REVIEW ARTICLE Applications and trends in systems biology in biochemistry Katrin Hubner, Sven Sahle and Ursula Kummer ă 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) 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 technology/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 publications in the field doi:10.1111/j.1742-4658.2011.08217.x 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 ‘systems 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 systems 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 biochemistry, 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 Systems biology in biochemical research K Hubner et al ă 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 applications of systems biology approaches that fit this definition, 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 extremely 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 example cases of applying systems biology approaches combining 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 and metabolic pathways Here, models have to describe explicit biochemical mechanisms of systems and have to relate to quantitative experimental measurements 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 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 not include them here With this scope in mind, we optimized 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 ‘mathematical model’ OR ‘mathematical models’ OR ‘kinetic model’ OR ‘kinetic models’ OR ‘differential equation 2768 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 metabolic OR signal* OR ‘cell cycle’ OR oscillation*) NOT pharmacokinetic* NOT pharmacodynamic* NOT electrophysiolog* NOT ‘molecular modeling’ NOT ‘molecular 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 possible, 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 models, and against older reviews that review the field partially 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 mathematical modeling of metabolism by Gombert and Nielson [17], all articles are summarized extensively in tabular form to allow a quick overview of the published material Table provides information on the studied system, major findings, and employed computational and experimental approaches, as well as the reference itself Figure provides a tree-like view on how the articles are ordered to ease navigation within Table 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 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 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS K Hubner et al ¨ Systems biology in biochemical research me Fig Systematic tree for the navigation of Table Articles are ordered according to the system studied and systems are annotated with gene ontology (GO) numbers 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 60 Articles # publications 50 40 30 20 10 10 20 09 20 08 20 07 20 06 20 05 20 04 20 03 20 02 20 01 20 00 20 Year Fig Number of publications describing systems biology applications in biochemistry per year 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 published before 2000, although very few of these worked directly with quantitative biological data One of the exceptions is the field of calcium signaling, where computational modeling very quickly formed the basis for deciphering the mechanism behind calcium oscillations [18] In addition to the general trend to use systems biology approaches more frequently, there is also an increasing trend in the articles to actually validate the developed models with experimental data This is definitely a positive development because the actual validation 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 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2769 Systems biology in biochemical research K Hubner et al ă 35 Journals 30 # publications 25 20 15 10 g in try e er is on m ne gi he oS lc en PL ic ca ol gi g lo ab io rin et fb ee M in lo ng na l oe ur na bi Jo ur d gy jo an lo y io BS og lb y FE ol na og hn tio ol ec ta bi ot pu al Bi m ic y et co or og oS ol he bi ft PL s lo em na st ur y sy Jo og ar ol ul bi ec s ol em M l st na ur jo C sy al ic ys ph BM o Bi common ones covering the whole period (Fig 3) are Biophysical Journal, Journal of Theoretical Biology, Biotechnology and Bioengineering, FEBS Journal (formerly 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 information 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 additional issue arising with this policy is the fact that 2770 Fig Number of publications describing systems biology applications in biochemistry in the years 2000–2010 in the 10 most often used journals references cited in the supplementary material 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 Placing formulations of models as well as crucial methodology, 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 The organisms studied with systems biology 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 musculus, 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 biotechnologically relevant organisms, such as Lactococcus lactis and Corynebacterium glutamicum, are often investigated Prokaryotic organisms of medical relevance, such as Mycobacterium tuberculosis [25,26] and Heliobacter pylori [27,28] appear twice, with many others only appearing once The biochemical networks that are studied in these prokaryotic organisms have been mostly of metabolic FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ê 2011 FEBS K Hubner et al ă 90 Systems biology in biochemical research ulated kinase, mitogen-activated protein kinase and janus kinase-signal transducer and activator of transcription signaling (Fig 5) There is a clear trend towards eukaryotic and signaling 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 in isolation or, with increasing numbers, in an integrative 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 Thus, the overall picture depicts more specific metabolic 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 approximately 2005 onwards, metabolism has been catching up again (Fig 6) Organisms 80 # publications 70 60 50 40 30 20 10 na ia al th A r ge ni A is ev um la ic X am ut gl C s i ct cus la i L veg or n R li co lus E cu us e m isia M v re ce s S ien ap s H Fig Number of publications describing systems biology applied to the study of specific organisms in biochemistry in the years 2000–2010 nature, reflecting their importance in biotechnology Here, apart from the central energy metabolism including glycolysis (Fig 5), pathways of biotechnological importance such as lysine synthesis [29] in Corynebacterium 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 