Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell's dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.
Keyphrases
- cell fate
- single cell
- early stage
- cell therapy
- quality improvement
- high resolution
- pregnant women
- deep learning
- single molecule
- high speed
- convolutional neural network
- mesenchymal stem cells
- mass spectrometry
- molecular dynamics simulations
- machine learning
- molecular dynamics
- transcription factor
- rectal cancer
- endoplasmic reticulum stress
- pregnancy outcomes
- drug induced