MAST: A hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach
Bioinformatics Advances, ISSN: 2635-0041, Vol: 2, Issue: 1, Page: vbac092
2022
- 4Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Motivation: Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario. Results: We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-Throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model. The application to real data of human colorectal cancer tissue investigating the spatio-Temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85148559844&origin=inward; http://dx.doi.org/10.1093/bioadv/vbac092; http://www.ncbi.nlm.nih.gov/pubmed/36699399; https://academic.oup.com/bioinformaticsadvances/article/doi/10.1093/bioadv/vbac092/6873756; https://dx.doi.org/10.1093/bioadv/vbac092; https://academic.oup.com/bioinformaticsadvances/article/2/1/vbac092/6873756
Oxford University Press (OUP)
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