mammalian 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- 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- 70 Metabolism Signaling 60 # publications 50 40 30 20 10 s si to op Ap T TA -S K) R (E K JA K κB F- te id ac m N AP M B/ o y iu c al I-κ C in Am g er d hy e al sc FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS En bo ar C l e- om t en C en G Fig Number of publications describing systems biology applied to specific biochemical systems in the years 2000–2010 2771 Systems biology in biochemical research K Hubner et al ă 60 50 Metabolism Genome-scale metabolism Signaling Metabolism + signaling # publications 40 30 20 10 10 20 20 20 07 20 20 05 20 20 20 20 20 0 20 10 20 20 08 20 20 20 05 20 20 20 20 20 0 20 Prokaryotes Eukaryotes 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 chemical species other than macromolecules as metabolites), proteins and, to a lesser extent (in the light of the present reviewed systems), RNA and DNA In addition, enzymatic activities and cellular properties such as growth and death rates are measured in a time-dependent manner Only a very few metabolites are measured in vivo (e.g using imaging technologies) Examples that frequently are measured using in vivo methods are calcium (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 discovered 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 expecta2772 Fig Number of publications per year describing signaling, metabolic systems, whole-genome metabolic models or mixed systems in prokaryotic and eukaryotic organisms, respectively tion that many as yet unknown behaviours will be discovered subsequently The in vitro characterization of metabolites after preparing cell extracts is mostly carried out using HPLC or assay kits and, in a few cases, with GC-MS The dominant technology to measure protein concentrations is immunoblotting Approximately 70% of all manuscripts featuring protein concentrations (e.g in the context of signaling) 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 concentrations 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 Enzyme activities are usually measured with 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 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ê 2011 FEBS K Hubner et al ă Systems biology in biochemical research frequently refer to kinetic parameters measured in test tubes using isolated enzymes under highly unphysiological 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 processbased modeling (e.g the entry of a reaction scheme) and translate this reaction scheme into ODEs However, temporal or dynamic models are mainly simulated and analyzed in this mathematical framework All other approaches 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 second 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, corresponding experimental data are still sacrve Second, 250 Modeling methodology # publications 200 150 100 50 t ne rid yb H ri t Pe c gi et ic st oc Lo St om i ch oi E PD St E D O ric Fig Number of publications describing systems biology applied to biochemistry in the years 2000–2010 using a specific computational modeling approach computational methods (e.g for the parametrization of the models) are much less developed than for ODE based models Furthermore, there are fewer userfriendly 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 userfriendly 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 recently linked to a discussion of parameter 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 computation 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 documentation 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 articles 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 software (Fig 8) Additional commercial software packages that are widely used are mathematica (Wolfram Research, Champaign, IL, USA; www.wolfram.com) and, for the set-up and analysis of whole-genome FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2773 Systems biology in biochemical research K Hubner et al ă 120 Software # publications 100 80 60 40 20 ey el a rk onn Be ad m A BR O C Ph m b la n w O i as a ic ep G at m he at M T U PA XP I S PA O C O D N y en LI Si G N at M Fig Number of publications describing systems biology applied to biochemistry in the years 2000–2010 employing the ten most commonly used software tools models, lindo (Lindo Systems Inc., Chicago, IL, USA; www.lindo.com) and simpheny (genomatica, San Diego, CA, USA; www.genomatica.com) In addition, free and specialized software, such as xppaut [42], copasi [43] and gepasi [44], as well as the semiacademic 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 parameters (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 publication 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 existing models, or by merging existing 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 documentation of models (miriam [49], as well as central data resources for the storage of computational models, such as the well curated BioModels database 2774 [12], JWS Online [46], the CellML repository [50] or, for whole-genome scale models, the BIGG database [51]) These approaches will hopefully help to overcome problems of insufficient documentation, at least on the model side On the side of computational methods, 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 computational scientists in the field and published in 2007 [53] However, because of the differring nature of data generation, 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, probabilistic approaches occur at least in 20% of the questionaire 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 investigations at a systems level On the one hand, we were surprised by the fact that only a small fraction of the publications found using the keyword ’systems biology’ actually reflect applications of systems biology approaches to biological systems resulting in new biological insights However, on the other hand, and by restricting ourselves to purely biochemical applications, 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 success and only rarely as a success that results directly in biotechnological or pharmaceutical developments However, this is of course true for most scientific disciplines 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 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 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ê 2011 FEBS K Hubner et al ă When compiling this review, we came across a number of unexpected problems, some of which we have already noted above Missing documentation of computational 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 