Morgan Craig

SHAPPE reconstructs spatial point patterns from environmental constraints, improving ABM initialization and reducing cell interaction errors in glioblastoma samples.

The paper explores the balance between model simplicity and complexity in mathematical models of ovarian cancer growth and treatment response, showing that statistical metrics alone are insufficient for capturing biological mechanisms.

This study shows that dietary saturated fatty acid chain length affects B-cell lymphoma metabolism and progression, with medium-chain fatty acids promoting tumor growth and long-chain fatty acids restricting it.

This study shows that the H3-K27M mutation alters hematopoietic stem cell dynamics and erythroid differentiation, increasing blood cell numbers despite impaired maturation.

DiffDose uses automatic differentiation to optimize drug dosing regimens by turning mechanistic PK/PD and QSP models into gradient-based tools for personalized treatment design.

This study uses a mechanistic immunological model to show how immunodeficiencies shape viral evolution during primary infections, with varying effects on mutation frequency and speed depending on the type of infection and immune deficiency.

This paper introduces TM-RWFS, a method to generate heterogeneity in mechanistic models by constraining trajectories between data bounds, showing it efficiently reproduces biological variability.

The paper introduces UNMASC, a mathematical model to analyze non-monotonic cytokine interactions in T cell differentiation, showing synergistic and antagonistic effects in cytokine combinations.

This study uses virtual patient cohorts to model immune response differences in cancer and immunosuppressed COVID-19 patients, identifying key immune dysregulation factors.

The paper develops a mathematical model of glioblastoma growth and immune interactions, showing that targeting TAMs to enhance their antitumoral properties could significantly improve treatment outcomes.

This study shows that immunosuppressed patients with hematologic cancers develop viral populations with immune escape mutations, suggesting the need for combination therapies.

The paper presents computational tools for analyzing SARS-CoV-2 genetic data, showing that haplotype networks and unsupervised learning can reveal lineage emergence and population dynamics.

The paper uses network and data science techniques to analyze single-cell gene expression data from GBM and GSCs, identifying key molecular regulators and their interactions that differentiate pediatric and adult GBMs.

This paper uses mathematical modeling to analyze the relationship between monocyte and neutrophil levels during chemotherapy and shows that monocytopenia precedes neutropenia, informing G-CSF dosing strategies.

Open-source agent-based PhysiCell model of SARS-CoV-2 infection in lung epithelium; version 5 clears virus in about 6 days in simulation, and delayed T cell arrival increases epithelial loss.