@NCIsysbio #SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms. A recent great example of

@NCIsysbio #SysBio4CancerResearch: Another important future direction is the integration of AI/ML approaches with mechanistic modeling. Combining highly predictive black-box approaches with parameterized math models to gain mechanistic insight into that black-box:

@NCIsysbio Hey all! Happy to be back for Part 2 of #SysBio4CancerResearch and wanted to start with some cancer #SystemsBiology lyrics to One Kiss (@OliverHeldens remix) by @CalvinHarris & @DUALIPA. #SysBio4CancerResearch

@NCIsysbio #SysBio4CancerResearch: Mathematical modeling has made a significant recent impact in the design of treatment scheduling protocols that are evolution-based strategies to mitigate the evolution of resistance. But there is still many unanswered questions, and #MathOnco or

📌 Please join one our chat on #SystemsBiology and cancer research on July 18 at 7 pm ET or on July 23 at 8 pm ET. Use #SysBio4CancerResearch to follow a discussion of recent advances and opportunities to use systems-level approaches in #CancerResearch. @NCIsysbio

@NCIsysbio On the question of new technologies #SysBio4CancerResearch, I’d like to point out a few software packages we’ve been using to analyze spatial data: First, VALIS (Virtual Alignment of pathoLogy Image Series), published by @cgatenbee has been invaluable in our workflow, aligning

@NCIsysbio I am most excited about Systems Bio approaches to better understand cancer as an evolutionary & ecological process. Math modeling is key to driving advances in treatment scheduling in this complex, multiscale disease. #SysBio4CancerResearch 1/2

@NCIsysbio On new technologies/approaches in #SysBio4CancerResearch: A neat example of how @S_Marzban incorporated VALIS into our workflow for Lenia-based spatial models to determine the effect of ECM on immune infiltration (joint work w/ @AmelioLab) Our preprint is here:

@NCIsysbio On new technologies/approaches in #SysBio4CancerResearch: Mistic: an open-source multiplexed image t-SNE viewer created by @sandhya212 t-SNE or UMAP is a nice way to visualize relationships in high dimensional data in a reduced dimension diagram, but you lose the visual,

@NCIsysbio Take our recent review in Annals of Oncology for example, showing that mathematics was involved in major advances in treatment scheduling every single decade since 1960’s! #SysBio4CancerResearch 2/2 https://t.co/YPZ7RyyWeO #OpenAccess

SysBio 4 Cancer Research by @theNCI Division of Cancer Biology https://t.co/nQLHRASk3B #Cancer #NCI #CancerResearch #SysBio4CancerResearch #OncoDaily #Oncology

@NCIsysbio A7: Dr. Forest White shares preclinical #SystemsBiology approaches that revealed potential new therapeutic strategies for incurable cancers in a @MIT seminar: https://t.co/2NJE0Lk7va #SysBio4CancerResearch

@NCIsysbio Looking forward to Part 2 next week! #SysBio4CancerResearch

@NCIsysbio A4: In a #CancerMoonshotSeminar, Dr. Martha Shrubsole shows how an integrative #SingleCell atlas of the host and microenvironment reveals insights into #ColorectalCancer development. https://t.co/iSs8Tr7kx0 #Sysbio4CancerResearch #CMSSHTAN

@NCIsysbio A6: In a #CancerMoonshotSeminar, @SantagataLab shared how multiplexed tissue imaging reveals insights into the spatial biology of cancer. https://t.co/FlAJtY4WdP #SysBio4CancerResearch #CMSSHTAN @NCIHTAN

@NCIsysbio A8: I think, especially with the growing area of spatial biology, there are opportunities in #dataviz for cancer #SystemsBiology (which may be related to some conversations about #DataViz4Cancer). https://t.co/3lm33txhJd. #SysBio4CancerResearch

@NCIsysbio @MDAndersonNews A1: (3/3) We leverage cutting-edge single-cell & spatial multi-omics, molecular imaging, combined with the development and innovative use of bioinformatics tools, computational pathology, and advanced machine learning models for novel discoveries @NCIsysbio #SysBio4CancerResearch

@NCIsysbio A5: the @NCIHTAN Human Tumor Atlas Network Data Portal: https://t.co/FzGpN2TlSO includes clinical, genomics, spatial multi-omics, and imaging data across various organs and diseases, publicly accessible by the research communities. #SysBio4CancerResearch

@NCIsysbio A8: I also think there may be opportunities for more translational #SystemsBiology initiatives like @ARPA_H ADAPT, which aims to harness advanced technologies and a deep understanding of tumor biology to build cancer #biomarkers. https://t.co/toOvfgyjkf #SysBio4CancerResearch

@NCIsysbio A5: The @NCIDataSci Cancer Research Data Commons is a great resource for data. https://t.co/hzW3vpHVBC  #SysBio4CancerResearch