is a result of the fact that they have to describe both
experimental and computational methodology, as well
as the results from both. Similar to many other fields,
this has led to a rather annoying trend, namely putting
extensive material into a supplement. This results in
articles that are almost uncomprehensible without
reading the supplementary material as well. Very often,
the actual model that is the basis for the results, and
thus is an absolutely crucial part of the work, ends up
in the supplementary information. Even though it is
often possible to download this material along with
the original article, it does not make the reading of a
scientific work any easier by pushing central informa-
tion into an additional file. The least that journals
should consider is an automated packaging of both
files into one pdf for download. Fortunately, this has
already been implemented for least a few journals (e.g.
Nature, Journal of Biological Chemistry). One addi-
tional issue arising with this policy is the fact that
references cited in the supplementary material do not
count for citation indices and the computation of

15
20
25
30
35
Biophysical journal
BMC systems biology
Molecular systems biology
Journal of theoretical biology
PLoS computational biology
Biotechnology and bioengineering
FEBS journal
Journal of biological chemistry
Metabolic engineering
PLoS one
# publications
Journals
Fig. 3. Number of publications describing
systems biology applications in biochemistry
in the years 2000–2010 in the 10 most
often used journals.
Systems biology in biochemical research K. Hu
¨
bner et al.
2770 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
nature, reflecting their importance in biotechnology.
Here, apart from the central energy metabolism includ-
ing glycolysis (Fig. 5), pathways of biotechnological
importance such as lysine synthesis [29] in Corynebac-
terium glutamicum, sucrose synthesis [30–32] in sugar

up again (Fig. 6).
Experimental approaches
Here, we focus on the experimental approaches used in
conjecture with computational modeling, in the core of
a systems biology approach.
Experimental data in systems biology are obviously
either time-series data (if used for dynamic models) or
single time point data (if used for static models). How-
0
10
20
30
40
50
60
70
Genome-scale
Central
Carbohydrate
Energy
Amino acid
Calcium
I-κB/NF-κB
MAPK (ERK)
JAK-STAT
Apoptosis
# publications
Metabolism
Signaling
Fig. 5. Number of publications describing

FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2771
ever, in some cases, dynamic models are also build
using steady-state profiles. This is true for data used as
a basis for modeling, as well as for data used for
model validation.
The compounds commonly measured in time-series
analysis are metabolites (hereon, we refer to all chemi-
cal species other than macromolecules as metabolites),
proteins and, to a lesser extent (in the light of the pres-
ent reviewed systems), RNA and DNA. In addition,
enzymatic activities and cellular properties such as
growth and death rates are measured in a time-depen-
dent manner.
Only a very few metabolites are measured in vivo
(e.g. using imaging technologies). Examples that fre-
quently are measured using in vivo methods are cal-
cium (in the more than 30 publications studying
calcium signaling) and NADPH [35]. In only a few
cases, NMR is also employed for in vivo studies [36–
39]. However, most often, metabolites are extracted
from cells and measured in vitro. This puts limits on
the time resolution of the experimental results, which
does not allow fast dynamics to be followed. In many
cases, the temporal dynamics of the system of studied
is rich over a relatively short time-scale (e.g. calcium,
p53, NF-jB, nuclear factor jB), which was only dis-
covered after in vivo methods became available for
these compounds. Together with the relatively high
level of noise in many of the in vitro measurements,
this highlights the need for a strong effort to develop

standard kits. If these are measured in cell extracts or
in vitro under physiological conditions, they are a
valuable source for the modeling. However, studies
0
10
20
30
40
50
60
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2000
2001
2002
2003
2004
2005
2006
2007
2008

based modeling (e.g. the entry of a reaction scheme)
and translate this reaction scheme into ODEs. How-
ever, temporal or dynamic models are mainly simu-
lated and analyzed in this mathematical framework.
All other approaches do not yet play a significant role.
Nevertheless, stochastic approaches are specifically
used in the context of signaling networks because these
networks often feature low copy numbers of molecules,
which poses problems for the ODE framework. Static
or stoichiometric models are mainly analyzed using
flux balance analysis (FBA), which has become the sec-
ond most abundant computational approach in recent
years.
Unexpectedly, few models describe spatial as well as
temporal developments of biochemical systems. This
might be the result of a variety of factors: First, corre-
sponding experimental data are still sacrve. Second,
computational methods (e.g. for the parametrization of
the models) are much less developed than for ODE
based models. Furthermore, there are fewer user-
friendly software tools that allow spatial modeling
and, thus, more programming is required for this type
of modeling. This is also reflected by the fact that no
increase in the usage of spatial models has been
observed over the last 10 years. Unless more user-
friendly tools become available, we consider that there
will be no clear trend in this direction. For the few
spatial models available, the dominating computational
approach is the use of partial differential equations
(PDEs).

