AI for Decoding Gene Expression and Cellular Behavior

These approaches are increasingly applied to analyze high-dimensional datasets produced by high-throughput sequencing. They can help identify patterns in complex biological systems that traditional statistical techniques may miss.1

As a result, AI is becoming a useful tool for improving the scale and detail of analyses related to gene expression and cellular behavior.

Gene expression is controlled by complex regulatory mechanisms that determine how cells respond to internal states and external cues. At the core of this regulation are gene regulatory networks (GRNs), which coordinate the spatial and temporal activity of genes through interactions among transcription factors, non-coding RNAs, enhancers, and other regulatory elements.2,3

Traditional computational approaches to modeling these processes often rely on predefined rules or simplified, linear regulatory networks. However, such models may not reflect the nonlinear, variable, and context-specific nature of gene expression.1,3

In contrast, AI models such as recurrent neural networks (RNNs), graph neural networks (GNNs), and transformer-based frameworks offer enhanced capacity to model nonlinear interactions directly from high-dimensional transcriptomic and epigenomic data. This includes single-cell RNA sequencing (scRNA-seq), time-series transcriptomics, and nascent transcription profiling.4,5

By integrating multi-omics data—including chromatin accessibility, transcription factor binding, and epigenetic modifications—AI models can capture the complex regulation of transcription. This integrative approach helps uncover mechanisms underlying lineage bifurcations and cell-type specification, offering a more comprehensive view of gene regulation and the factors influencing cellular function.1

Generative AI models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), extend this capability by learning latent representations of gene expression patterns and identifying subtle transcriptional signatures that precede cell fate transitions.

This enables the simulation of potential differentiation paths and the exploration of hypothetical perturbations, offering a data-driven approach for generating hypotheses about cellular dynamics in response to changes in signaling or gene regulation.1,6

These methods enable the reconstruction of GRNs, modeling of transcriptional dynamics over time, and analysis of cell fate decisions under varying biological conditions. Together, these approaches offer new insights into gene regulation and support a more predictive, mechanistic understanding of cellular behavior.

AI models are helping to improve how gene expression and cellular activity are characterized. This has important applications in areas such as developmental biology, disease modeling, regenerative medicine, and drug discovery.

These models can also predict how cells respond to treatments at the level of gene regulation. By doing so, they support the discovery of new biomarkers and make it easier to guide cellular outcomes for therapeutic use. For example, an AI platform called Molecular Twin, which integrates multi-omics data, effectively predicts outcomes in patients with pancreatic adenocarcinoma.1

In disease modeling, AI-driven frameworks can reconstruct perturbed regulatory circuits with cell-type resolution, enabling a more accurate representation of disease states.

One such tool, single-cell Variational Inference (scVI), uses deep generative models to analyze scRNA-seq data. It corrects for batch effects and captures transcriptional variation across cells. scVI has been used to study immune cell states in COVID-19 and to map tumor microenvironments in cancer. These applications have provided insights into disease progression and resistance to treatment.7,8

In drug discovery, AI models can predict how cells respond to genetic or environmental perturbations. The Single-cell generative model (scGen), based on a variational autoencoder, forecasts transcriptional responses to drugs or CRISPR-based gene edits in specific cell types.

In recent studies, scGen has been used to anticipate how immune cells react to cytokine stimulation or checkpoint inhibitors, informing early-stage drug screening and guiding therapeutic hypothesis generation.9

Platforms such as CellOracle extend these capabilities by simulating gene regulatory perturbations using inferred GRNs. By integrating chromatin accessibility data with transcription factor binding motifs, CellOracle facilitates in silico knockout and overexpression experiments, enabling researchers to prioritize targets before undertaking wet-lab validation.

This approach is particularly valuable in stem cell biology and oncology, where elucidating mechanisms of lineage reprogramming and tumor plasticity is essential.10

In synthetic biology, AI models are also being used to design programmable cells and gene circuits. DeepSEA, a deep learning model trained on large-scale functional genomics data, predicts the effects of noncoding variants on chromatin accessibility and transcription factor binding. This tool aids in identifying regulatory elements that can be engineered for precise control of gene expression in synthetic constructs or gene therapy vectors.11

This integrated approach is accelerating progress across biomedical research and the development of next-generation therapeutics and engineered cellular systems.

