Adam L. MacLean

This study shows that G1 phase cells exhibit stronger EMT gene expression and RNA velocity than cycling cells, suggesting cell cycle phase influences EMT and should not be regressed out.

The paper models the Mecom-Cdk6 regulatory network to show how IGF signaling and self-activation shape stem cell fate transitions through multistable landscapes.

The paper shows that clinically relevant leukemia phenotypes emerge at the macrostate level from single cell data, revealing how disease states aggregate from noisy single cell transcriptomes.

The paper shows HDAC1 inhibition in MDSCs increases Malat1, decreases pSTAT3, induces apoptosis and cell cycle arrest, and reduces T cell suppression.

This study uses a Drosophila model to show that RNA exosome mutations disrupt brain transcriptomes and cause neurodegeneration by altering rRNA processing.

The paper develops mathematical models and Bayesian inference methods to analyze tumor-immune dynamics in metastatic breast cancer, revealing immune suppressive parameters as key predictors of response to immunotherapy.

The paper investigates how myeloid-T cell interactions and B cell activation influence response to checkpoint inhibition in metastatic breast cancer using multi-omics and computational methods.

This paper introduces iterative logistic regression (iLR) to identify small sets of marker genes for subtle cell state changes, showing high accuracy in distinguishing cell subtypes and predicting therapeutic effects.

The paper investigates how network logic (additive vs multiplicative) influences EMT transitions and shows that AND logic better matches experimental data on miR-200 regulation.

The paper models EMT dynamics across cancers to identify genes associated with intermediate states and plasticity, finding SFN and NRG1 as key markers.

This paper introduces a framework for model discovery and selection in noisy biological systems using hybrid dynamical systems and neural networks, showing improved performance over existing methods.

The paper shows that clonal hematopoiesis later in life is driven by stem cell variation established before birth, using CpG methylation patterns in twins.

This study shows CXCL12 gradients around TMEM doorways promote tumor cell invasion and create immunosuppressive niches by affecting CD8+ T cell function and distribution.

The paper identifies gene expression changes in Nipbl+/- mouse embryos during gastrulation that link Nipbl-deficiency to birth defects via mesoderm lineage misallocation and Nanog overexpression.

The paper develops Bayesian methods for inferring dynamic parameters in single cells using gene expression and Ca2+ data, showing that transfer learning improves inference when cells are ordered by transcriptional similarity.

The paper identifies androgen receptor-mediated regulation of sexually dimorphic gene expression in the mouse kidney and finds limited conservation in human kidney.

This study introduces popInfer, a method for inferring gene regulatory networks from single-cell multiomics data, and applies it to uncover dynamic regulation of hematopoietic stem cell quiescence in mice under different dietary and aging conditions.

This study develops a mathematical model to analyze MDSC dynamics in metastatic niches, showing that MDSC delays and suppression capabilities significantly impact metastasis outcomes.

The paper develops a cell-cell communication model to explain lineage commitment in hematopoiesis by analyzing the GATA1-PU.1 network's role in fate decisions.

RVAgene is a recurrent variational autoencoder that models gene expression time series data, accurately reconstructing temporal profiles and enabling biological feature discovery.

The paper uses single-cell RNA sequencing to identify spatially distinct basal stem cell populations in human epidermis and shows their role in maintaining epidermal homeostasis through signaling and differentiation.

The paper models EMT-immune dynamics in epithelial cancer progression, identifying key factors that influence invasion-free survival and predicting new genes affecting cancer outcomes.

The paper uses single-cell transcriptomics to define basal cell states in skin homeostasis and wound healing, identifying distinct transcriptional states and their dynamics.

This paper introduces SoptSC, a method that integrates clustering, lineage inference, and communication network prediction in single-cell transcriptomics using a similarity matrix optimization framework.

This paper introduces an agent-based model of kidney branching morphogenesis and uses Bayesian inference to show that growth factor-dependent cell division is essential for branching.

The paper uses single-cell tracking to show that HSCs from infected mice exhibit heterogeneous movement patterns compared to controls, with increased persistence possibly aiding survival in altered niche environments.