their work might only be cited in the supplement,
which does not appear in the science citation index.
Accordingly, it is very hard to review the trends within
the algorithms and tools. It is, however, clear that the
commercial software matlab (MathWorks, Natick,
MA, USA; www.mathworks.com) is the dominating
software (Fig. 8). Additional commercial software
packages that are widely used are mathematica (Wol-
fram Research, Champaign, IL, USA; www.wolfram
com) and, for the set-up and analysis of whole-genome
0
50
100
150
200
250
ODE
Stoichiometric
PDE
Stochastic
Logic
Petri net
Hybrid
# publications
Modeling methodology
Fig. 7. Number of publications describing systems biology applied
to biochemistry in the years 2000–2010 using a specific computa-
tional modeling approach.
K. Hu
¨

and necessitates the development of software standards
for the exchange (sbml [47], cellml [48]) and docu-
mentation of models (miriam [49], as well as central
data resources for the storage of computational
models, such as the well curated BioModels database
[12], JWS Online [46], the CellML repository [50] or,
for whole-genome scale models, the BIGG database
[51]). These approaches will hopefully help to over-
come problems of insufficient documentation, at least
on the model side. On the side of computational meth-
ods, there is currently a similar community effort that
creates a standard for minimal information called
MIASE [52].
Finally, we would like to mention that by and large
our results agree with an analysis of currently used
computational standards, approaches and tools that
was based on questionaires distributed to computa-
tional scientists in the field and published in 2007 [53].
However, because of the differring nature of data gen-
eration, there are also a few significant differences (e.g.
approaches) that are rarely mentioned in published
research (as in the present review) and are more often
named in the questionaires. As an example, probabilis-
tic approaches occur at least in 20% of the questio-
naire responses, although they are significantly less
prevalent in the publications reviewed here. A similar
situation applies to some software tools that are more
dominant in the questionaire-based survey and are
scarcely noted in the actual publications.
Discussion

80
100
120
Matlab
NG
SimPheny
LINDO
COPASI
XPPAUT
Mathematica
Gepasi
Own
COBRA
Berkeley
madonna
# publications
Software
Fig. 8. Number of publications describing systems biology applied
to biochemistry in the years 2000–2010 employing the ten most
commonly used software tools.
Systems biology in biochemical research K. Hu
¨
bner et al.
2774 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
When compiling this review, we came across a num-
ber of unexpected problems, some of which we have
already noted above. Missing documentation of com-
putational research is a clear and abundant problem
that makes systems biology research less tractable than
it should be. In our opinion, this must change. In addi-

insights from the integration of computational model-
ing with quantitative experimentation, although the
majority clearly do. In many studies, computational
modeling is used to understand complex mechanisms
that are difficult to dissect by pure experimental means
and to generate likely hypotheses that push forward
our comprehension of the complicated interactions and
their functionality in quite an efficient way. There are
many examples for this and we only want to highlight
a few of them. One of the prominent examples is the
field of calcium signal transduction where our current
understanding of the mechanism behind the often
observed calcium oscillations would not have been
possible without computational modeling, with this
having already started way before the onset of systems
biology, as reviewed here. However, important new
insights have been generated in the past decade. Thus,
the impact of calcium dynamics on CaMKII has been
studied in detail (see entry 210 in Table 1). Other
downstream effects have been investigated, including
apoptosis (see entry 229 in Table 1). In addition, the
stochasticity of single calcium channels and its impact
on the overall dynamics have been investigated in
many studies (see entry 314 in Table 1).
Further signal transduction systems that exhibit
complex behaviour have been explained quite well with
the aid of validated computational modeling. We are
only able to mention a few examples and, once again,
have to refer to the material in Table 1. A beautiful
study explains the response of yeast to osmotic shock

