Today on the mathematical oncology blog (a great resource to follow), Jesse has written a blog post highlighting the findings of our recent preprint on cancer systems immunology. Integrating theory and modeling with preclinical and clinical data enabled discovery of how combination therapy acts on intricate tumor-immune networks to mediate tumor response. Read the blog post here.
The Society for Mathematical Biology (SMB) held its annual meeting in Edmonton, where Xiaojun presented on methods for model discovery from noisy biological data (read the paper),
and Adam presented on our recent cancer systems immunology work leading to the discovery of mechanisms driving response to combination therapy (read the preprint) plus new work inferring patient- and site-specific tumor-immune dynamics to inform clinical decision making from RECIST data. Thanks to the hosts, the SMB community is strong!
Two new preprints are out this month from our lab in collaboration with the Evanthia Roussos Torres lab at USC Keck. In the first, Yingtong developed a new method, iterative logistic regression: iLR, to identify subtle effects in single-cell gene expression datasets. This led to the discovery of myeloid cell differentiation pathways as a target of entinostat, an epigenetic modulator. Read the preprint here.
In the second, Jesse co-led a large cancer systems immunology project with Edgar Gonzalez that integrated preclinical and clinical data with mathematical models to reveal how combination therapies act on tumor-immune interactions during breast cancer metastasis. We discovered an essential role for myeloid-derived suppressor cell and tumor-associated macrophage interactions with T cells, as well as B cell activation, in mediating the effects of therapy. Read the preprint here.
Anupam led a project on the role of logic in modeling gene regulatory network (GRN) dynamics. Through a detailed investigation into the role of logic in GRN models of EMT and developmental fate decisions, we discovered that choice of logic (AND vs OR) profoundly impacts cell fate. We go on to highlight — beyond the caution that must be exercised in choice of logic — that through experimental design it is possible to infer the logic of GRNs in vivo. Read the paper in Development.
Also, thanks to an initiative from the Company of Biologists, a pedunculate oak tree accompanies our paper in the Forest of Biologists.
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In a new paper led by MeiLu, published today in npj Systems Biology and Applications, we sought to define the extent to which paths through EMT intermediate states are shared and — by fitting a mathematical model of the cell state transition dynamics — were able to identify genes that mark for the *dynamic* regulation of EMT across cancer types and stimuli.
Adam attended the Systems Approaches to Cancer Biology 2025 conference at CU Anschutz, CO, where he presented recent work from collaboration with the Roussos Torres lab at USC Keck on awakening immune responses in metastatic tumor microenvironments. The science was excellent, as was the snow.
Our paper led by Xiaojun on Data-driven model discovery and model selection for noisy biological systems has been published PLOS Comp Biol. Mathematical models wielded skillfully can offer great insight into biological systems. The process of constructing models, however, is typically manual and labor-intensive. Data-driven model discovery provides an exciting alternative but dealing appropriately with the typical level of biological noise we observe in data is a challenge. Here we presents model discovery and model selection methods to infer models and evaluate the current limits of model discovery from noisy data.
Working together with the Joint Educational Project (JEP) at USC, we have developed a curriculum to enhance elementary school STEM education consisting of four lesson plans developed from the methods and results of MacLean Lab research, which are aligned with the Next Generation Science Standards for 4th grade. The curriculum and its implementation and assessment in K-12 schools partnered with USC has been published in
Connected Science Learning.
We ended the year with Oaxacan—American—British (the Christmas cracker hats) themed holiday lunch at Guelaguetza, with a special guest. Happy holidays from the MacLean lab!
In new work led by Anupam we reveal that choice of network logic in ODE modeling of gene regulatory networks plays an underappreciated and oversized role in determining cell fate outcomes. In application to tristable EMT landscapes, we show that it is critical to consider the choice of logic when constructing models.
By mapping out transition paths dictated by logic we provide methods with which to infer the logic used by gene circuits in live cells undergoing EMT through simple perturbation experiments. Read the paper on bioRxiv.