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New preprint: Modeling+inference of site-specific dynamics of metastasis

Our new preprint led by Jesse with input from Xiaojun and the Roussos Torres lab is out now: Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune parameters in metastatic breast cancer.

In which we present a modeling+inference framework to extrapolate beyond small, heterogeneous phase 1 cohorts and personalize tumor-immune models of response to triple combination therapy. We discover that control only of the immune suppressive parameters and not of the cytotoxic parameters is necessary to explain response. Read the paper here.

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SMB 2025

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!

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New preprints on subtle gene effects and cancer systems immunology

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.

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Paper out now: The underexplored impact of logic on gene regulatory networks

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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Paper out now: Model discovery from noisy biological dynamics

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.

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Paper out now: Teaching computational stem cell biology in a K-12 curriculum

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.

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