Skip to content

Computational Models for Gene Expression Analysis

Lead Researcher: [Researcher Name]
Timeline: 2024-2026
Funding: National Science Foundation (NSF)

Project Overview

This project focuses on developing novel computational models for analyzing gene expression data from single-cell RNA sequencing experiments. By combining mathematical modeling with machine learning approaches, we aim to identify gene regulatory networks and patterns associated with cellular differentiation.

Key Research Questions

  1. How can we effectively model temporal changes in gene expression during cell differentiation?
  2. What mathematical frameworks best capture the stochastic nature of gene expression?
  3. Can we develop interpretable machine learning models that provide biological insights?

Methodology

Our approach combines:

  • Differential equation-based models of gene regulatory networks
  • Stochastic simulation algorithms
  • Deep learning architectures for feature extraction
  • Interpretable machine learning techniques

Current Progress

  • Developed initial framework for time-series analysis of scRNA-seq data
  • Created validation pipeline using synthetic data
  • Established collaboration with experimental biology lab for validation

Publications

  1. [Author et al. (2024). "Title of Publication." Journal Name. DOI: link]
  2. [Author et al. (2023). "Title of Publication." Conference Name. DOI: link]

Team Members

  • [PI Name], Principal Investigator
  • [Graduate Student], PhD Candidate
  • [Collaborator], External Collaborator from [Institution]

Resources