addition, 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 confusing 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 drylaboratory experiments Therefore, articles should either clearly emply the term ‘computational experiments’ when refering to these or use the more commonly 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 represent 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 modeling with quantitative experimentation, although the majority clearly 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, Systems biology in biochemical research 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 A beautiful study explains the response of yeast to osmotic shock (see entry 382 in Table 1) The control of MAPK signaling has also been predicted and experimentally confirmed (see entry 334 in Table 1) Recently, receptor properties that are crucial for the information processing 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 Arabidopsis thaliana (see entry in Table 1), understanding 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 benefit, namely the increased understanding of 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 systems biology has become a tool for enabling the discoverery of new potential applications, similar to molecular modeling and bioinformatics in the past Finally, we want to stress once more that we have restricted ourselves to biochemical systems and 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 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2775 Systems biology in biochemical research K Hubner et al ă References Kitano H (2002) Systems biology: a brief overview Science 295, 1662–1664 Noble D (2003) The future: putting Humpty-Dumpty together again Biochem Soc Trans 31, 156–158 ´ Mesarovic M (1968) Systems Theory and Biology Springer, New York, NY, pp 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glutamicum and experimental verification J Biosci Bioeng 90, 184–192 Rohwer JM & Botha FC (2001) Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data Biochem J 358, 437–445 Schafer WE, Rohwer JM & Botha FC (2004) A kinetic ă study of sugarcane sucrose synthase Eur J Biochem 271, 3971–3977 FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 326 MAPK (ERK), induced by EGF (EGFR) 325 JAK2-STAT5, induced by Epo (EpoR) 324 JAK2-STAT5, induced by Epo (EpoR) 323 JAK2-STAT5, induced by Epo (EpoR) Entry System Table (Continued) Major findings Model The system acts as an amplifier ODE Homo sapiens, Phoenix-eco with maximum amplification and cells (293T sensitivity for input signals whose embryonic intensity match physiological values for Epo concentration and with duration in kidney cells transformed the range of one to 100 The with adenovirus response of the system reaches saturation for more intense and E1a) longer stimulation with Epo Mus musculus, STAT5 undergoes rapid ODE BaF3-EpoR nucleocytoplasmic cycles, continuously (pre-B cells) coupling receptor activation and target gene transcription, thereby forming a remote sensor between nucleus and receptor Mus musculus, Down-regulation in any of three ODE + red blood cells components of the JAK2-STAT5 delay signaling pathway in red blood cells affects the hematocrit level in an individual considerably It is predicted that exogenous Epo injection may compensate for the effects of the single down-regulation of the Epo hormone level, STAT5 or EpoR/JAK2 expression level, and that it may be insufficient to counterpart a combined down-regulation of all the elements in the JAK2-STAT5 signaling cascade Chlorocebus Positive and negative feedback ODE sabaeus, COS-1 oops and Raf kinase inhibitor (SV-40 protein work together to transformed shape the response pattern kidney and dynamical characteristics of fibroblast-like the ERK pathway cells) Organism, cells sim, fit, sens, osc sim, fit, pident sim, fit ss, sim, mident, fit Analysis NG MATLAB NG LAB SBTOOLBOX, Software Time series of proteins measured by immunoblotting Experiment Time series of proteins measured by immunoblotting Refers to time series of proteins and RNA NG NG Time series of proteins measured by immunoblotting NG MAT-NG Access Shin SY, Rath O, Choo SM, Fee F, McFerran B, Kolch W & Cho KH (2009) J Cell Sci 122, 425435 Swameye I, Muller TG, ă Timmer J, Sandra O & Klingmuller U ă (2003) Proc Natl Acad Sci USA 100, 1028–1033 Lai X, Nikolov S, Wolkenhauer O & Vera J (2009) Comput Biol Chem 33, 312–324 Vera J, Bachmann J, Pfeifer AC, Becker V, Hormiga JA, Darias NVT, Timmer J, Klingmuller U & ă Wolkenhauer O (2008) BMC Syst Biol 2, 38 References K Hubner et al ă Systems biology in biochemical research 2843 2844 330 MAPK (ERK), induced by EGF (EGFR) 329 MAPK (ERK), induced by EGF (EGFR) 328 MAPK (ERK), induced by EGF (EGFR) ODE Concentration of Raf significantly Homo sapiens, HeLa (cervix affects the level of phospho-MEK carcinoma and phospho-ERK upon stimulation (predicted by the model and cells); Chloroce bus sabaeus, confirmed by experiment) COS-7/E3 (kidney fibroblast-like cells) Model 331 MAPK (ERK), induced by EGF (EGFR) Major findings A predicted and experimentally validated ODE model shows that both Raf-1 and, unexpectedly, B-Raf are needed to fully activate ERK in two different cell lines Thus, the formal methodology introduced in the study rationally infers evidentially supported pathway topologies even when a limited number of biochemical and kinetic measurements are available Homo sapiens, Initial velocity of receptor activation as a ODE HeLa (cervix result of ligand binding kinetics rather carcinoma cells) than peak maxima is important for signal propagation and cell fate decision EGF concentration determines how much EGF receptor internalization contributes to the overall signal ODE Homo sapiens, Experimental behaviour could only be HeLa (cervix reproduced with a decisive model carcinoma cells) wherein at least three, and probably more than four, phosphorylation sites decisively suppress the SOS activity Homo sapiens, It is proposed that Shoc2 regulates ODE HeLa (cervix the spatio-temporal patterns of carcinoma cells) the Ras-ERK signaling pathway primarily by accelerating the Ras–Raf interaction Homo sapiens, HEK293 (embryonic kidney cells); Rattus norvegicus, PC-12 (adrenal pheochromocy toma cells) Organism, cells 327 MAPK (ERK), induced by EGF (EGFR) Entry System Table (Continued) sim, fit, sens sim sim, fit, sens ss, sim, fit, sens sim, mident, global sens Analysis GENESIS KINETIKIT, SBW, MATLAB CELLDESIGNER, MATLAB CELLDESIGNER, MATLAB Own Software Time series of proteins measured by immunoblotting; localization of proteins measured by imaging