Characterization of hepatic fatty acids using magnetic resonance spectroscopy for the assessment of treatment response to metformin in an eNOS-/- mouse model of metabolic nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. Liver biopsy remains the gold standard for diagnosis and staging of disease. There is a clinical need for noninvasive diagnostic tools for risk stratification, follow-up, and monitoring treatment response that are currently lacking, as well as preclinical models that recapitulate the etiology of the human condition. We have characterized the progression of NAFLD in eNOS-/- mice fed a high fat diet (HFD) using noninvasive Dixon-based magnetic resonance imaging and single voxel STEAM spectroscopy-based protocols to measure liver fat fraction at 3 T. After 8 weeks of diet intervention, eNOS-/- mice exhibited significant accumulation of intra-abdominal and liver fat compared with control mice. Liver fat fraction measured by 1 H-MRS in vivo showed a good correlation with the NAFLD activity score measured by histology. Treatment of HFD-fed NOS3-/- mice with metformin showed significantly reduced liver fat fraction and altered hepatic lipidomic profile compared with untreated mice. Our results show the potential of in vivo liver MRI and 1 H-MRS to noninvasively diagnose and stage the progression of NAFLD and to monitor treatment response in an eNOS-/- murine model that represents the classic NAFLD phenotype associated with metabolic syndrome.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.