because these have been reviewed recently [10,11].
Therefore, the actual number of successful systems
biology studies will be several times the amount
reviewed here.
Acknowledgements
We would like to acknowledge the Klaus Tschira
Foundation and the BMBF (Virtual Liver Network
and SysMO) for funding.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2775
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Table 1. All articles describing systems biology approaches in biochemistry are summarized, as ordered by: (a) system studied; (b) organism and (c) publication year, accompanied by refer-
ence details and major findings, as well as the computational approaches, the accessibility of the model and the experimental approaches employed. ‘Model’ indicates the principal model-
ing approach (stoich being the abbreviation for stoichiometric model). ‘Analysis’ represents the model analysis employed [ss, steady-state; sim, simulation; fit, parameter estimation; opt,
optimization; sens, sensitivities (including metabolic control analysis; MCA); bif, bifurcation analysis; stab, stability; rob, robustness; pident, parameter identifiability; mident, model identifi-
cation; infer, parameter inference; mred, model reduction; osc, oscillations; SNA, stoichiometric network analysis; FDA, flux distribution analysis]. Only publically available databases are
cited for accessibility because local websites are often not online after a short time (which was also observed during the current review). Here, BM gives the ID in the BioModels data-
base. JWS or CellML indicate the availability in JWS Online or the CellML repository, respectively. NG, not given (or, in the case of accessibility, not available). Only computational meth-
ods for the actual modeling and experimental methods that are used as basis or for the validation of the computational models are listed. Single time points of metabolites or proteins,
etc., indicate that single measurements either at steady-state or at a different specific state (e.g. exponential growth of cells) are taken. C5a, complement 5a; CaMKII, calmodulin-depen-
dent protein kinase II; CaN, calcineurin; DHPR, dihydropyridine receptor; EGF, epidermal growth factor; EMSA, electromobility shift assay; Epo, erythropoietin; ER, endoplasmic reticulum;
ERK, extracellular signal-regulated kinase; FBP, fructose 1,6-bisphosphate; FRET, fluorescence resonance energy transfer; GPCR, G protein-coupled receptor; GSH, glutathione; HRG,
heregulin; IFN, interferon; IKK, I-jB kinase; IL, interleukin; IP3, inositol 1,4,5-triphosphate; IRS, insulin receptor substrate; JAK-STAT, janus kinase-signal transducer and activator of tran-
scription, mitogen-activated protein kinase; LPS, lipopolysaccharide; MAPK, mitogen-activated protein kinase; MCF, macrophare chemotactic factor; MCIP, modulatory calcineurin-interact-
ing protein; MDCK, Madin Darby canine kidney; MEF, mouse embryonic fibroblast; MEK, MAP kinase/ERK kinase; NFAT, nuclear factor of activated T-cells; NF-jB, nuclear factor jB; NGF,
nerve growth factor; PDGF, platelet-derived growth factor; PDGFR, platelet-derived growth factor receptor; PEP, phosphoenolpyruvate; PFK, phosphofructokinase; PFL, pyruvate formate-
lyase; Pi, inorganic phosphate; PI3K, phosphatidylinositol 3-kinase; PIP, phosphatidylinositol 4,5-bisphosphate; PKA, protein kinase A; PKC, protein kinase C; PLC, phospholipase C; PP2A,
type 2A phosphatase; PPP, pentose phosphate pathway; PTS, photransferase system; ROS, reactive oxygen species; RyR, ryanodine receptor; SERCA, SERCA, sarcoplasmic reticulum
Ca
2+
ATPase; TCA, tricarboxylic acid; TGF, transforming growth factor; TNF, tumor necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand; XAIP, X-linked inhibitor of apoptosis pro-

C, Sa
´
nchez-Jime
´
nez
F & Medina MA
(2008)
Amino Acids
34, 223–229.
2 Amino acid,
arginine
synthesis
Escherichia
coli
The outcome of combinations of
perturbations on cellular arginine
concentration was predicted
accurately, establishing the model
as a powerful tool for the design
of arginine-overproducing strains
ODE sim
XPPAUT NG Single time point of
fluxes and metabolites
measured by HPLC
and assay kits
Caldara M, Dupont G,
Leroy F, Goldbeter A,
Vuyst LD & Cunin R
(2008) J Biol Chem
283, 6347–6358.