Experiment Time series of proteins measured by immunoblotting and assays Time series of proteins measured by immunoblotting Time series of proteins measured by immunoblotting Time series of proteins measured by immunoblotting and FRET; enzyme activities measured by assay; fluxes measured by fluorescence microscopy NG Access BM 19, JWS, CellML NG NG NG Kamioka Y, Yasuda S, Fujita Y, Aoki K & Matsuda M (2010) J Biol Chem 285, 33540–33548 Matsunaga-Udagawa R, Fujita Y, Yoshiki S, Terai K, Kamioka Y, Kiyokawa E, Yugi K, Aoki K & Matsuda M (2010) J Biol Chem 285, 7818–7826 Fujioka A, Terai K, Itoh RE, Aoki K, Nakamura T, Kuroda S, Nishida E & Matsuda M (2006) J Biol Chem 281, 8917–8926 Schoeberl B, Eichler-Jonsson C, Gilles ED & Muller G ă (2002) Nat Biotechnol 20, 370375 Xu TR, Vyshemirsky V, Gormand A, von Kriegsheim A, Girolami M, Baillie GS, Ketley D, Dunlop AJ, Milligan G, Houslay MD & Kolch W (2010) Sci Signal 3, ra20 References Systems biology in biochemical research K Hubner et al ă FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS Homo sapiens, HMEC 184A1 (breast epithelial cells) Homo sapiens, MCF-10A (breast epithelial cells) NG 333 MAPK (ERK), induced by EGF (EGFR) 334 MAPK (ERK), induced by EGF (EGFR) Organism, cells 332 MAPK (ERK), induced by EGF (EGFR) Entry System Table (Continued) Model EGF induces oscillations between cyto ODE plasmic and nuclear ERK Negative feedback from phosphorylated ERK to the cascade input is necessary to match the robustness of the oscillation characteristics observed over a broad range of ligand concentrations The model-based analysis performed ODE not only identified Raf kinase inhibitor protein as an actual inhibitor of the activation of the ERK pathway, but also suggested the existence of an intense feedback-loop control of the pathway by the activated ERK that maybe responsible for damped oscillations seen in a fraction of activated MEK both in experiments and simulations Collectively, kinases control amplitudes ODE more than duration, whereas phosphatases tend to control both This is illustrated and validated experimentally: (a) a kinase inhibitor affects the amplitude of EGF-induced ERK phosphorylation much more than its duration and (b) a phosphatase inhibitor influences both signal duration and signal amplitude, in particular, long after EGF administration Major findings FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS sim, sens sim, fit, pident, global sens sim, bif, sens, osc Analysis GEPASI NG AUTO MATLAB, Software Experiment NG Time series of proteins measured by immunoblotting and ELISA Time series of proteins measured by immunoblotting NG Time series of proteins measured by imaging and immunoblotting Access BM 84, JWS, CellML Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R & Westerhoff HV (2005) Oncogene 24, 5533–5542 Vera J, Rath O, Balsa-Canto E, Banga JR, Kolch W & Wolkenhauer O (2010) Mol Biosyst 6, 2174–2191 Shankaran H, Ippolito DL, Chrisler WB, Resat H, Bollinger N, Opresko LK & Wiley HS (2009) Mol Syst Biol 5, 332 References K Hubner et al ă Systems biology in biochemical research 2845 2846 338 MAPK (ERK), induced by Epo (EpoR) 337 MAPK (ERK), induced by EGF or HRG or NGF (ErbBs) 336 MAPK (ERK), induced by EGF (EGFR) or NGF (TrkA) 335 MAPK (ERK), induced by EGF (EGFR) or NGF (TrkA) Entry System Table (Continued) Homo sapiens, HEK293 (embryonic kidney cells), HepG2 (hepatocellular carcinoma cells), HeLa (cervix carcinoma cells, IF6 (melanoma cells); Rattus norvegicus, PC-12 cells (adrenal pheochromocy-toma cells) Rattus norvegicus, PC-12 (adrenal pheochromocytoma cells) Homo sapiens, MCF-7 (breast carcinoma cells); Rattus norvegicus, PC-12 (adrenal pheochromocytoma cells) Mus musculus, CFU-E (erythroid progenitor cells) Organism, cells sim Analysis sim, sens ODE Model ODE sim, fit, mident, ED, sens It is shown how a spatially distributed, ODE signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level A distributive ERK phosphorylation mechanism was predicted and experimentally confirmed Increasing one ERK isoform reduces activation of the other isoform through a feedback-mediated process sim, sens, fit It is proposed that the observed ODE quantitative differences in EGF and NGF signaling can be accounted for by differential feedback regulation of the MAPK cascade A model is used and tested to study how stimuli induce different signal patterns and thereby different cellular responses, depending on cell type and the ratio between B-Raf and C-Raf Major findings POTTERSWHEEL MATLAB, MATLAB GEPASI MATLAB Software Experiment Refers to time series of proteins NG Time series of proteins measured by imaging Time series of proteins measured by immunoblotting NG Single time point of proteins measured by immunoblotting Access BM 250 + 251 BM 270 Schilling M, Maiwald T, Hengl S, Winter D, Kreutz C, Kolch W, Lehmann WD, Timmer J & Klingmuller U ă (2009) Mol Syst Biol 5, 334 Nakakuki T, Birtwistle MR, Saeki Y, Yumoto N, Ide K, Nagashima T, Brusch L, Ogunnaike BA, Okada-Hatakeyama M & Kholodenko BN (2010) Cell 141, 884–896 Brightman FA & Fell DA (2000) FEBS Lett 482, 169–174 Robubi A, Mueller T, Fueller J, Hekman M, Rapp UR & Dandekar T (2005) Biol Chem 386, 1165–1171 References Systems biology in biochemical research K Hubner et al ă FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS Drosophila melanogaster, oocytes Organism, cells Saccharomyces cerevisiae 343 MAPK (Fus3, Kss1), induced by pheromone (Ste2) Model Feedback regulation requires that dualspecificity phosphatase mRNA and protein are unstable Activation as a result of ligand binding rather than peak maxima is important for signal propagation and cell fate decision sim, fit sim, fit, mident ss, sim, bif Analysis FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS sim, random sampling of parameters PDE The system can exhibit either graded ODE or ultrasensitive (biphasic) response dynamics based on the binding kinetics of enzymes to the scaffold At the basis of the postulated theory is an analytical result indicating that weak interactions make the response biphasic, whereas tight interactions lead to a graded response ss, sim, bif ODE ODE PDE A single diffusing inhibitor is sufficient to convert a single-peaked extracellular input into a two-peaked (or even more) complex pattern of intracellular signaling Major findings Ligand discrimination by T cells is controlled by the dynamics of competing feedback loops that a high-gain digital amplifier, which is itself modulated during differentiation by alterations in the intracellular concentrations of key enzymes Homo sapiens, Autocrine loops with positive feedback 342 