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Applications and trends in systems biology inbiochemistry Katrin Hu¨bner, Sven Sahle and Ursula Kummer Department of Modeling of Biological Processes, COS Heidelberg/BioQuant, University

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,

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 biology approaches that have been employed to shed light on both biochemical functions and previously unknown mechanisms We point out which computational and experimental methods are employed most frequently and which trends in systems biology research can be observed Finally, we discuss some problems that we have encountered in publica- tions in the field.

Introduction

One of the fastest growing fields in the life sciences is

systems biology PubMed lists more than 3000 articles

which, in one way or the other, use this term in their

title or abstract during the last decade (precisely, the

last 11 years, including the year 2000) compared to a

mere three articles in the preceding century Obviously,

this is partially a result of the fact that the term

‘sys-tems biology’ had not been used during that time.

However, as we will see in the present review, also

with respect to research that would now be called

sys-tems biology, there is clearly significantly less to report

before the year 2000 Interestingly, looking closely at

the more than 3000 articles using the term ‘systems

biology’, it becomes apparent that approximately half

of them describe methodological work either on the

computational or the experimental side, and more than

one-third are classified as reviews However, only a

handful of the latter represent reviews that actually review a set of articles Most of the articles classified

as reviews could rather be classified as news and views Another large portion of articles uses the term ‘systems biology’ in a different sense than we would understand

it (e.g stating that they are investigating a biological 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

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

seen below.

However, to identify articles encompassing

applica-tions of systems biology approaches that fit this

defini-tion, we note that, on the one hand, it is completely

insufficient to search for articles that explicitely state the

term ‘systems biology’ On the other hand, it is

extre-mely difficult to define good keywords for a search in

PubMed because the term ‘model’, as well as similar

terms, are used in many different contexts and it is very

cumbersome to find relevant work in the multitude of

articles that are available with obvious keywords.

Therefore, we first defined the scope of the articles

that we would like to review These have to fit the

above definition in the sense that they represent

exam-ple cases of applying systems biology approaches

com-bining experimental investigation and computational

modeling (subsequent to the year 2000) In addition,

fitting our own expertise and the scope of the FEBS

Journal, we restrict ourselves to typical intracellular

biochemical systems These include signaling systems

describe explicit biochemical mechanisms of systems

and have to relate to quantitative experimental

mea-surements of systems behaviour appearing in the same

article or in previous publications Correspondingly,

purely experimental findings have to directly relate to

previous computational models.

We do not focus on cell biological, biomechanical or

higher level descriptions of multicellular systems in the

present review Finally, the systems biology of the cell

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

‘mathe-matical model’ OR ‘mathe‘mathe-matical 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

‘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 missed one or more articles However, checking against, for example, the BioModels database [12], which contains a curated collection of biological mod- els, and against older reviews that review the field par- tially and from a different viewpoint [13–16], we estimate that we cover at least a representative 80–90% of those articles in the field that fit the above requirements Thus, we offer a good picture of the field with respect to the last decade.

Similar to the highly informative review about ematical modeling of metabolism by Gombert and Nielson [17], all articles are summarized extensively in tabular form to allow a quick overview of the pub- lished material Table 1 provides information on the studied system, major findings, and employed compu- tational and experimental approaches, as well as the reference itself Figure 1 provides a tree-like view on how the articles are ordered to ease navigation within Table 1 itself The ordering is by systems because many scientists will be interested in a specific system, even across species boundaries The large number of articles reviewed prohibits a detailed referencing in the text when discussing general trends For recapitulating these trends, we would make reference to Table 1.

math-General developments There is a clear increase in publications that employ systems biology approaches to tackle open biochemical questions Because we focused on original work, rather than on any articles just mentioning systems biology, this fact is not blurred by the vastly increasing number

of news and views, articles and minireviews, and so

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 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.

biol-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

Fig 2 Number of publications describing systems biology

applica-tions in biochemistry per year

common ones covering the whole period (Fig 3) are

Biophysical Journal, Journal of Theoretical Biology,

Bio-technology and Bioengineering, FEBS Journal (formerly

European Journal of Biochemistry), Journal of Biological

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 addiinforma-tional 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 h-indices, etc The latter was confirmed by us by testing different examples from several journals Plac- ing formulations of models as well as crucial method- ology, both on the experimental and computational sides, into the supplementary material then implies a strong and systematic disadvantage for the careers of young scientists working in these fields.

Systems studied

approaches in the last decade are by a large extend eukaryotic and only to a lesser extent prokaryotic (Fig 4) Among the first, classical scientific model organisms such as Saccharomyces cerevisiae, Mus mus- culus, Rattus norvegicus and, for obvious reasons, Homo sapiensare dominant However, studies also include the parasite Trypanosoma brucei [19,20] or the biotechnologically relevant Aspergillus niger [21–24] Again, the prokaryotic key players are typical model organisms, such as Eschericia coli, although biotechno- logically relevant organisms, such as Lactococcus lactis and Corynebacterium glutamicum, are often investi- gated Prokaryotic organisms of medical relevance, such as Mycobacterium tuberculosis [25,26] and Heliob-

only appearing once.