Single time point of
proteins measured by
ELISA; enzyme kinetics
measured by assays;
refers to single time
points of metabolites
Curien G, Bastien O,
Robert-Genthon M,
Cornish-Bowden A,
Cardenas ML &
Dumas R (2009)
Mol Syst Biol
5, 271.
4 Amino acid,
lysine
biosynthesis
Corynebacterium
glutamicum
Targets for optimization of lysine
production (aspartokinase, lysine
permease, extracellular lysine
concentration) were predicted
and tested successfully
ODE ss, sim,
MCA
MATLAB NG Time series of metabolites
measured by HPLC and
enzyme assays; biomass
and cell number
measured by

methionine
Mammalian,
hepatocytes
The behavior of a constructed model
in response to genetic abnormalities
and dietary deficiencies is similar to
the changes seen in a wide variety
of experimental studies
ODE sim NG CellML Refers to single time
points of metabolites
Reed MC, Nijhout
HF, Sparks R &
Ulrich CM (2004)
J Theor Biol 226,
33–43.
7 Amino acid,
methionine
and threonine
synthesis
Arabidopsis
thaliana
Under near physiological conditions,
S-adenosylmethionine, but not AMP,
modulates the partition of a
steady-state flux of
phosphohomoserine
ODE sim,
sens, fit
KALEIDA-
GRAPH

Chassagnole C, Fell
DA, Raı
¨
s B, Kudla B
& Mazat JP (2001)
Biochem J 356,
433–444.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2779
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
9 Amino acid,
tryptophan
synthesis
Escherichia
coli
An integrated optimization of the
whole network leads to a significant
increase in tryptophan production
rate for all systems under study
ODE sim,
opt
NG NG Refers to enzyme
kinetic measurements
Schmid JW, Mauch
K, Reuss M, Gilles

the benzenoid network. By contrast,
control of flux through the
b-oxidative and non-b-oxidative
pathways is highly distributed
ODE sim, fit,
sens
MATLAB NG Time series of
metabolites
measured by
GC-MS
Colo
´
n AM, Sengupta
N, Rhodes D,
Dudareva N &
Morgan J (2010)
Plant J 62, 64–76.
12 Carbohydrate,
ethanol
production
Saccharomyces
cerevisiae
A model developed succeeded in
describing and interpreting the
effects of ethanol stress. In
particular, the ratio between the
kinetic constants associated with
ethanol production and glucose
consumption gave the estimation of
the metabolic yield of the processes

¨
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2780 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
14 Carbohydrate,
glycerol
synthesis
Saccharomyces
cerevisiae
The developed model indicates that
the best strategy to increase flux
through the pathway is not to
increase enzyme activity, substrate
concentration or coenzyme
concentration alone but, instead,
to increase all of these parameters
in conjunction with each other
ODE ss, sim,
sens
GEPASI BM 76,
JWS,
CellML
Time series of metabolites
measured by assays; fluxes
measured by assay kits;
enzyme activities measured
by assays

A, Encalada R,
Olivos A,
Mendoza-
Herna
´
ndez G &
Moreno-Sa
´
nchez R
(2007) FEBS J
274, 4922–4940.
16 Carbohydrate,
glycolysis
Lactococcus
lactis
Modeling Pi explicitly and regulation
of pyruvate kinase (by Pi and FBP),
PFK (by PEP) and GAPDH (by
NADH) are critical to describe
observations (rapid increase in PEP,
Pi and gradual decrease in FBP)
during glucose run-out experiments
ODE ss, sim
GEPASI JWS,
CellML
Refers to time series of
metabolites measured
by
13
C-NMR

Marino S, Lall R,
Goel G, Neves AR
& Santos H (2006)
Syst Biol (Stevenage)
153, 286–298.
18 Carbohydrate,
glycolysis
Mus musculus,
brain
Results suggest relations between
the changes in morphology,
glycolytic flux, ATP production
and ATP levels
ODE ss, sim
MATHEMATICA JWS Time series of metabolite
and enzyme activities
measured by
spectrophotometry;
single time point of
proteins measured by
immunoblotting
Ola
´
h J, Klive
´
nyi P,
Gardia
´
nG,Ve
´

