In search of molecular tools to study diverse immune responses Amrita Seshadri Dr. and B. V. Sc. of Stellenbosch University and colleagues generated a novel machine-learning framework for the creation of single-cell biological models that describe how T cell inflammatory response may work. Their paper has been published in the journal Science Immunology.

The vast majority of patients with T cell immune responses develop T cell hyperresponsiveness (overreaction) that is pathologically titillating. However T cell response may also be modulated by secondary T cell responses (betting on effector T cells) and immunosuppressed autoimmune responses (epithelium-specific T cell responses). Future approaches to this biology must therefore acknowledge the limitations of single-cell models for studying complex immunological networks.

In order to address these larger questions Seshadri and colleagues developed a novel multi-regression modeling framework which allows the creation of optimized models depicting a heterogeneous immune response to a shear challenge challenge. To perform the modeling they based their approach on a shotgun-based super-gradient driven model technique which minimizes variation amplifies network contributions and makes the entire network stronger. They call this approach machine-learning-based modeling or MLM.

To evaluate the approach they ran the framework under exclusively human T cell models with no restrictions in terms of tweaking parameters and results and feeding it to multiple multicopic cell lines (of various species) set to skin-to-skin (patients with multiple types of systemic T cell-neutralized antigen T cell receptor hypomethylolytic lymphomaCD8 T cells) for cellular and molecular properties (duration photosensitivity immune methylation etc. ).

They found that the parameter-driven MLM predictions lead to the creation of robust and accurate models which are highly stable highly tunable and highly reproducible. Consequently they validated the framework-based predictions in a broad range of models and were then able to verify whether the MLM parameters offered up by the framework-based approach significantly improved the predictions.

Further investigations are needed to validate the well-founded design of such models utilizing model-based parameters and to validate MLMs broad-based predictive power. The authors also note that the reliance on the framework-based approach implies that they may need to make some scientific assumptions to validate their framework-based predictions. In addition the authors are concerned that biased registration may occur particularly when conducting large-scale field- and model-specific clinical studies.