Non-Hodgkin Lymphoma T cell

Non-Hodgkin's lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. Most non-Hodgkin's lymphoma arises from B cells. It is the most common type of lymphoma and a big portion of all lymphomas are B-cell. T-cell lymphomas are rare, comprising less than 15% of non-Hodgkin lymphomas. With early diagnosis and advanced treatment methods, non-Hodgkin lymphoma has a high survival rate.

• Combine mathematical modeling with experiments

Investigate the tissue-scale physiologic effects by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. Computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of tumor growth.

• Dynamical Modeling

The module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. Construct a quantitative kinetic model of key gene regulators, and use gene expression profile data from mature human T cells to determine appropriate model parameters. This helps to elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations.

Computational Research Trends

Mathematical modeling constitutes an emerging area of oncological research aiming to predict spatial and temporal evolution of tumors, by describing many different phenomena, which occur at different scales. Among these, modeling at the macroscopic scale has an interesting potential of application, when applied in a framework where actual diagnostic imaging is used to identify the metabolic tumor volume undergoing proliferation.

Our computational biology platform has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. We have years of experience performing computational analyses of related data sets and aiding Non-Hodgkin's lymphoma research. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers. Contact us for more service details.

References

1. Rosj Gallicchio, et al. A mass transfer model for computational prediction of proliferation and therapy outcome of non-Hodgkin lymphoma.International Communications in Heat and Mass Transfer. 2021.
2. Frieboes HB, et al. Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma. PLoSONE. 2015. 10(6): e0129433.