Invention:
This invention presents methods for the estimation of joint characteristic functionals by way of ultrafast fluorescence imaging of multiple physiological random processes, each one labelled with a different fluorescence die.
Background:
Many biologic processes taking place inside a living organism are unpredictable in time and space, and cannot be known exactly. These mechanisms and interactions among them are better modeled as physiological random processes, the statistics of which are fully described by joint characteristic functionals. Flow cytometry is currently used to detect and measure physical and chemical characteristics of a population of cells, to analyze disease progression. This method does not provide a complete picture and often inaccurate. Deep learning is being coupled with current practices to improve analysis. This technology can be used to study complex diseases (e.g., tumor) by imaging the biological processes involved with disease progression and response to treatment. Physiological modeling has been applied to other areas such as improving sleep apnea and accounting for mechanistic effects in gas-to-gas conversion processes for biotechnology. Use of data-driven statistical modeling techniques with characterization functions improves upon parametric modeling methods, such as for bacteria growth. Light sheet imaging has been applied to transport of influenza viruses in live cells. Yet, these advanced imaging and modeling techniques appear primarily in medical journals and research hospitals rather than commercial use. Flow cytometer from ThermoFisher Scientific makes multiparametric flow cytometry available using acoustic hydrodynamic focusing rather than radiative imaging, and perhaps methods in this invention could extend analysis capabilities of microscopes and other imaging devices.
Applications:
- Study complex diseases
- Molecular biology
- Drug discovery
Advantages:
