A.K. Smilde, M.E. Timmerman, M.M.W.B. Hendriks, J.J. Jansen, H.C. Hoefsloot
In functional genomics it is more rule than exception that experimental designs are used to generate the data.The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites.
D.J. Vis, J.A. Westerhuis, H.C.J. Hoefsloot, F. Roelfsema, M.M.W.B. Hendriks, A.K. Smilde
The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods.
D. Hasdemir, G.J. Smits, J.A. Westerhuis, A.K. Smilde
With the increasing amount and complexity of data generated in biological experiments it is becoming necessary to enhance the performance and applicability of existing statistical data analysis methods. This enhancement is needed for the hidden biological information to be better resolved and better interpreted. Towards that aim, systematic incorporation of prior information in biological data analysis has been a challenging problem for systems biology.
J.J. Jansen, E. Szymańska, H.C. Hoefsloot, A.K. Smilde
Many metabolomics studies aim to find 'biomarkers': sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment.
D.M. Hendrickx, H.C.J. Hoefsloot, M.M.W.B. Hendriks, D.J. Vis, A.B. Canelas, B. Teusink, A.K. Smilde
Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms.
D.M. Hendrickx, H.C.J. Hoefsloot, M.M.W.B. Hendriks, A.B. Canelas, A.K. Smilde
In many metabolomics applications there is a need to compare metabolite levels between different conditions, e.g., case versus control. There exist many statistical methods to perform such comparisons but only few of these explicitly take into account the fact that metabolites are connected in pathways or modules. Such a priori information on pathway structure can alleviate problems in, e.g., testing on individual metabolite level.
The Netherlands Metabolomics Centre focusses on the development of metabolomics based technologies and instrumentation to address the current and future challenges in biology, biotechnology and biomedical research in order to improve personalised health and quality of life.