MAPK (ERK), induced A431 allow cells to modulate the amplitude by radiation and (epidermoid and the duration of the signaling carcinoma cells) response to external stimuli TGF-a, (EGFR) Mus musculus, OT-1 (T hybridoma cells) 341 MAPK (ERK), induced by plasmodial myosin heavy chain (TCR) Rattus 340 MAPK norvegicus, (ERK), induced 208F and rat-1 by isopropyl thio-b-D-galacto- (fibroblasts); side Mus musculus, NIH-3T3 (embryonic fibroblasts) 339 MAPK (ERK), induced by gurken (EGFR) Entry System Table (Continued) NG NG MATLAB JDESIGNER, MATLAB FORTRAN, C AUTO, MATLAB, Software Experiment NG NG NG Refers to signal-response curves Refers to time series of proteins and enzyme activities measured by immunoprecipitation, ELISA and assays Single time point and time series of proteins measured by immunoblottiing and flow cytometry NG NG Refers to single time point of proteins measured by immunostaining; gene expression measured by in-situ hybridization Time series of proteins measured by immunoblotting; RNA measured by Nothern blotting and microarrays Access Shvartsman SY, Hagan MP, Yacoub A, Dent P, Wiley HS & Lauffenburger DA (2002) Am J Physiol Cell Physiol 282, C545–C559 Thalhauser CJ & Komarova NL (2010) PLoS ONE 5, e11568 Bluthgen N, Legewie S, ă Kielbasa SM, Schramme A, Tchernitsa O, Keil J, Solf A, Vingron M, Schafer R, Herzel H & ă Sers C (2009) FEBS J 276, 1024–1035 Altan-Bonnet G & Germain RN (2005) PLoS Biol 3, e356 Shvartsman SY, Muratov CB & Lauffenburger DA (2002) Development 129, 2577–2589 References K Hubner et al ă Systems biology in biochemical research 2847 2848 Saccharomyces cerevisiae Saccharomyces cerevisiae 345 MAPK (Fus3, Kss1), induced by pheromone Saccharomyces cerevisiae Saccharomyces cerevisiae 346 MAPK (Fus3, Kss1), induced by pheromone Saccharomyces cerevisiae Organism, cells 344 MAPK (Fus3, Kss1), induced by pheromone Entry System Table (Continued) 347 MAPK (Fus3, Kss1), induced by pheromone 348 MAPK (Hog1, Fus3, Kss1), induced by osmotic stress Model Demonstration that modulation of ODE signal duration increases the range of stimulus concentrations for which dose-dependent responses are possible; this increased dynamic range produces the counterintuitive result of ‘signaling beyond saturation’ in which dose-dependent responses are still possible after apparent saturation of the receptors High intrinsic basal signaling may ODE be a general property of MAPK pathways, allowing rapid and sensitive responses to nvironmental changes Explanation of the phenotype of more ODE than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone MAPKs Fus3, Kss1 and target genes ODE show sustained oscillations in the phos phorylation and activation during continuous pheromone exposure over an 8-h period Oscillations require the negative regulators SST2 and MSG5 and control periodic morphology changes (i.e in cell shape and polarity) ODE + Small changes in protein abundances Stochastic can have large effects on MAPK activation Protein degradation plays an important role in regulating the pheromone pathway Major findings sim sim ss, sim, sens, bif, osc sim, sens, osc sim Analysis NG MATHEMATICA XPPAUT MATLAB, BIONETS, CELLERATOR NG Software Refers to enzyme kinetics and phenotypic behavior Experiment NG NG Time series of proteins measured by immunoblotting; cell number measured by spectrophotometry Time series of proteins measured by immunoblotting; mRNA measured by fluorescence-activated cell sorting Time series of proteins measured by immunoblotting NG NG Time series of proteins measured by immunoblotting; morphology measured by microscopy BM 32, JWS Access Macia J, Regot S, Peeters T, Conde N, ´ Sole R & Posas F (2009) Sci Signal 2, ra13 Behar M, Hao N, Dohlman HG & Elston TC (2008) PLoS Comput Biol 4, e1000197 Wang X, Hao N, Dohlman HG & Elston TC (2006) Biophys J 90, 1961–1978 Hilioti Z, Sabbagh W, Paliwal S, Bergmann A, Goncalves MD, Bardwell L & Levchenko A (2008) Curr Biol 18, 1700–1706 Kofahl B & Klipp E (2004) Yeast 21, 831–850 References Systems biology in biochemical research K Hubner et al ă FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 353 p53 352 NO/cGMP 351 MAPK (Hog1), induced by osmotic stress 350 MAPK (Hog1), induced by osmotic stress 349 MAPK (Hog1), induced by osmotic shock Entry System Table (Continued) Major findings The interaction between the Sho1 and the Sln1 branch is analyzed Simulation results predict that both branches contribute to the overall wild-type response for moderate osmotic shock, while under severe osmotic shock, the cell responds mainly through the Sln1 branch Saccharomyces Computational modeling suggested cerevisiae that a negative-feedback loop operates early in the pathway and leads to rapid attenuation of Hog1 signaling Experimental analysis revealed that the membrane-bound osmosensor Sho1 is phosphorylated by Hog1 and that phosphorylation occurs on Ser-166 Saccharomyces Kinetic analyses of the developed cerevisiae model indicate that budding yeast cells have evolved to protect themselves economically: although they show almost no response to fast pulse-like changes of osmolarity, they respond periodically and are well adapted to osmotic changes with a certain frequency New model integrates various Mammalian, vascular smooth interactions among the components muscle cells of the NO signaling systems and can serve as a general modeling framework for studying NO-mediated vascular smooth muscle cell relaxation under various physiological and pathological conditions Homo sapiens, p53 binds DNA in a H1299 (lung sequence-independent manner It is a carcinoma cells) latent DNA binding protein that must become activated for sequence-specific DNA binding Saccharomyces cerevisiae Organism, cells PDE ODE ODE ODE ODE Model sim sim, fit sim, fit sim, mident sim, fit, sens Analysis MATLAB MATLAB MATLAB NG NG Software Time series of proteins measured by immunoblotting Refers to time series of proteins Experiment NG Single time point of proteins measured by immunoblotting; fluxes measured by fluorescence recovery after photobleaching Refers to single time point and time series of proteins NG NG Refers to time series of proteins NG NG Access Hinow P, Rogers CE, Barbieri CE, Pietenpol JA, Kenworthy AK & DiBenedetto E (2006) Biophys J 91, 330–342 Yang J, Clark JW, Bryan RM & Robertson CS (2005) Am J Physiol Heart Circ Physiol 289, H886–H897 Zi Z, Liebermeister W & Klipp E (2010) PLoS ONE 5, e9522 Hao N, Behar M, Parnell SC, Torres MP, Borchers CH, Elston TC & Dohlman HG (2007) Curr Biol 17, 659–667 Parmar JH, Bhartiya S & Venkatesh KV (2009) Phys Biol 6, 036019 References K Hubner et al ă Systems biology in biochemical