{"doi":"10.1016/j.patter.2022.100549","title":"Developing tools for analyzing and viewing multiplexed images","abstract":"Dr. Prabhakaran and Dr Gatenbee are research scientists in Anderson’s lab and have developed Mistic, a publicly available tool that simultaneously views multiplexed images and assists in gaining biological and clinical insights into patients’ data. They discuss the role of mathematical modeling in translational cancer research and clinical decision making and describe how mathematical modeling fits into the data science definition. Dr. Prabhakaran and Dr Gatenbee are research scientists in Anderson’s lab and have developed Mistic, a publicly available tool that simultaneously views multiplexed images and assists in gaining biological and clinical insights into patients’ data. They discuss the role of mathematical modeling in translational cancer research and clinical decision making and describe how mathematical modeling fits into the data science definition. Alexander R.A. Anderson: As a mathematician by training, I’ve always loved the idea that mathematics can help us better understand biology. My lab at Moffitt brings together a diverse team of quantitative scientists all viewing cancer through the lens of evolution and ecology. We integrate mathematical and computational modeling approaches with experimental and clinical data to better understand cancer and translate this understanding into novel therapies. Working with clinical data, it was inevitable that we would end up analyzing histology. I’m a Scotsman in love with New England IPA and an avid collector for indie rock vinyl (especially the colored variety) and vintage science fiction. Sandhya Prabhakaran: I am a computer scientist and applied statistician by training. During my PhD and postdoctoral training, I was intrigued by the trifecta of problems in analyzing high-throughput biological datasets: the cost of collecting such data, the sheer size of the search space, and the complex and poorly understood mechanisms in biology. Solving these problems would require the right data and the usage of data science tools to help shape the biological questions, which in turn help build meaningful mathematical models to understand the underlying biological mechanisms. When I am not coding, I enjoy spending time with my family. I also practice yoga and am an avid runner. Chandler Gatenbee: I am an evolutionary biologist by training and was bitten by the mathematical modeling bug during my PhD, where I used agent based models to explore how infection by the JC virus may increase the risk of colorectal cancer. After that, I took a deeper dive into modeling by joining the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, which was especially appealing due to their focus on understanding cancer through the lens of evolution and ecology. This is also where I learned to work with whole slide images, as well as the ecological tools to analyze them. Outside of this work, I’m also a big IPA and music geek, always on the hunt for new brews and sounds. ARAA: Cancers are complex, dynamic, adaptive systems—complex because they consist of multiple cellular and microenvironmental components, dynamic because the components interact with each other through a complex network of interactions that change in space and time, and adaptive because critical elements of the network as well as the network itself can change and adapt to perturbations. Mathematical models are truly the only way to decipher this complexity and predict potential therapeutic strategies to exploit it. Once you accept the dynamic adaptive nature of cancer, there is no escape cancer ecology and evolution—making sense of an evolving cancer with a spatially diverse ecology requires both sophisticated measurement technologies and also models that can utilize these measurements. Just as we have developed Mistic1Prabhakaran S. Gatenbee C. Robertson-Tessi M. West J. Beg A.A. Gray J. Antonia S. Gatenby R.A. Anderson A.R.A. Mistic: An Open-Source Multiplexed Image T-SNE Viewer.Patterns. 2022; 3: 100523https://doi","journal":"Patterns","year":2022,"id":309711,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9538,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":280732,"name":"Chandler Gatenbee","orcid":"0000-0002-9730-5964","position":1,"is_corresponding":false},{"id":280735,"name":"Alexander R.A. 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