Abstract
Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.
Highlights
Dynamic responses contain important information about the behavior of biological systems
Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of Liver X receptor (LXR)-induced hepatic steatosis
The model includes the hepatic uptake of free fatty acids (FFA) from plasma that predominantly originate from adipose tissue
Summary
Dynamic responses contain important information about the behavior of biological systems. The application of omics technologies, such as transcriptomics and metabolomics, to study the dynamics of biological systems results in high-dimensional time-series data, in which the number of gene expression values or small molecules detected in biological fluids is larger than the number of samples. In silico dynamic models often lack the multi level layers of regulation that control metabolism This impedes their application in disease modeling because causes of disease can be located at multiple levels, and molecular therapies can be targeted to genes, proteins and metabolites. To overcome current limitations in statistical analysis and mechanistic modeling we combine metabolic modeling with machine learning techniques to integrate longitudinal metabolic and transcriptomic data. ADAPT combines mechanism-based differential equation models with machine learning to model temporal metabolic data (Tiemann et al, 2013; Rozendaal et al, 2018b). The new version of ADAPT uses the metabolite data as input to estimate trajectories of metabolic
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