research 2849 2850 Organism, cells 359 358 357 356 Model The model suggests the oscillations in ODE p53 activity require both a negative and a postive feedback loop An experiment to test this prediction is suggested Oscillations in both p53 and Mdm2 ODE emerge as stress response, which may allow cells to repair their DNA without consequences of continuous excessive p53 activation Major findings Mus musculus, NIH-3T3 (embryonic fibroblasts); Homo sapiens, MCF-7 (breast carcinoma cells) PIP2, induced Mus musculus, The mathematical model was coherent ODE by EGF (EGFR) NIH-3T3 with the biological response in (embryonic describing the accumulation of diacyl fibroblasts) glycerol caused by PIP2 hydrolysis in cells with EGF treatment PKC, induced Rabbit, Persistent activation of PKC is increased ODE by parallel fibers Purkinje cells if the temporal interval between the and climbing conditioned stimulus (by parallel fibers) fibers (mGluR, and unconditioned stimulus (by climbing AMPA) fibers) is similar to classical conditioning (0.1–3 s) PLC, induced Mus musculus, The model accounts for regulation of ODE by EGF NR6 fibroblasts PIP2 concentration and is sufficiently (EGFR) detailed to explain unique quantitative features of recent experimental data Competitive pathways that deplete PIP2 from the membrane, as well as receptor-mediated enhancement of PIP2 supply, must be significant for agreement between model and experiment Quorum Escherichia coli Results suggest an alternative Petri net sensing glucose-regulated pathways for (stochastic) autoinducer AI-2 synthesis 354 p53, induced by c-irradiation 355 p53, induced by c-irradiation Homo sapiens, cancer cells Entry System Table (Continued) sim, fit sim sim, sens NG NG XPP NG NG sim, osc ss, sens NG Software sim, bif, osc Analysis NG NG NG NG NG NG Access Haugh JM, Wells A & Lauffenburger DA (2000) Biotechnol Bioeng 70, 225–238 Su Y, Lu D, Tan XED, Dong AR, Tian HY, Luo SQ & Deng QK (2006) J Biotechnol 124, 574–591 Kotaleski JH, Lester D & Blackwell KT (2002) Integr Physiol Behav Sci 37, 265–292 Ciliberto A, Novak B & Tyson JJ (2005) Cell Cycle 4, 488–493 Bar-Or RL, Maya R, Segel LA, Alon U, Levine AJ & Oren M (2000) Proc Natl Acad Sci USA 97, 11250–11255 References Time series of cell density Li S, Assmann SM & measured by Albert R (2006) spectrophotomtry; proteins PLoS Biol 4, e312 measured by assay; RNA measured by northern blotting and RT-PCR; proteins measured by SDS/PAGE Time series of proteins measured by ELISA and immunoblotting Refers to time series of proteins and metabolites measured by imaging and different assays Single time point of proteins measured by immunoblotting and thin-layer chromatography Time series of proteins measured by immunoblotting Refers to time series of proteins measured by imaging Experiment Systems biology in biochemical research K Hubner et al ă FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS Major findings Analysis FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS ODE Homo sapiens, HaCaT keratinocytes) 365 Smad, induced by TGF-b (TGF-bR) 364 Smad, induced by TGF-b (ALK1/5) The simulation results indicate that the signal response to TGF-b is regulated by the balance between clathrin dependent endocytosis and non clathrin mediated endocytosis Two-component signal transduction is ODE one of the important mechanisms for bacteria to sense their environment and to respond to altered conditions The present study gives some insights into the dynamics of such systems and can be used as starting conditions for other systems Homo sapiens, Simulations predict that when EGFR is Stochastic HMEC 184A1 activated with TGF-a, receptor (breast epithelial activation is biased toward the cell cells) surface whereas EGF produces a signaling bias toward the endosomal compartment Experiments confirm these predictions Mus musculus, TGF-b signaling is turned off as a result ODE embryonic of a negative feedback loop by Smad7 endothelial cells that also renders the system globally robust 363 Shc/Grb2/SOS, induced by TGF-a or EGF (EGFR) Escherichia coli 362 Sensor (KdpD/KdpE), induced by K+ ss, sim sim, fit, opt, sens sim sim, fit sim, fit, sens, pident, ED ODE ODE sim, fit Model Homo sapiens, NIH-3T3 (embryonic fibroblasts) It is demonstrated that the model can accurately reproduce the experimental observations, can be used to make predictions with accompanying uncer tainties, and can be applied to optimal experimental design Rattus norvegicus, Experimentally determined hepatocytes dose-response data can only be explained by transient RasGAP activation Organism, cells 361 Rho protein (Cdc42), induced by EGF (EGFR) 360 Ras, induced by EGF (EGFR) Entry System Table (Continued) C++, SBML-PET MATLAB Own in NG MATLAB NG DBSOLVE, SCAMP Software Experiment Time series of proteins measured by ELISA Time series of proteins measured by immunoblotting; time series of mRNA measured by northern blotting Refers to time series of proteins measured by immunoblotting Refers to time series of proteins measured by immunoblotting NG NG Time series of proteins measured by immunoblotting NG NG Time series of proteins measured by immunoprecipitation and affinity precipitation Access NG BM 163 Melke P, Jonsson H, ă Pardali E, ten Dijke P & Peterson C (2006) Biophys J 91, 4368–4380 Zi Z & Klipp E (2007) PLoS ONE 2, e936 Resat H, Ewald JA, Dixon DA & Wiley HS (2003) Biophys J 85, 730–743 Markevich NI, Moehren G, Demin OV, Kiyatkin A, Hoek JB & Kholodenko BN (2004) Syst Biol (Stevenage) 1, 104–113 Casey FP, Baird D, Feng Q, Gutenkunst RN, Waterfall JJ, Myers CR, Brown KS, Cerione RA & Sethna JP (2007) IET Syst Biol 1, 190–202 Kremling A, Heermann R, Centler F, Jung K & Gilles ED (2004) BioSystems 78, 2337 References K Hubner et al ă Systems biology in biochemical research 2851 2852 370 Syk activation, induced by IgE-dimer (FceRI) 369 Sonic hedgehog, induced by Shh 368 Smad, induced by TGF-b (TGF-bR) 367 Smad, induced by TGF-b (TGF-bR) 366 Smad, induced by TGF-b (TGF-bR) Entry System Table (Continued) Major findings Smad nucleocytoplasmic shuttling as a dynamic network that flexibly transmits quantitative features of the extracellular TGF- signal, such as its duration and intensity, into the nucleus Homo sapiens, A key quantity that determines HeLa (cervix the type of behavior of the pathway is carcinoma cells); the ratio of the constitutive to the Mus musculus, ligand-induced rate of degradation NIH-3T3 of the receptors (embryonic fibroblasts) Neovison vison, Imbalance in the rates of lung R-Smad phosphorylation and epithelial cells dephosphorylation is likely an important mechanism of Smad nuclear accumulation