#mathonc23 search results

Hi Zach, we met at the poster session during #mathonc23. Welcome to Moffitt.

Had a blast at #MathOnc23 - thank you @NikosDim3 for the gorgeous photo 🤩

Always very relaxed at the end of a very stimulating conference if you don't fly out until the next day. Even better when you share an AirBnB with your old labmates! Thanks for a great conference @gliomath and @ara_anderson! #MathOnc23

Thanks to @ara_anderson and @gliomath for organizing the inaugural Math Onco conf #MathOnc23. I enjoyed the didactic sessions, it was a blend of cancer biology and computational models. I look forward to doing this again next year.

This week in #MathOnco 253: - selection, adaptive immunity, adaptive therapy, cellular barcoding, and combination therapy Art from #MathOnc23, Phoenix meeting Sign up here: mathematical-oncology.org/newsletter

Thanks @gliomath and @ara_anderson and the rest of the organising team for #MathOnc23. What a pleasure to meet so many other cancer modellers and learn about their incredible work!

The final presentation of #MathOnc23 is by Erin Angelini presenting, A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness.

The IMO at Moffitt runs a workshop on modeling cancer each year: imoworkshop.org #mathonc23

The videos of the talks will be available in a few weeks after a bit of curation #mathonc23

The website also has a blog for posting resources and other thoughts on mathematical oncology #mathonc23

There is now a Mathematical Oncology web site: mathematical-oncology.org for helping to organize the community #mathonc23

The conference is wrapping up with a discussion of the future of mathematical oncology #mathonc23

Angelini: Did a sensitivity analysis of the dose response curve parameters. When the ability to kill cells is higher than the transition to resistant cells then there is an optimal dose for TTP which is below MTD #mathonc23

Angelini: Measure time to progression (TTP) which is the time until the total burden recovers to its starting size after the induction of therapy #mathonc23

Angelini: Use a Hill function dose response curve that affect death rates of the 2 cell types as well as the rate of switching from sensitive to resistant states. #mathonc23

Angelini: Have two cell types, sensitive and resistant (persister) cells. Drugs can increase the rate of transition from sensitive to resistant, as well as kill cells #mathonc23

Angelini: Building a population dynamic model of treatment induced non-genetic resistance. Then evaluate the activity of drugs both for their killing and generating persisters, and look at time to progression #mathonc23

Angelini: Think of treatment as a double-edged sword. It kills sensitive cells but pushes cells into the persister state, setting the stage for future relapse. #mathonc23

Angelini: Phenotypic switching into a persister state is associated with environmental stress. The state can be inherited across a limited number of cell generations #mathonc23

Next the penultimate talk of #MathOnc23 is by Einar Bjarki Gunnarsson from Jasmine Goo’s group presenting, Estimation of the rates of cell proliferation and phenotypic switching in cancer and implications for treatment.

Kristin highlights some the key sex differences she has found in her incredible GBM dataset including image localised biopsy analysis #MathOnc23

Fantastic start last night to #MathOnc23 with a packed poster reception full of amazing science, seeing old friends and making new! #EveryPatientDeservesTheirOwnEquation Grateful for support from @MayoCancerCare @MayoClinic @MoffittNews @NCIsysbio @NCIPhySci @JSMF

Now we have our penultimate didactic lecture from long term friend, collaborator and chair of #MathOnc23 @gliomath presenting, Sex differences in Mathematical Oncology @MayoClinic

Next up is Parag Katira presenting, GBM communities: exploiting the interactions between ploidy and host physiology #MathOnc23 This work emerged form the winning team of the #MoffittIMO workshop X on Cancer Communities!

Next up @ac_palmer presents, Accurate prediction of the clinical efficacy of combinations of cancer therapies #MathOnc23

Abstract submission is now open for #MathOnc23, Phoenix: mathematical-oncology.org/mathonc23 Looks to be a great workshop, organized by @gliomath & @ara_anderson!

Had a blast at #MathOnc23 - thank you @NikosDim3 for the gorgeous photo 🤩

Final talk of #MathOnc23 Day 2 is from Patrick Gelbach from @USCSysBio_Lab presenting, Ensemble-based Genome-scale Modeling Predicts Metabolic Differences between Macrophage Subtypes in Colorectal Cancer.

This week in #MathOnco 253: - selection, adaptive immunity, adaptive therapy, cellular barcoding, and combination therapy Art from #MathOnc23, Phoenix meeting Sign up here: mathematical-oncology.org/newsletter

Opening the 3rd and final day of #MathOnc23 is las plenary speaker @IBozic_ presenting, Measuring evolution in cancer.

Next up Sebastien Motsch presents, Self-organization in malignant brain tumors: emergence of oncostreams #MathOnc23

More details about #MathOnc23 schedule are now live on the website: mathematical-oncology.org/mathonc23 looking forward to Phoenix in a few weeks!

Next up is Reshmi Patel also from @UTCompOnco presenting, Predicting the response of I-SPY 2 breast cancer patients to treatment using a mechanism-based model and quantitative MRI data #MathOnc23

Next the penultimate talk of #MathOnc23 is by Einar Bjarki Gunnarsson from Jasmine Goo’s group presenting, Estimation of the rates of cell proliferation and phenotypic switching in cancer and implications for treatment.

Next up is Hugo Miniere from @UTCompOnco presenting, A mathematical model for predicting the spatio-temporal response of breast cancer cells treated with doxorubicin #MathOnc23

Next up is Pedro Lowenstein presenting, Spatio-temporal organization of brain tumors: from oncostreams to liquid crystals #MathOnc23

Next up is Nat Kendal-Freedman presenting, Modelling the Role of T-Regulatory Cells on Relapse Following Car T-Cell Therapy #MathOnc23

Next up is our very own @mathoncbro presenting, Applying the principles of convexity and concavity to guide treatment scheduling of ALK inhibitors in non-small cell lung cancer #MathOnc23 @mathonco @MoffittResearch

Next up is @colincess from @USCSysBio_Lab presents, Representation learning for tumor agent-based model parameter estimation #MathOnc23

#MathOnc23