Abstract
Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbott A (2010) Germans cook up liver project. Nature 468:879
Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8(5):e1002518
Alberghina L, Cirulli C (2010) Proteomics and systems biology to tackle biological complexity: yeast as a case study. Proteomics 24:4337–4341
Barberis M, Klipp E, Vanoni M, Alberghina L (2007) Cell size at S phase initiation: an emergent property of the G1/S network. PLoS Comput Biol 3(4):e64
Bar-Shalom Y, Rong Li X, Kirubarajan T (2001) Estimation with applications to tracking and navigation. Wiley-Interscience, Hoboken
Barth T, Chan T, Haimes R (2001) Multiscale and multiresolution methods: theory and applications. Springer, New York
Bassingthwaighte J, Hunter P, Noble D (2009) The cardiac physiome: perspectives for the future. Exp Physiol 94:597–605
Chang A, Scheer M, Grote A, Schomburg I, Schomburg D (2008) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucl Acids Res 37:D588–D592
Cotterell J, Sharp J (2010) An atlas of gene regulatory networks reveals multiple three-gene mechanisms for interpreting morphogen gradients. Mol Syst Biol 6:425
Cvijovic M, Olivares-Hernández R, Agren R, Dahr N, Vongsangnak W, Nookaew I, Patil KR, Nielsen J (2010) BioMet toolbox: genome-wide analysis metabolism. Nucl Acid Res 38:W144–W149
Erjavec N, Cvijovic M, Klipp E, Nyström T (2008) Selective benefits of damage partitioning in unicellular systems and its effects on aging. Proc Natl Acad Sci USA 105(48):18764–18769
Flöttmann M, Schaber J, Hoops S, Klipp E, Mendes P (2008) ModelMage: a tool for automatic model generation, selection and management. Genome Inf 20:52–63
Hucka M, Finney A, Sauro H, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmey JH, Hunter PJ et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531
Jansson A, Jirstrand M (2010) Biochemical modeling with systems biology graphical notation. Drug Discov Today 15(9–10):365–370
Jazwinski AH (1970) Stochastic processes and filtering theory. Academic Press, New York
Jol SJ, Kümmel A, Terzer M, Stelling J, Heinemann M (2012) System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes. PLoS Comput Biol 8(3):e1002415
Jörnsten R, Abenius T, Kling T, Schmidt L, Johansson E, Nordling TE, Nordlander B, Sander C, Gennemark P, Funa K, Nilsson B, Lindahl L, Nelander S (2011) Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Mol Syst Biol 7:486
Klipp E, Nordlander B, Krüger R, Gennemark P, Hohmann S (2005) Integrative model of the response of yeast to osmotic shock. Nat Biotech 23(8):975–982
Kloeden PE, Platen E (1992) Numerical solution of stochastic differential equations. Springer-Verlag, New York
Kotte O, Zaugg JB, Heinemann M (2010) Bacterial adaptation through distributed sensing of metabolic fluxes. Mol Syst Biol 6:355
Krantz M, Ahmadpour D, Ottosson LG, Warringer J, Waltermann C, Nordlander B, Klipp E, Blomberg A, Hohmann S, Kitano H (2009) Robustness and fragility in the yeast high osmolarity glycerol (HOG) signal-transduction pathway. Mol Syst Biol 5:281
Krause F, Uhlendorf J, Lubitz T, Schulz M, Klipp E, Liebermeister W (2010) Annotation and merging of SBML models with semanticSBML. Bioinformatics 26:421–422
Kuepfer L, Peter M, Sauer U, Stelling J (2007) Ensemble modeling for analysis of cell signaling dynamics. Nat Biotech 25(9):1001–1006
Kümmel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2(2006):0034
Le Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B, Snoep J, Hucka M (2006) BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucl Acid Res 34:D689–D691
Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M et al (2009) The systems biology graphical notation. Nat Biotech 27(8):735–741