during TGF-b signaling Mammalian A dynamical model that fits observed data and is robust to perturbations in its parameters The model provides insight into the nature and strength of pathway interactions and suggests directions for future research The roles of the Src kinase Lyn, the Rattus norvegicus, immunoreceptor tyrosine-based RBL-2H3 activation motifs on the b and c subunits of FceRI, and Syk itself (basophilic in the activation of Syk were leukemia cells) investigated Although the b immunoreceptor tyrosine activation motif acts to amplify signaling in experimental systems, there are conditions under which the b immunoreceptor tyrosine activation motif will act as an inhibitor Homo sapiens, HaCaT (keratinocytes) Organism, cells sim, sens sim, fit sim, sens, rob ODE ODE COPASI Software Own MATLAB MATLAB, EXCEL ss, sens, sim NG sim, fit, sens, parameter correlation Analysis ODE ODE ODE Model Experiment Refers to time series of proteins measured by immunoblotting and immunoprecipitation NG NG Single time point and time series of proteins measured by immunoblotting Refers to single time point of proteins BM 112 NG Time series of proteins measured by immunoblotting BM 101, CellML Time series of proteins measured by immunoblotting and imaging Access Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, Wofsy C & Goldstein B (2003) J Immunol 170, 3769–3781 Boykin ER & Ogle WO (2010) Mol Biosyst 6, 1993–2003 Clarke DC, Betterton MD & Liu X (2006) Syst Biol (Stevenage) 153, 412–424 Vilar JMG, Jansen R & Sander C (2006) PLoS Comput Biol 2, e3 Schmierer B, Tournier AL, Bates PA & Hill CS (2008) Proc Natl Acad Sci USA 105, 6608–6613 References Systems biology in biochemical research K Hubner et al ¨ FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS Escherichia coli Rhodobacter sphaeroides 372 Taxis, chemotaxis, induced by chemotactic ligand 373 Taxis, chemotaxis, induced by chemotactic ligand 375 TOR, induced by rapamycin Saccharomyces cerevisiae 374 Taxis, phototaxis, Halobacterium induced by light salinarum Escherichia coli Organism, cells 371 Taxis, chemotaxis, induced by chemotactic ligand Entry System Table (Continued) Model First kinetic model for prokaryotic cells that couples the signal-transduction pathway with the flagellar motor switch Simulations demonstrate that switching occurs through subsequent rate-limiting steps, which are both under sensory control, suggesting that two signals may be involved in halobacterial phototaxis By contrast to the prevailing view of a de novo assembly of PP2As, analysis proposes a specificity factor, based on Tap42p-Tip41p, for PP2As as the key signaling mechanism ODE ODE Dynamic receptor team formation ODE can explain the experimental observations High upstream sensitivity of the signal transduction network is caused by the negative regulation between ligand occupancy of the receptors and kinase activity Qualitative evidence that the dynamic Agent-based range of chemotactic response can be traced to both the heterogeneity of receptor types present, and the modulation of their cooperativity by their methylation state Using an experimental design approach, ODE a model for the chemotaxis pathway could be identified Major findings sim, fit, mident sim, fit sim, fit, mident, ED sim sim Analysis MATLAB PROMOT, DIVA NG CHEMOSCAPE NG Software Refers to single time point and time series of response times Time series of proteins measured by immunoblotting NG Time series of cell rotation and proteins measured by imaging and immunoblotting Refers to chemotactic behavior NG NG Refers to phenotypic behavior Experiment NG Access NG Kuepfer L, Peter M, Sauer U & Stelling J (2007) Nat Biotechnol 25, 1001–1006 Roberts MAJ, August E, Hamadeh A, Maini PK, Mc-Sharry PE, Armitage JP & Papachristodoulou A (2009) BMC Syst Biol 3, 105 Nutsch T, Marwan W, Oesterhelt D & Gilles ED (2003) Genome Res 13, 2406–2412 Miller J, Parker M, Bourret RB & Giddings MC (2010) PLoS ONE 5, e9454 Albert R, Chiu YW & Othmer HG (2004) Biophys J 86, 2650–2659 References K Hubner et al ă Systems biology in biochemical research 2853 2854 Mus musculus, pancreatic b-cells Mus musculus, pancreatic b-cells Saccharomyces cerevisiae 379 Carbohydrate, glycolysis + calcium, induced by glucose Xenopus laevis, oocytes Homo sapiens, HEK293 (embryonic kidney cells) 377 Wnt/b-catenin, induced by Wnt (DSHI) Integrative 378 Carbohydrate, glycolysis + calcium Xenopus laevis, egg extracts Organism, cells 376 Wnt/b-catenin, induced by Wnt Entry System Table (Continued) 380 Carbohydrate, glycolysis + calcium, induced by glucose 381 Carbohydrate, glycolysis + cytoskeleton organization, microtubules Model ODE Different modes of bursting are ODE explained In addition, the model predicts that there is bistability between stationary and oscillatory glycolysis for a range of parameter values Gap-junctional diffusion of calcium is ODE not necessary for islet synchronization, at most supplementing electrical coupling Strong metabolic coupling (e.g through glucose 6-phosphate) can abolish both oscillations and synchrony Polymerization state of the microtubules ODE coherently alters: (a) the glycolytic flux through modulating the flux control between pathways at branch points and (b) the robustness of the metabolic network toward variation The model correctly describes the dependence of calcium oscillation frequency on SERCA density Two scaffold proteins axin and APC pro ODE mote the formation of degradation complexes in very different ways Axin degradation depends on APC, which appears as an essential part of an unappreciated regulatory loop that prevents the accumulation of b-catenin at decreased APC concentrations b-catenin level is almost linearly ODE proportional to the phosphorylation activity of GSK3 Major findings ss, sim, fit, sens, bif ss, sim, bif, osc sim, osc ss, sim sim, MCA sim, MCA Analysis AUTO AUTO 2000 XPPAUT, XPPAUT MATLAB COPASI NG Software Time series of metabolites measured by imaging Dellen BK, Barber MJ, Ristig ML, Hescheler J, Sauer H & Wartenberg M (2005) J Theor Biol 237, 279–290 Bertram R, Satin L, Zhang M, Smolen P & Sherman A (2004) Biophys J 87, 3074–3087 Sun YC (2009) Theor Biol Med Model 6, 13 Lee E, Salic A, Kruger ă R, Heinrich R & Kirschner MW (2003) PLoS Biol 1, E10 References Refers to single time point Aon MA & Cortassa of metabolites measured S (2002) Biophys by spectrophotometry Chem 97, 213–231 and assays; biomass measured by weighting; DNA measured by flow cytometry; enzyme activities measured by spectrophotometry and assays URL CellML Tsaneva-Atanasova K, Zimliki CL, Bertram R & Sherman A (2006) Biophys J 90, 3434–3446 Refers to time