The use of AI in modeling GRNs and cellular behavior raises important ethical considerations. One concern is data privacy and security, as AI in biotechnology depends on large volumes of sensitive genetic and health information.

Another concern is dataset bias, since many training datasets are based on limited populations, tissues, or experimental conditions. As a result, models may perform poorly or generate misleading results when applied to underrepresented cell types, disease states, or patient groups, potentially worsening existing health disparities.12,13

Model transparency is also an issue, especially in clinical or therapeutic contexts. Many deep learning models rely on incomprehensible algorithms with limited interpretability, making it difficult to validate predictions or the underlying biological mechanisms. This lack of explainability can undermine clinical trust and complicate regulatory approval processes.12,13

The dual-use risk of AI tools in synthetic biology must also be recognized. Predictive models capable of engineering gene circuits or reprogramming cells could be misused to design harmful biological agents or bypass biosafety controls. Responsible development, therefore, requires proactive governance, comprehensive risk assessment, and alignment with ethical and safety frameworks to prevent misuse while supporting innovation.12,13

AI is changing how cell biology is modeled and manipulated. Beyond advancing mechanistic understanding, these tools streamline translational workflows by enabling scalable, biologically informed predictions.

As their application expands in biomedical research, it is critical to ensure their use is representative, transparent, and ethically grounded. Addressing these priorities will help optimize AI integration into the study of gene expression and cellular behavior.

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Serebreni, L., & Stark, A. (2021). Insights into gene regulation: From regulatory genomic elements to DNA-protein and protein-protein interactions. Current Opinion in Cell Biology, 70:58-66. doi: 10.1016/j.ceb.2020.11.009

Vélez-Cruz, N., & Papandreou-Suppappola, A. (2024). Bayesian learning of nonlinear gene regulatory networks with switching architectures. Frontiers in Signal Processing, 4. doi: 10.3389/frsip.2024.1323538

Li, S., Hua, H., & Chen, S. (2025). Graph neural networks for single-cell omics data: a review of approaches and applications. Briefings in Bioinformatics, 26(2). doi: 10.1093/bib/bbaf109

Wang, Y., Chen, X., Zheng, Z., Huang, L., Xie, W., Wang, F., Zhang, Z. & Wong, K.C. (2024). scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics. iScience, 27(4). doi: 10.1016/j.isci.2024.109352

Yu, H., & Welch, J.D. (2021). MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks. Genome Biology, 22, 158. doi: 10.1186/s13059-021-02373-4

Sehanobish, A., Ravindra, N.G., & van Dijk, D. (2020). Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. arXiv:2006.12971. doi: 10.48550/arXiv.2006.12971

Treppner, M., Binder, H., & Hess, M. (2022). Interpretable generative deep learning: an illustration with single cell gene expression data. Human Genetics, 141(9):1481-1498. doi: 10.1007/s00439-021-02417-6

Rodov, A., Baniadam, H., Zeiser, R., Amit, I., Yosef, N., Wertheimer, T., & Ingelfinger, F. (2025). Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI. European Journal of Immunology, 55(2):e202451234. doi: 10.1002/eji.202451234

Kamimoto, K., Stringa, B., Hoffmann, C.M., Jindal, K., Solnica-Krezel, L., & Morris, S.A. (2023). Dissecting cell identity via network inference and in silico gene perturbation. Nature, 614:742–751. doi: 10.1038/s41586-022-05688-9

Li, Z., Gao, E., Zhou, J., Han, W., Xu, X., & Gao, X. (2023). Applications of deep learning in understanding gene regulation. Cell Reports Methods, 3(1):100384. doi: 10.1016/j.crmeth.2022.100384

Dara, M., & Azarpira, N. (2025). Ethical Considerations Emerge from Artificial Intelligence (AI) in Biotechnology. Avicenna Journal of Medical Biotechnology, 17(1):80-81. doi: 10.18502/ajmb.v17i1.17680

De Haro, L.P. (2024). Biosecurity Risk Assessment for the Use of Artificial Intelligence in Synthetic Biology. Applied Biosafety, 29, 2. doi: 10.1089/apb.2023.0031