225 + 236,
JWS, CellML
Refers to time series
of metabolites
Westermark PO &
Lansner A (2003)
Biophys J 85,
126–139.
20 Carbohydrate,
glycolysis
Saccharomyces
cerevisiae
It is shown that, in essence, the
common acetaldehyde concentration
can be modeled as a small
perturbation on the ’pacemaker’,
whose effect on the period of the
oscillations of cells in the same
suspension is indeed such that
a synchronization develops
ODE sim, osc NG BM 254,
JWS
Time series of metabolites
measured by
spectrophotometry
Bier M, Bakker BM
& Westerhoff HV
(2000) Biophys J
78, 1087 –1093.
21 Carbohydrate,

glycolysis
Saccharomyces
cerevisiae
The model reproduces several
experimental findings about
synchronization of oscillations across
cells, although coupling via
acetaldehyde does not explain the
measurements sufficiently
ODE sim, bif, osc
AUTO BM 206,
JWS, CellML
Refers to single time
point of metabolites
measured by assays
Wolf J & Heinrich R
(2000) Biochem J
345, 321–334.
23 Carbohydrate,
glycolysis
Saccharomyces
cerevisiae
A constructed model agrees with
almost all experimentally known
stationary concentrations and
metabolic fluxes, with the frequency
of oscillation and with the majority
of other experimentally known
kinetic and dynamical variables
ODE sim, fit, osc Own software BM 61, JWS,

fit, sens
GEPASI BM 172 Refers to time series
of metabolites measured
by spectroscopy,
assays, HPLC and MS;
refers to single time point
of enzyme kinetics
measured by assays
Pritchard L & Kell
DB (2002) Eur J
Biochem 269,
3894–3904.
25 Carbohydrate,
glycolysis
Saccharomyces
cerevisiae
Extension of a original model with
regulation of pyruvate
decarboxylase, a reversible
alcohol dehydrogenase, and drainage
of pyruvate. Using the method of
time rescaling in the extended
model, the description of the
transient closed-system experiments
is significantly improved
ODE sim, fit
CVODE NG Time series of
metabolites measured
by fluorescent spectroscopy
Hald BO & Sørensen

glycolysis
Trypanosoma
brucei
An analysis of the control of glycolytic
flux in bloodstream form T. brucei
shows that hexokinase, PFK and
pyruvate kinase are in excess, albeit
less than predicted. Depletion of
PFK and enolase had an effect on
the activity of some other
glycolytic enzymes
ODE ss, sim,
MCA
JARNAC BM
211
Time series of proteins
measured by
immunoblotting;
enzyme activities measured
by assay kits; fluxes
measured by enzymatic
assay and polarography
Albert MA,
Haanstra JR,
Hannaert V, Roy
JV, Opperdoes
FR, Bakker BM &
Michels PAM
(2005) J Biol Chem
280, 28306–28315.

ODE sim, fit
GEPASI NG Time series of fluxes
measured by
spectrophotometry
Orosz F, Wa
´
gner G,
Ortega F, Cascante M
& Ova
´
di J (2003)
Biochem Biophys
Res Commun 309,
792–797.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2783
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
30 Carbohydrate,
glycolysis, PPP
Brassica napus Net flux of glucose through the
oxidative PPP accounts for close to
10% of the total hexose influx. The
reductant produced by the oxidative
PPP accounts for at most 44% of
the NADPH and 22% of total

Biotechnol Bioeng
79, 53–73.
32 Carbohydrate,
glycolysis, PPP
Penicillium
chrysogenum
Different methods for calculating
fluxes from measurements are
compared. The fluxes that are
determined using
13
C-labeling data
are shown to be highly dependent
on the underlying metabolic network
Stoich ss (cumulative
bondomer
sim)
SPADIT NG Single time point of
metabolites measured
by NMR
van Winden WA,
van Gulik WM,
Schipper D,
Verheijen PJT,
Krabben P, Vinke
JL & Heijnen JJ
(2003) Biotechnol
Bioeng 83, 75–92.
33 Carbohydrate,
photosynthesis

opt
MATLAB BM 166 Refers to single time point
of metabolites
Zhu XG, de Sturler
E & Long SP (2007)
Plant Physiol 145,
513–526.
Systems biology in biochemical research K. Hu
¨
bner et al.
2784 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
35 Carbohydrate,
pyruvate
Lactococcus
lactis
The metabolic shift in pyruvate
metabolism (from homolactic to
mixed-acid fermentation) is studied.
It is shown that PFL plays a key role
and allosteric inhibition of PFL and
altered pfl gene expression are
important in the regulation
of the shift
ODE sim, fit NG NG Time series of proteins
measured by ELISA and
assay kits; metabolites

