From Enzymology to Systems Biology and Back

About twenty years ago, systems biology was far away from modeling large metabolic networks due to insufficient computational power to go beyond modeling the reaction kinetics of individual enzymes.
Today, after the enormous increases in the speed of computation and growth in data storage capabilities, systems biology is approaching its goal to be able to investigate complex biological systems be it individual cells, tissues, organs or even whole organisms. Furthermore, in systems biology, modeling of pathways not only includes single enzyme reaction kinetics but also higher-level controlling processes such as gene regulation and signal transduction making the in-silico reconstruction of such heterogeneous networks significantly more complicated.

However, these network studies strongly depend on the availability of interpretable and reliable data from mechanistic studies of enzyme activities, insights in gene regulation and from investigations of inter- and intracellular signaling. Systematic metabolic pathway studies require a well-founded mechanistic understanding supported by experimental data on single enzymes. The lack of this data has meant that researchers have often moved their focus to the study of the metabolom and reaction products. Studies from metabolic flux analysis have shown that calculated flux rates using metabolic models are relatively insensitive to large errors in kinetic parameters for most enzymes [1], thus justifying the use of metabolomics until better data becomes available. In addition, since large amounts of kinetic data are stored in databases such as BRENDA or SABIO-RK, applying mathematical quantification methods (e.g. as described by Liebermeister and Klipp [2,3]) to the value distributions of these parameters for particular reactions and specific organisms, allows determination of a set of data which can be used in dynamic pathway simulations.

Whilst modelling methods, such as those described above, are able to give very useful insights and be used to make predictions, they are nevertheless, not good enough to avoid the necessity to produce more accurate and comprehensive experimental data [4]. For example, time-resolved measurements allowing insights in the mechanistic activities of individual enzymes during the catalysis of chemical reactions lead to an increased understanding of correlations between conformational changes and chemical processes in temporal order which is extremely important for further progress in developing models and understanding events on single site in the protein.


Furthermore, additional experimental data with increased depth and accuracy will help to address current questions in health care and biotechnology. Developments in industrial biotechnology have shown that the design and redesign of enzyme catalysts requires sound modeling based on reliable structural-mechanistic knowledge.

The ESCEC Symposia embrace structural, computational and biological disciplines, and bring researchers (established and younger workers) together to discuss the limits and challenges of systems biology, considering where and when the mechanistic view should be favoured over the holistic one and how this discipline is making new and valuable discoveries.
This conference series also provides a platform to discuss standards in biochemistry in general and the STRENDA Guidelines in particular, aiming to improve the quality of data reporting in the scientific literature. All participants are invited to discuss latest results, approaches and methodologies in experimental, theoretic and bioinformatics enzymology.





[1] Cornish-Bowden, A. and Hofmeyr, J.-H.S. (2005) Kinetic characterization of enzymes for sytstems biology. The Biochemist, pp 11-14.

[2] Liebermeister, W. and Klipp, E. (2006) Bringing metabolic networks to life: integration of kinetic, metabolic and proteomic data. Theor. Biol. Med. Mode 3:42.

[3] Liebermeister, W. (2008) Validity and combination of biochemical models. In: Proceedings of the 3rd Beilstein ESCEC Symposium (Eds. Hicks, M.G. and Kettner, C.). Logos-Verlag, Berlin, pp. 163 – 179.

[4] Cvijovic, M., Almquiest, J., Hagmar, J., Hohmann, S., Kaltenbach, H.-M., Klipp, E., Krantz, M.-, Mendes, P., Nelander, S., et al. (2014) Bridging the gaps in systems biology. Mol. Genet. Genomics 289(5):727-34.