Home
ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) is a modeling approach to simulate the progression of metabolic diseases. The method, originally developed at Eindhoven University of Technology, can also be applied to evaluate the long-term effects of pharmacological interventions. Using a Systems Biology approach, experimental data from longitudinal studies are integrated in mechanism-based disease progression models. ADAPT identifies the biological processes that are disturbed and collectively induce the disease. Such information can be used for the development of therapies, improved diagnosis and prevention. See an animation of ADAPT on YouTube: http://youtu.be/x54ysJDS7i8 (Systems Biology of Disease Progression).
ADAPT was published in 2013. (Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PA, van Riel NA. Parameter trajectory analysis to identify treatment effects of pharmacological interventions. PLoS Comput Biol. 2013 Aug;9(8):e1003166. http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003166 )
Modelling multi-factorial, progressive diseases is an important challenge in Systems Biology and for the application of a Systems Biology approach in biomedical and clinical research (Systems Medicine). Processes at different biological levels are affected, including gene transcription, post-translational modification, signalling and metabolism. Multivariate molecular data of the metabolome, proteome and transcriptome need to be integrated to progress our understanding of such diseases. Also the dynamic interaction of organs and tissues needs to be considered. The time scales of the molecular events governing cell behavior versus the gradual and adaptive (patho)physiological changes induced by a progressive disease differ many orders of magnitude. ADAPT was developed as a computational approach to bridge the scales and different levels of biological detail.
ADAPT aims to investigate phenotype transitions during disease development and after therapeutic intervention. ADAPT is based on a time-dependent evolution of model parameters to describe the dynamics of metabolic adaptations. The progression of metabolic adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages.
The mathematical framework of ADAPT is based on systems of coupled, nonlinear Ordinary Differential Equation (ODE). ODE models can capture causal relationships in a biological network and describe system dynamics. Many established numerical methods are available for simulation and analysis of such models. ODE models in biology are typically constructed to simulate processes at a single timescale, usually capturing short-term dynamics ranging from seconds to hours. However, diseases typically develop and progress over a period of years and also therapeutic interventions take considerable time to become effective. To describe and simulate biomolecular processes covering time scales that are several orders of magnitude different, time-dependent parameters are used.
Models developed with ADAPT describe a metabolic network in cells, between cells in different tissues and in the blood plasma connecting the tissues. The metabolic network can be described in great detail, including enzyme kinetics. The model can calculate metabolite concentrations and fluxes. Transitions between metabolic phenotypes during disease progression also involve changes in the transcriptome and proteome. Likewise, interventions at one level (e.g. adding a protein kinase inhibitor drug) will result in changes in the other levels as well. However, the interaction networks of genes and proteins are less well known and kinetic information is generally lacking. ADAPT uses a data-driven approach to describe the effects that changes in protein activity and, indirectly, gene expression have on the metabolic network. It uses methods and numerical algorithms from statistical inference and system identification.
For this, different types of molecular data are combined which are collected in a longitudinal study at different stages of disease progression or after therapeutic interventions. Changes in model parameter are inferred from the experimental data yielding time-varying parameters. The time-varying parameters can be represented as trajectories that link the different phenotypes into a model of disease progression or treatment.
The resulting models can describe long-term adaptations in a biological system, but since the models contain kinetic equations for all the included metabolic processes the same model can also simulate short-term dynamics (e.g. on a time-scale of seconds) induced by variations in metabolite concentrations and fluxes. (Van Riel NA, Tiemann CA, Vanlier J, Hilbers PA. Applications of analysis of dynamic adaptations in parameter trajectories. Interface Focus. 2013 Apr 6;3(2):20120084. http://rsfs.royalsocietypublishing.org/content/3/2/20120084 )
Because of the uncertainties associated with the data and the complexity of the system at hand, in general multiple acceptable parameter trajectories are obtained. Here acceptable means that the parameter values yield a model that is in agreement with the experimental data. ADAPT analyses how uncertainties in experimental data propagate through parameter values and model predictions (uncertainty analysis).
Step 1. In longitudinal studies quantitative experimental data is collected at different stages of a disease or treatment intervention.
Step 2. Information of data uncertainty (such as variance / standard deviation, or the distribution) is used to develop a data error model (probability density function). The error model is sampled using a Monte Carlo approach. Given the samples cubic smoothing splines are calculated that describe the dynamic trend of the experimental data. To account for experimental and biological uncertainties many of samples are drawn and a collection of splines is calculated.
Step 3. The cubic splines are used as input for ADAPT to iteratively estimate dynamic trajectories of metabolic parameters and fluxes.
The progression of adaptations induced by a disease or treatment intervention is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The time-dependency of the parameters is introduced by dividing a simulation in N_i steps of \Delta t time period. Initially (n=0) the system is in steady-state and corresponding parameters \theta [0] are estimated to describe the experimental data of the healthy / untreated phenotype. Subsequently, for each step n>0 the system is simulated for a time period of \Delta t using the final values of the model states of the previous step X[n-1] as initial conditions. Simultaneously, parameters \theta [n] are estimated by minimizing the difference between the data interpolants and corresponding model outputs Y[n] . Here, the previously estimated parameter set \theta [n-1] is provided as initial set for the optimization algorithm.
The output of ADAPT is typically presented as time-course probability densities. Histograms calculated from the acceptable sets show the density of trajectories. A darker color represents a higher density of trajectories in that specific region and time point. In the example, the white lines enclose the central 67% of the densities and the errorbars represent some of the experimental data that was used as input.
ADAPT was initially applied to a metabolic condition associated with obesity and hepatic steatosis. The metabolic network model describes lipoprotein metabolism in blood plasma and liver.
ADAPT is applied in the RESOLVE project on Systems Medicine of Metabolic Syndrome (funded by FP7 Health grant agreement No 305707) http://www.resolve-diabetes.org/
