Non-hodgkin Lymphoma B 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. B cells are a type of lymphocyte that fights infection by producing antibodies to neutralize foreign invaders. B-cell lymphomas include both Hodgkin's lymphomas and most non-Hodgkin lymphomas. 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. Subtypes of non-Hodgkin's lymphoma that involve B cells include diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, and Burkitt lymphoma.
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 B cells to determine appropriate model parameters. This helps to elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations.
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
- María Rodríguez Martínez, et al. Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis. PNAS. 2012.
- Frieboes HB, et al. Predictive Modeling ofDrug Response in Non-Hodgkin's Lymphoma. PLoSONE. 2015. 10(6): e0129433.