The biochemical networks that are studied in these prokaryotic organisms have been mostly of metabolic

nal of theoretical biology

ing

FEBS jour

nal

Journal of biological chemistr

y

Metabolic engineer

ingPLoS one

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

cane, xanthan biosynthesis in Xanthomonas campestris

[33] and citrate metabolism in fruit [34] have been

studied.

By contrast, most studies on eukaryotic (e.g

mam-malian and especially human) cells focus on signaling

systems, which reflects the importance of these systems

in the context of cancer research Dominant examples

are calcium, nuclear factor jB, extracellular

signal-reg-ulated kinase, mitogen-activated protein kinase and janus kinase-signal transducer and activator of tran- scription signaling (Fig 5).

There is a clear trend towards eukaryotic and ing systems over the years, which coincides with the above observation that basic medical science has discovered systems biology later than the engineering field, in which metabolic engineering has been one of the forerunners Signaling pathways are either studied

signal-in isolation or, with signal-increassignal-ing numbers, signal-in an signal- tive way, encompassing several pathways and their cross-talk Unexpectedly, only few articles feature a combination of signaling and metabolic networks However, these are also increasing slowly.

integra-Thus, the overall picture depicts more specific bolic systems studied in the beginning of the decade, often published in biotechnology/engineering journals Later, signaling systems became slighty prevalent, reflecting systems of medical relevance in eukaryotic cells Finally, with the whole genome-based metabolic models becoming more approachable from approxi- mately 2005 onwards, metabolism has been catching

meta-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

alCarbohydrate

B/NF-κB

ATApoptosis

Metabolism Signaling

Fig 5 Number of publications describing

systems biology applied to specific

bio-chemical systems in the years 2000–2010

Fig 4 Number of publications describing systems biology applied

to the study of specific organisms in biochemistry in the years

2000–2010

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

new methods for detecting metabolites in vivo, such as

the development of nanosensors [40], with the

expecta-tion that many as yet unknown behaviours will be discovered subsequently.

The in vitro characterization of metabolites after paring cell extracts is mostly carried out using HPLC

pre-or assay kits and, in a few cases, with GC-MS.

The dominant technology to measure protein centrations is immunoblotting Approximately 70% of all manuscripts featuring protein concentrations (e.g.

con-in the context of signalcon-ing) use this method, which again requires cells to be killed and their contents extracted Therefore, it is quite unexpected that live cell imaging methods for proteins (e.g using green fluorescent protein-tagged antibodies) are also still only rarely used in systems biology studies.

Obviously, live cell imaging on the one hand is also hampered by several problems (e.g the need to follow many cells to be able to judge cell–cell variation, signal

to noise ratios with proteins or metabolites of low centrations and the autofluorescence of some cell types) On the other hand, in vitro measurements are limited by the above mentioned facts, such as low time resolution and experimental errors and, in addition, these methods are often so laborous and expensive that they are only performed in up to three replicas with computed standard deviations that have dubious statistical meaning Often, replicas are purely technical and not biological replicas.

standard kits If these are measured in cell extracts or

valuable source for the modeling However, studies

Fig 6 Number of publications per yeardescribing signaling, metabolic systems,whole-genome metabolic models or mixedsystems in prokaryotic and eukaryoticorganisms, respectively

frequently refer to kinetic parameters measured in test

tubes using isolated enzymes under highly

unphysio-logical conditions as the basis for an initial parameter

guess, although these often have been shown to be far

away from actual in vivo parameters [41].

Computational approaches

Studying the computational approaches used in the

systems biology of cellular biochemistry, it is highly

obvious that the formalism of ordinary differential

equations (ODE) is the dominating approach (Fig 7).

This does not necessarily mean that the scientist

actually set up ODEs by him/herself because several

software tools used in systems biology allow a

process-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).

The computational tasks applied on the temporal or dynamic models are mostly simulations, the fitting of model parameters to experimental data and the computation of sensitivities to detect dependencies in the model Here, parameter estimation is rarely and only

identifiability, which appears to enter the field only now This certainly should have more impact in the future Very often, the exact methodology by which these computations are carried out is not documented in the articles We find it utterly unexpected that, overall, it

is only a minority of articles that properly describe (in a reproducible way) the computational research performed in the study Thus, very often, neither the exact numerical algorithm used to simulate a specific behaviour, nor the software with which the computa- tion was performed, are given and referenced This has somewhat improved over the course of the decade, although it appears that there is a lack of awareness of the fact that a documentation of the computational approaches is scientifically as important as the docu- mentation of the experimental data, which are never missing This problem is increased by the trend (as noted above) of some journals to put crucial (e.g methodological) information, and sometimes even the whole description of the computational model, into the supplementary material Once again, this renders arti- cles incomprehensible without reading the supplement and puts those scientists who are working on new methods and tools into the unfortunate situation that 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

packages that are widely used are mathematica fram Research, Champaign, IL, USA; www.wolfram.- com) and, for the set-up and analysis of whole-genome

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

models, lindo (Lindo Systems Inc., Chicago, IL,

USA; www.lindo.com) and simpheny (genomatica,

San Diego, CA, USA; www.genomatica.com) In

addi-tion, free and specialized software, such as xppaut

[42], copasi [43] and gepasi [44], as well as the

semi-academic software berkeley madonna [45], are being

used more and more often.