Martens DE,
Hugenholtz J &
Snoep JL (2002)
Mol Biol Rep 29,
157–161.
37 Carbohydrate,
pyruvate
Rattus
norvegicus,
liver
Even without a priori assumptions
about the optimization of the
metabolism. The model can predict
that the transformation of five
pyruvates into two citrates plus one
malate is the dominating reaction.
The conversion of pyruvate into its
products is almost optimal with
93% efficiency
Stoich SNA
MATHEMATICA NG Refers to single time point
of fluxes measured by
carbon labeling
Stucki JW &
Urbanczik R (2005)
FEBS J 272,
6244–6253.
38 Carbohydrate,
storage-
polysaccharide

reduction of cytosolic neutral
invertase levels are the most
promising targets for genetic
manipulation
ODE sim, sens,
SNA
METATOOL BM 23,
JWS
Refers to single time point
of fluxes and enzyme
kinetics
Rohwer JM &
Botha FC (2001)
Biochem J
358, 437–445.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2785
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
40 Carbohydrate,
sucrose
synthesis
Saccharum
officinarum
The model supports a hypothesis of
vacuolar sucrose accumulation

SCAMP NG Enzyme kinetic
measurements
Scha
¨
fer WE, Rohwer
JM & Botha FC
(2004) Eur J
Biochem 271,
3971–3977.
42 Carbohydrate,
xanthan
synthesis
Xanthomonas
campestris
Major constraint for maximum
xanthan gum production concerns
energy availability (i.e. respiratory
chain efficiency rather than carbon
precursor supply)
Stoich FBA,
opt
EXCEL NG Time series of biomass
measured by weighting
and spectrophotometry;
metabolites measured by
HPLC; rates measured by
gas chromatography; single
time point of enzyme
activities measured
by assays

Biotechnol Prog
19, 1136–1141.
44 Carbohydrate,
xylose
catabolism
Candida mogii Xylitol production is optimal for a
substrate of 10% glucose and
90% xylitol
ODE sim, fit
MATLAB NG Time series of metabolites
measured by assays
Tochampa W,
Sirisansaneeyakul
S, Vanichsriratana
W, Srinophakun P,
Bakker HHC &
Chisti Y (2005)
Bioprocess
Biosyst Eng 28,
175–183.
Systems biology in biochemical research K. Hu
¨
bner et al.
2786 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
45 Carbohydrate,
xylose

L-arabitol, but
is distributed over the first three
steps in the pathway, preceding and
following
L-arabitol. Flux control
appeared to be strongly dependent
on the intracellular
L-arabinose
concentration
ODE ss,
MCA
SCAMP NG Single time point of enzyme
activities measured by assay
kits, proteins measured by
SDS/PAGE
de Groot MJL,
Prathumpai W,
Visser J & Ruijter
GJG (2005)
Biotechnol Prog
21, 1610–1616.
47 Central Arthrospira
platensis
Photosynthetic growth is coupled to
production of NADH,H
+
and
therefore balancing for these
conditions is only possible with one
pathway converting NADH,H

was evaluated by using succinate
production as a case study
Stoich FBA
LINDO,
MATLAB
NG Refers to single time point
of growth rates
David H, Akesson M
& Nielsen J (2003)
Eur J Biochem 270,
4243–4253.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2787
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
49 Central Canis lupus
familiaris,
MDCK (kidney
epithelial cells)
Minimum substrate consumption flux
distribution to the fluxes estimated
from experiments unveiled high
overflow metabolism under the
applied process conditions
ODE sim, fit,
opt

