Biologists in the Modern Age Influenced by Artificial Intelligence Fundamental Architectures
In the era of artificial intelligence (AI), experimental biologists are playing an integral role in the field of single-cell biology. Their contributions are essential in generating high-quality, AI-compatible datasets, validating AI-generated hypotheses, and developing innovative experimental systems for real-time, multi-omics single-cell analysis.
One of the key resources for single-cell data is the Single Cell Expression Atlas, a representative public dataset that provides a broad representation of single cell data. Another valuable resource is the Human Cell Atlas (HCA), which offers a comprehensive map of cellular diversity in the human body. Additionally, there are public datasets like Tabula Sapiens, GEO, NCBI SRA, and GlaxoSmithKline's internal datasets that contribute to the wealth of information available for research.
The role of experimental biologists extends beyond data generation. They design and conduct biologically meaningful experiments to preserve key cell-cell interactions and communication necessary for biologically relevant hypothesis testing. This is achieved through the development and utilization of advanced models, such as new animal models, primary cell cultures, and 3D tissue models.
AI tools and foundational models, such as generative and predictive modeling frameworks like CellOracle, scGen, and scVI, enable researchers to simulate and forecast cellular responses. These models help prioritise hypotheses and reduce costs by computational pre-screening of experiments. However, these AI models heavily rely on large, well-annotated, and appropriately generated datasets that experimental biologists must produce, especially in under-explored systems or with limited starting material.
Experimental biologists also contribute to the advancement of single-cell technologies for real-time multi-omics detection and manipulation inside living cells. This experimental innovation is critical to translate AI-driven data insights into physiological understanding of normal and disease-related cellular functions.
Arpita Kulkarni, the Director of the Functional Genomics and Bioinformatics Core at HMS/BIDMC and a cell and developmental biologist focused on bridging single-cell biology and AI for therapeutic discovery, emphasises the importance of experimental biologists in this context. She notes that they decide which tissue types, organisms, or disease states are important for the question at hand, helping to select biologically diverse and well-annotated datasets.
Collaboration is a key aspect of this work. Researchers can use their own single-cell data or that of a collaborator for training. Open-source foundational models to explore include scVI, scGPT (emerging), totalVI, UniCell, and others. Collaborators can help download and standardise the data using tools like Scanpy, Seurat, or scVI-tools.
In summary, experimental biologists in this AI-driven era serve as integral partners who generate the data, provide biological context, validate computational models, and innovate experimental approaches. Their work ensures that AI tools lead to meaningful biological and clinical discoveries in single-cell biology.
- The Single Cell Expression Atlas, the Human Cell Atlas, Tabula Sapiens, GEO, NCBI SRA, and GlaxoSmithKline's internal datasets are valuable resources for single-cell data in research.
- AI tools like CellOracle, scGen, and scVI enable researchers to simulate and forecast cellular responses, helping to prioritize hypotheses and reduce costs.
- Experimental biologists are essential in generating high-quality, AI-compatible datasets and developing innovative experimental systems for real-time, multi-omics single-cell analysis.
- Arpita Kulkarni, a cell and developmental biologist, emphasizes the importance of experimental biologists in bridging single-cell biology and AI for therapeutic discovery.
- Collaboration is crucial in this field, as researchers can use open-source foundational models like scVI, scGPT, totalVI, UniCell, and others, and tools like Scanpy, Seurat, or scVI-tools for standardizing data.
- In the field of fitness-and-exercise, nutrition, health-and-wellness, medical-conditions, and even space-and-astronomy, the insights gained from single-cell biology research can have a significant impact on lifestyle, technology, education-and-self-development, and entertainment.
- The role of experimental biologists extends beyond data generation, as they also design and conduct biologically meaningful experiments and contribute to the advancement of single-cell technologies for real-time multi-omics detection and manipulation inside living cells.