The above observation about poorly documented

computational methodology unfortunately also applies

to models themselves Thus, often important

parame-ters (e.g initial values) are missing and sometimes

incomplete equations are given Here, it should be

mentioned that a very few journals (e.g FEBS Journal)

actually employ curation of models submitted for

pub-lication via usage of JWS Online [46], which helps to

avoid these problems.

Two trends within the last few years are positive

and interesting First, slowly, more and more models

receive proper validation within the study This means

that the model is not only used to reproduce data

(often after parameter fitting), but also is actually used

for independent predictions of observable behaviour,

which is then experimentally verified and thus the

model is validated The second trend is the re-use of

models Thus, more and more studies rely on previous

modeling work, either by extending or modifying

exist-ing models, or by mergexist-ing existexist-ing models with each

other or with new models This trend is supported by

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 ods, there is currently a similar community effort that creates a standard for minimal information called MIASE [52].

meth-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 The last decade has seen a strong increase in research carrying the label systems biology, which combines computational and quantitative experimental investiga- tions at a systems level On the one hand, we were sur- prised by the fact that only a small fraction of the publications found using the keyword ’systems biology’

approaches to biological systems resulting in new logical insights However, on the other hand, and by restricting ourselves to purely biochemical applications,

bio-we identified almost 400 publications that represent successful applications of systems biology, and the numbers are clearly on the rise The success of these applications is obviously often visible as a scientific suc- cess and only rarely as a success that results directly in

However, this is of course true for most scientific plines Stating that these are successful applications does not imply that all of the cited articles are very strong cases; many are and some are not.

disci-However, our aim is to give a comprehensive and representative overview of systems biology research, its trends and the commonly used computational, as well

as experimental, methodologies Therefore, we decided not to focus on just a few articles but, rather, to try to gather a complete as possible set of publications.

AUT

MathematicaGepasi Own COBRA Ber

keleymadonna

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

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-tion, terminologies in such an interdisciplinary field

have to be chosen with care To exemplify this point,

in many publications, the term ‘experiment’ is used for

a computational experiment (e.g a simulation) This is

quite normal in theoretical or mathematical literature.

However, in the context of systems biology, this is

con-fusing because it is sometimes not so easy to judge, if the

word experiment’, without reference to computations

(e.g not using the more explicit term ‘computational

experiment’), actually refers to wet-laboratory or

either clearly emply the term ‘computational

experi-ments’ when refering to these or use the more

com-monly used terminology (e.g ‘simulations’) Another

confusing term is ‘prediction’ because some articles use

this word to indicate that their model fits experimental

data (after parameter fitting), whereas, usually, the

term is needed to state that the model actually predicts

experimental behaviour to which it has not been fitted

in the first place It is sometimes almost impossible to

tell the difference, if it is not clearly indicated which

data have been used for fitting and which have been

used for model validation.

We would like to pick up a question raised at the

beginning of this review: does systems biology

repre-sent an approach that offers anything beyond the

existing purely experimental approaches? Reading the

approximately 400 articles featured in this review, we

would answer with a clear ’yes’ This does not mean

that all studies published have gained many new

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 (see entry 382 in Table 1) The control of MAPK sig- naling has also been predicted and experimentally con- firmed (see entry 334 in Table 1) Recently, receptor properties that are crucial for the information process- ing within erythropoietin signaling are also identified (see entry 259 in Table 1).

On the metabolic side, exciting examples of integrated systems biology approaches are the identification of key players in the branched amino acid metabolism in Ara-

the metabolism of tobacco grown on media containing different cytokines (see entry 176 in Table 1) and the investigation of substrate channeling in the urea cycle (see entry 191 in Table 1).

However, and apart from this more basic scientific

complex mechanisms, there are also very applied examples of research benefitting from systems biology Thus, systems biology has been used for the prediction

of drug targets (e.g see entries 84, 104 and 197 in Table 1) and for biotechnological engineering (e.g see entries 14, 16, 36 and 392 in Table 1) Obviously, most

of these have not entered industrial production yet (more time is needed for that) but it is clear that sys- tems biology has become a tool for enabling the dis- coverery of new potential applications, similar to molecular modeling and bioinformatics in the past Finally, we want to stress once more that we have

excluded systems of cell cycle and circadian rhythms 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.

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