by LC-ESI-MS
Amador-Noguez D,
Feng XJ, Fan J,
Roquet N, Rabitz H
& Rabinowitz JD
(2010) J Bacteriol
192, 4452–4461.
51 Central Corynebacterium
glutamicum
C. glutamicum mutant strains show
markedly different relative flux
contributions (increased flux into the
PPP and lysine biosynthesis,
decreased flux into the TCA) at the
same time as maintaining a constant
supply of NADPH
Stoich FBA, opt
MATLAB,
SIMULINK
NG Time series of metabolites
measured by
13
C GC-MS,
MALDI-TOF MS and HPLC;
cell density measured by
spectrophotometry
Wittmann C &
Heinzle E (2002)
Appl Environ
Microbiol 68,

and efficiency
Stoich FBA, opt,
SNA, rob
FLUXANALYZER NG Refers to single time
point of transcript
levels measured by
microarrays
Stelling J, Klamt S,
Bettenbrock K,
Schuster S &
Gilles ED (2002)
Nature 420,
190–193.
Systems biology in biochemical research K. Hu
¨
bner et al.
2788 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
54 Central Escherichia
coli
Temperature upshift induces
redirection of metabolic fluxes
(i.e. increased nongrowth-associated
energy demand and, thus, reduced
biomass yield)
Stoich FBA, opt
MATLAB NG Refers to time series of

si AL
(2004) Nature 427,
839–843.
56 Central Escherichia
coli
The model correctly reproduces the
behavior of E. coli vis-a-vis substrate
mixtures. In a mixture of glucose,
glycerol, and acetate, it prefers
glucose, then glycerol, and
finally acetate
Logic sim NG NG Single time points of fluxes
and mRNA measured
by microarrays
Asenjo AJ, Ramirez
P, Rapaport I,
Aracena J, Goles E
& Andrews BA
(2007) J Microbiol
Biotechnol 17,
496–510.
57 Central Escherichia
coli
Construction of a model and
comparison of the simulation result
with the experimental data indicating
that the present model can simulate
the effect of the specific gene
knockouts to the changes in the
metabolisms to some extent

´
rez K & Lovley
DR (2008) PLoS
Comput Biol
4, e36.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2789
Table 1. (Continued).
Entry System
Organism,
cells Major findings Model Analysis Software Access Experiment References
59 Central Haemophilus
influenzae
An in silico metabolic network model
of H. influenzae is used to define
proteins predicted to be essential or
non-essential for cell growth.
Comparison of data from in vivo
protein expression with the protein
list associated with a genome-scale
metabolic model showed significant
coverage of the known metabolic
proteome
Stoich FBA NG NG Single time points of
metabolites measured by
assay kits; single time points
of enzyme activities
measured by assay kits

61 Central Homo sapiens,
erythrocytes;
Methylo-
bacterium
extorquens
The principle of flux minimization to
fulfill cellular functions with minimal
effort is introduced. The method
exhibits significant correlations with
flux rates obtained by either kinetic
modeling or direct experimental
determination. Larger deviations
occur for segments of the network
composed of redundant branches
where the flux-minimization method
always attributes the total flux to the
thermodynamically most
favorable branch
Stoich FBA NG BM 70,
JWS
Refers to single time point
of fluxes
Holzhu
¨
tter HG
(2004) Eur
J Biochem 271,
2905–2922.
Systems biology in biochemical research K. Hu
¨

nS
(2010) PLoS ONE
5, e12383.
63 Central Homo sapiens,
neutrophil
granulocytes
The role of the NADPH oxidase in
promoting oscillations was
confirmed. The model predicted an
increase in the amplitude of NADPH
oscillations in the presence of
melatonin, which was confirmed
experimentally
ODE sim, osc
BERKELEY
MADONNA
BM 143 Time series of metabolites
measured by imaging
Olsen LF, Kummer
U, Kindzelskii AL &
Petty HR (2003)
Biophys J 84,
69–81.
64 Central Homo sapiens,
skeletal muscle
The developed model can be applied
to test complex hypotheses
involving dynamic regulation of
cellular metabolism and energetics
in skeletal muscle during

Dash RK, Li Y, Kim
J, Saidel GM &
Cabrera ME (2008)
IEEE Trans Biomed
Eng 55,
1298–1318.
K. Hu
¨
bner et al. Systems biology in biochemical research
FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2791