series of metabolites measured by imaging; membrane potential measured by patch clamp CellML Refers to time series of metabolites measured by imaging Refers to single time point of proteins NG NG Time series of proteins measured by SDS/PAGE Experiment NG Access Systems biology in biochemical research K Hubner et al ă FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 384 Carbohydrate, glycolysis + PTS sensor system 385 Carbohydrate, glycolysis + PTS sensor system, induced by sucrose 386 Central + calcium, induced by glucose FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS A description of steady-state characteristics is sufficient for describing the signaling properties of the sucrose PTS Experimental validation is included Changes in glucose concentration (or, more generally, glucokinase activity) are sufficient to interconvert the fast and slow Ca2+ modes Escherichia coli Mus musculus, pancreatic b-cells Escherichia coli The steady-state characteristic presented shows a relationship between the growth rate and the output of the sensor system PTS The glycolytic flux that is measured by this sensor is a good indicator for representing the nutritional status of the cell mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate 383 Carbohydrate, Escherichia coli glycolysis + PTS sensor system Major findings A predictive integrative model (metabolism,, gene expression) for the response to osmotic shock is constructed and some conclusions are discussed Organism, cells 382 Carbohydrate, Saccharomyces glycolysis + cerevisiae MAPK (Hog1) + gene expression, induced by osmotic stress Entry System Table (Continued) ss, sim sim, osc ODE sim, sens sim, sens, fit ss, sim, fit, sens Analysis ODE ODE ODE ODE Model NG NG CADLIVE MATLAB MATHEMATICA Software NG NG NG NG Time series of metabolites measured by imaging Time series of metabolites measured by assay kits Time series of metabolite concentrations measured by ELISA Nunemaker CS, Bertram R, Sherman A, Tsaneva- Atanasova K, Daniel CR & Satin LS (2006) Biophys J 91, 2082–2096 Nishio Y, Usuda Y, Matsui K & Kurata H (2008) Mol Syst Biol 4, 160 Sauter T & Gilles ED (2004) J Biotechnol 110, 181–199 Time series of RNA Klipp E, Nordlander B, Kruger R, Gennemark measured by northern ă P & Hohmann S blotting; enzyme activities measured (2005) Nat by assay kits; Biotechnol 23, metabolites measured 975–982 by enzyme assays; proteins measured by immunoblotting Refers to time series of Kremling A, metabolites Bettenbrock K & Gilles ED (2007) BMC systems biology 1, 42 NG References Experiment Access K Hubner et al ¨ Systems biology in biochemical research 2855 2856 Mus musculus, pancreatic b-cells Organism, cells NG Escherichia coli 389 IP3 + calcium, induced by glutamate (mGluR) 390 Lactose + regulation of lac operon 388 Central + carbon Escherichia coli source sensing 387 Central + calcium, induced by glucose Entry System Table (Continued) Extension of a previous model to include a more detailed description of mitochondrial metabolism The model can account fast (period < min) and slow (period 2–7 min) oscillations in insulin and Ca2+ concentrations Oscillations in Ca2+, mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization These data are compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity It is shown that the adaptations are enabled by an indirect recognition of carbon sources through a mechanism that is termed distributed sensing of intracellular metabolic fluxes This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors Ins(1,3,4,5)P-4 was found to be an important modulator of the temporal dynamics of other inositol phosphates and to allow for paired pulse facilitation of calcium release In a model for the regulation of induction in the lactose operon, it was found that, for physiologically realistic values of the external lactose and the bacterial growth rate, bistable steady states may exist Major findings ODE + delay ODE ODE ODE Model sim, fit ss, sim, sens, osc sim sim, osc Analysis MATLAB GENESIS KINETIKIT, MATLAB XPPAUT Software Experiment Yildirim N & Mackey MC (2003) Biophys J 84, 2841–2851 BM 65 Refers to time series of enzyme activity Mishra J & Bhalla US (2002) Biophys J 83, 1298–1316 Kotte O, Zaugg JB & Heinemann M (2010) Mol Syst Biol 6, 355 Bertram R, Satin LS, Pedersen MG, Luciani DS & Sherman A (2007) Biophys J 92, 1544–1555 References DOQCS 31 + Refers to time series 32 of metabolites measured by imaging Refers to single time point of metabolites CellML BM 244 Time series of metabolites measured by imaging Access Systems biology in biochemical research K Hubner et al ¨ FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS NG Escherichia coli 392 Lipid, carnitine biosynthesis + cAMP, induced by phosphorylated enzyme IIA Organism, cells 391 Lipid + cytoskeletal organization, actin assembly Entry System Table (Continued) ODE Model Some of the most relevant conclusions ODE obtained are: (a) the regulatory interactions among glucose, glycerol and cAMP metabolism are far stronger than those present in the L-carnitine transport, production and degradation processes; (b) carnitine biosynthesis is very sensitive to the cAMP signaling system because it reacts at very low cAMP receptor concentrations; and (c) ATP is a critical factor in the transient dynamics All these model-derived observations have been experimentally confirmed by separate studies Lipids and ATP influence the dynamic interconversion between active and inactive actin nucleation sites Major findings sim, fit, pident, sens sim, fit, sens Analysis MATLAB COPASI Software Experiment NG NG References Kuhnel M, Mayorga ă LS, Dandekar T, Thakar J, Schwarz R, Anes E, Griffiths G & Reich J (2008) BMC Syst Biol 2, 107 Time series of metabolites Hormiga J, ´ ´ measured by assay Gonzalez-Alcon C, ´ kits; time series of enzyme Sevilla A, Canovas activities measured by M & Torres NV assay kits (2010) Mol Biosyst 6, 699–710 Time series of metabolites measured by thin-layer chromatography Access K Hubner et al ă Systems biology in biochemical research FEBS Journal 278 (2011) 2767–2857 ª 2011 The Authors Journal compilation ª 2011 FEBS 2857 ... describing systems biology applied to biochemistry in the years 2000–2010 employing the ten most commonly used software tools models, lindo (Lindo Systems Inc., Chicago, IL, USA; www.lindo.com) and. .. found using the keyword ? ?systems biology? ?? actually reflect applications of systems biology approaches to biological systems resulting in new biological insights However, on the other hand, and by... (see entry in Table 1), understanding 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