Liebermeister W (2008) Validity and combination of biochemical models. In Proceedings of 3rd International ESCEC workshop on experimental standard conditions on enzyme characterizations, Beilstein Institute, Frankfurt
Liebermeister W, Klipp E (2006) Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data. Theor Biol Med Mode 3:42
Lloyd C, Lawson J, Hunter P, Nielsen P (2008) The CellML model repository. Bioinformatics 24:2122–2123
Lubitz T, Schulz M, Klipp E, Liebermeister W (2010) Parameter balancing in kinetic models of cell metabolism. J Phys Chem B 114(49):16298–16303
Mišković L, Hatzimanikatis V (2011) Modeling of uncertainties in biochemical reactions. Biotechnol Bioeng. 108(2):413–423
Nelander S, Wang W, Nilsson B, She QB, Pratilas C, Rosen N, Gennemark P, Sander C (2008) Models from experiments: combinatorial drug perturbations of cancer cells. Mol Syst Biol 4:216
Novak B, Tyson JJ, Gyorffy B, Csikasz-Nagy A (2007) Irreversible cell-cycle transitions are due to systems-level feedback. Nat Cell Biol 9(7):724–728
Olivier B, Snoep J (2004) Web-based kinetic modelling using JWS online. Bioinformatics 20:2144
Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Holloway E, Kurbatova N, Lukk M, Malone J, Mani R, Pilicheva E, Rustici G, Sharma A, Williams E, Adamusiak T, Brandizi M et al (2010) ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucl Acid Res 39:D1002–D1004
Resat H, Petzold L, Pettigrew M (2009) Kinetic modeling of biological systems. Methods Mol Biol 541:311–335
Ristic B, Arulampalam S, Gordon Neil (2004) Beyond the Kalman filter: particle filters for tracking applications. Artech Print on Demand
Schaber J, Liebermeister W, Klipp E (2009) Nested uncertainties in biochemical models. IET Syst Biol 3(1):1–9
Schulz M, Uhlendorf J, Klipp E, Liebermeister W (2006) SBMLmerge, a system for combining biochemical network models. Genome Inf 17:62–71
Smallbone K, Simeonidis E, Swainston N, Mendes P (2010) Towards a genome-scale kinetic model of cellular metabolism. BMC Syst Biol 28(4):6
Smith L, Hucka M (2010) SBML level 3 hierarchical model composition, COMBINE 2010, informatics forum, University of Edinburgh, 09 Oct 2010
Southerna J, Pitt-Francisb J, Whiteley J, Stokeley D, Kobashid H, Nobes R, Kadookad Y, Gavaghan D (2008) Multi-scale computational modelling in biology and physiology. Prog Biophys Mol Biol 96:60–89
Stein L (2001) Genome annotation: from sequence to biology. Nat Rev Genet 2:493–501
Taylor CF, Field D, Sansone SA, Aerts J, Apweiler R, Ashburner M, Ball CA, Binz PA, Bogue M, Booth T, Brazma A, Brinkman RR, Michael Clark A, Deutsch EW, Fiehn O, Fostel J, Ghazal P, Gibson F, Gray T, Grimes G et al (2009) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotech 26(8):889–896
Tiger CF, Krause F, Cedersund G, Palmér R, Klipp E, Hohmann S, Kitano H, Krantz M (2012) A framework for mapping, visualisation and automatic model creation of signal-transduction networks. Mol Syst Biol 24(8):578
Wang L, Birol I, Hatzimanikatis V (2004) Metabolic control analysis under uncertainty: framework development and case studies. Biophys J 87(6):3750–3763
Wingreen N, Botstein D (2006) Back to the future: education for systems-level biologists. Nat Rev Mol Cell Biol 7(11):829–832
Wittig U, Golebiewski M, Kania R, Krebs O, Mir S, Weidemann A, Anstein S, Saric J, Rojas I (2006) SABIO-RK: integration and curation of reaction kinetics data. Bioinformatics 4075:94–103 (Lecture notes)
Acknowledgments
This work was supported by the European Commission (Contract No. 223137, FutureSysBio).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by J. Graw.
Rights and permissions
About this article
Cite this article
Cvijovic, M., Almquist, J., Hagmar, J. et al. Bridging the gaps in systems biology. Mol Genet Genomics 289, 727–734 (2014). https://doi.org/10.1007/s00438-014-0843-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00438-014-0843-3