Research by Benji from Appcubic

My Research

Optical Modulation

Harnessing the principles of physics and cutting-edge technology researchers have developed Optically Modulated Fluorescent Proteins (OMFPs) to modulate fluorescence intensity in fluorescence microscopy, significantly improving imaging sensitivity and specificity in cellular biology.

Such advancement, exemplified by methods like Synchronously Amplified Fluorescence Image Recovery (SAFIRe), enhances the accuracy of biological investigation, driving breakthroughs in life sciences from understanding disease pathways to therapeutic innovation.

Harnessing Optically Modulatable Fluorescent Proteins (OMFPs) for Reference-Free and Background-Free Cellular Imaging via Algorithmic Digital Signal Processing (DSP)

OMFPs, a novel class of fluorescent proteins meticulously researched and uncovered by our team, hold immense potential in revolutionizing the landscape of live-cell fluorescence imaging.

Innovatively adopted and employed sophisticated strategies to systematically uncover, comprehend, and interconnect experimental data derived from diverse sources. Extracted pivotal insights amidst a sea of background noise.
Achieved absolute reference-free and background-free cellular images by implementing mathematical transformations such as Fast Fourier Transform (FFT) on time-series cellular fluorescence data using bespoke algorithms developed in Python, Matlab, and Mathematica.

Engineering of Optically Modulatable Fluorescent Proteins (OMFPs)

The fluorescence intensity of OMFPs can be precisely modulated via co-illumination with photons exhibiting more red-shifted wavelengths than their inherent fluorescence.

Utilized site-directed mutagenesis for the engineering of novel OMFPs, drawing on intramolecular interactions between the chromophore and its environment via computer-assisted 3D modeling.
Fostered a highly collaborative design/engineering team environment to streamline and formalize experimental sequences, optimizing time efficiency while maintaining cost-effectiveness.

Mathematical Simulation of Fluorescent Protein Photophysics

Optical modulation and delayed fluorescence from OMFPs and additional fluorophores can be modeled with exceptional fidelity.

Employed multi-state models with rate matrices analogous to Jablonski diagrams for fluorescence simulation. This methodology facilitated the determination of vital photophysical parameters that were previously too complex to assess.
The simulation was achieved through Python-based computation (Numpy and Pandas), optimization (Scipy), and visualization techniques (Matplotlib). Utilized the object-oriented programming (OOP) paradigm, leading to flexible, cogent, and concise code.

Retention Projection

Algorithmic problem solving by mathematical toolkit and the state-of-the-art instrument is a crucial to the success of retention projection.

Measuring Unbiased Gas Chromatographic Retention Factor (K) vs. Temperature (T) Relationships

Problem Statement:
- Direct methods to measure k vs. T relationships are tedious and time-consuming.
Proposed Solution:
- A new methodology using a series of temperature programs and algorithmic back-calculation of effective temperature and hold-up time profiles.
Impact and Results:
- The k vs. T relationships measured from this approach had comparable accuracy as those measured isothermally.
- This methodology showed less than two-fold error across different GC-MS instruments, confirming its robustness.
Advancements and Future Work:
- The methodology is relatively fast, easy, and doesn’t require any additional equipment.
- Plans are in place to build a large database of k vs. T relationships using this methodology.

Retention Projection in Gas Chromatography-Mass Spectrometry (GC-MS)

Need and Relevance:
- Existing shared retention databases are unreliable and highly dependent on the specific GC-MS system.
- Lack of standards for all compounds of interest poses a significant challenge in compound identification.
Methodology and Accuracy:
- The Retention Projection methodology combined with a back-calculation technique offers a more accurate approach.
- The methodology is 3-fold more accurate under the same experimental conditions across labs, correctly accounting for unintentional GC-MS system differences.
Effect of Different Methods and Lab Conditions:
- Retention projections offer 4- to 165-fold more accuracy across labs using different methods.
- Distribution of error was predictable across different methods and labs.Distribution of error was predictable across different methods and labs.
Advancements and Potential:
- The methodology allows for automatic calculation of retention time tolerance windows.
- With high accuracy and reliability, retention projection is a powerful tool for compound identification, even without physical standards.

Retention Projection in LC–MS

Retention Projection in Liquid Chromatography-Mass Spectrometry (LC–MS)

Need and Relevance:
- Current standard for sharing retention data, Linear Retention Indexing, is unreliable due to inability to account for lab-specific experimental conditions.
Methodology and Accuracy:
- The Retention Projection methodology combined with a back-calculation technique offers a more accurate approach.
- 2-fold to 22-fold more accuracy observed with this approach under various lab conditions and intentional experimental differences.
Future Directions:
- Further investigation needed to correct for unintentional differences , such as changes in columns’ selectivity for charged compounds over time.
- A need to develop faster methods for measuring k vs. Φ relationships to make the methodology more practical.