Our scientific team develops omics data analysis methods to identify potential targets for therapeutic intervention against age-related diseases and aging.
Since modern omics data is high dimensional, i.e. the number of features in it is much higher than the number of measurements, the use of traditional machine learning methods is impossible due to the emerging problem of overfitting. Therefore, it is necessary to develop new mathematical methods to analyze this type of data.
In our work we use the models of statistical physics to analyze gene networks stability and predict their dynamics over time. The proposed models allow us to link gene network stability with mortality. The models we developed were validated on omics data of different types, such as transcriptome, proteome and metabolome measured in different tissues of various organisms.
Our techniques open new opportunities to identify targets to develop new therapy candidates against aging.
Extracting biological age from biomedical data via deep learning: too much of a good thing?Scientific Reports
Stability analysis of a model gene networks links aging, stress resistance, and neglegible senescence
Strehler-Mildvan correlation is a degenerate manifold of Gompertz fitPreprint Arxiv: 1502.04307
Critical dynamics of gene networks is a mechanism behing ageing and Gompertz law
The goal of our research is identification of the potential human anti-aging targets and development of a therapy that will prolong human lifespan and healthspan. We applied our methods to age-dependent biological data for several species.
We are currently running a nematode study conducted in collaboration with Robert Reis from Arkansas University, who is world leader in nematode life extension. We collected our proptietary longitudinal human proteomic data and are working on identification of human anti-aging targets and their further validation in mice.
Gero mHealth project is aimed at deep analysis of human physical activity data. The physical activity measurements are now readily available to users of wearable devices and fitness smartphone apps. Comprehensive vision of physically active and healthy diet lifestiles, however, is still in a badly need for quantitative metrics of wellness improvement progress.
Gero mHealth team is developing AI engine to identify common patterns of ageing and fitness status in human locomotor signal. Our research goal is to bridge data on everyday routine locomotor activity with a new level of understanding how these patterns undergo remodelling with ageing and wellbeing status of an organism. We believe that the benefits of our research will be quantitatve metrics for monitoring and providing recommendations on lifestyle interventions.
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The subject of our studies is Gene Regulatory Network (GRN) - commonly understood as a collection of molecules in the organism (including mRNA, proteins, metabolites etc), that interact with each other and govern the concentrations of each other. We study the dynamics of gene regulatory networks and their stability in time domain and develop the physical models that describe the dynamics of GRN. In our works we showed that GRN dynamics is related to aging.
We developed computational methods and tools based on our models that let us observe age-related GRN dynamics and identify targets against aging in age-dependent biological data.