DNA methylation is a biological process that involves the addition of methyl groups to nucleobases on specific regions of a DNA sequence. Recent studies showed that DNA methylation age is highly related to chronological age. Capturing the methylation infomation could help us investigate possible health problems at the early stage and possibly pre-diagnose certain diseases. Our goal is to develop a new measure to predict an individual’s “biological” age based on their epigenetic clock, which indicates early onset of age-related diseases. The biological age predictor can be incorporated by clinicians to a standard annual checkup and used in combination with other health measures to determine an individual’s overall health and to enable early disease intervention.

Traditionally researchers analyze metabolic systems using a simplified approximation of enzyme kinetics. as the result, the metabolic system could be represented by a series of non-linear kinetics equations (see KGEE for examples). However, it is not practical to find the kinetics equation for every single reaction in the system, especially when we simulate a large complicated system, for example, human metabolic network.

The alternative solution is Metabolic Flux Analysis which employs stoichiometric models of metabolism and mass spectrometry method to understand cellular physiology within particular boundaries and predict its metabolic capability over time. In our project we extended the method to understand the full scale of human metabolic network, including the major organs esential for human metabolism. Moreover, we applied morden AI framework to boost the simulation, which enable us to evaluate the impact of daily diet/vitamin intake, gendar difference, individual condition, etc. This method has great potential to help analysis and pre-experiment of drug effect in R & D stage.

Cancer cells generally immerse in oxidative stress with a high intracellular reduction level. Thus, nanocarriers with sequential responsiveness to oxidative and reductive species, matching up the traits of high oxidation in tumor tissue microenvironment and high reduction potential inside cancer cells, are highly desired for specific cancer therapy. In this work, researchers report a supramolecular nanomedicine, comprised of reduction-responsive nanoparticles (NPs) core whose surface was modified by an oxidation-responsive polyethylene glycol (PEG) derivative via strong host-guest interactions. By such a delicate design, PEGylation of NPs not only reduced the immunogenicity and extended systemic circulation, but also enabled the oxidation-responsive de-PEGylation in the tumor tissues and subsequent intracellular payload release in response to glutathione (GSH) inside tumor cells. As a proof of concept, this supramolecular nanomedicine exhibited specific chemotherapeutic effect against cancer in vitro and in vivo with a decent safety profile.