Biology subjects

Michael, C. T.

Publications and source records attributed to Michael, C. T..

4 recordsLinked to original sources

bioRxiv2026

A Comparison of Mechanisms Driving Lesion Outcomes during Lung Tumor and Tuberculosis Granuloma Formation

Small cell lung cancer (SCLC) and tuberculosis (TB) are both deadly diseases that present with spatially complex lung lesions. These lesions share many similarities, including several key spatial interactions between T cells and macrophages. Both SCLC and TB present with significant heterogeneity, both in terms of progression of disease and responses to treatment; current experimental methods have few tools to investigate the spatiotemporal evolution of these lesions within human lungs. We have applied our computational agent-based model, GranSim, to extensively study heterogeneity of TB granuloma scale formation, infection outcome and treatment in detail. We introduce TumorSim, an analogous agent-based model designed to understand the heterogeneity of SCLC lung tumors. TumorSim mechanistically and spatio-temporally captures immune-tumor interactions, many of which are well-studied in isolation, including cytokine-based recruitment of adaptive cells and PD1/PDL1-based inhibition of cytotoxic T-cell activity. Drawing from known lung immunology as well as literature on lung tumor responses, we define and explore a wide set of parameters to characterize TumorSim behavior using global sensitivity analysis. We compare factors that drive dynamics of both SCLC tumors and TB granulomas. As model validation, sensitivity analysis captures several well-known correlates of improved SCLC outcomes including macrophage-mediated cytotoxic T-cell recruitment. Surprisingly, both models predict a two-phase formation process occurring with an abrupt change in tumor/granuloma dynamics upon arrival of adaptive immune cells into the lung from lung-draining lymph nodes. Simulations suggest that while CCL5 is associated with improved tumor control later during tumor growth, CCL5 plays a pro-tumor role early during tumor growth by recruiting regulatory T cells. We also find that, similar to virtual TB granulomas, TumorSim tumors are increased in volume when immunosuppressive mechanisms outweigh pro-inflammatory responses. This novel tumor model can serve as a basis for future studies on lung tumor-immune dynamics to study both immunotherapeutics and anti-cancer drugs.

cancer biology↗

bioRxiv2026

Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure

Tuberculosis (TB) remains a global health concern, as Mycobacterium tuberculosis (Mtb) currently infects a quarter of the worlds population. Though many TB patients sterilize infection with short-course treatment, regimens shorter than 4 months risk post-treatment, potentially facilitating drug resistance. Granulomas are spatially-heterogeneous hallmark immune structures that form during TB, comprising immune cells and caseum, necrotic tissue that can trap Mtb in a non-replicating state. Two mechanisms causing relapse have been hypothesized: persistence, where treatment kills all replicating Mtb, and relapse follows once Mtb trapped within caseum returns to a replicative niche; and threshold, where replicating Mtb remain alive below detectable levels. Typically, clinical relapse is described as TB recurrence <2 years after a misdiagnosis of cure upon treatment completion (MDxC). Comparatively, many experimental models cannot screen for cure and examine relapse [~]2-months after treatment completion. Capacity to untangle these considerations in vivo are limited. Here, we examine the impact of study design (e.g., cure screening) on mechanisms underpinning reported instances of relapse using our computational model capturing whole-host Mtb infection dynamics, HostSim. Simulations uncover rates of reported relapse depending on whether hosts are screened for cure upon treatment completion. If not screened, then relapse is likely driven by incomplete sterilization of replicating bacteria; whereas cure-screened relapse is most likely to be caused by gradual expansion of non-replicating Mtb from within caseum into cellular areas of a granuloma. This suggests that TB patients that relapse after MDxC may best be treated with caseum-penetrating antibiotics such as rifamycin-class antibiotics. ImportanceIncomplete treatment of TB leads to risks relapse, which may occur years later. The threat of relapse is the main reason TB treatment takes 4-9 months. Predictors of relapse are not well-defined given variability in technical definitions of relapse and nuances of study designs. Here, we simulate both clinical and experimental relapse studies, including multiple diagnostic tests and relapse definitions, using biologically-based computation. We find that two hypothesized types of relapse that each potentially require a different treatment strategy are simultaneously at play. Simulations suggest that relapse after a "cure" diagnosis (most clinical studies) is caused by reactivation of non-replicating bacteria hidden from treatment within necrotic granuloma tissue or other sites, whereas experiments that cannot test for cure post-treatment are likely to report relapse caused by incomplete sterilization of replicating Mtb. Predictions depend on the currently unclear relationship between bacterial burden and clinical symptoms.

systems biology↗

bioRxiv2025

Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden

Pulmonary infection after inhalation of Mycobacterium tuberculosis (Mtb) causes tuberculosis (TB). TB presents with lung granulomas - complex spheroidal structures composed of immune cells and bacteria. Granulomas often have centralized caseum (necrotic tissue) where mycobacteria are quarantined, complicating and prolonging multi-antibiotic regimens. Determining which antibiotic regimens are optimal for reducing treatment time and toxicity is a goal of recent TB eradication campaigns. Clinical trials are expensive and challenging, making it difficult to untangle which host-pathogen interactions drive the heterogeneous infection and treatment outcomes observed at between-host and within-host scales. To determine responses to antibiotic regimens, we simulate treatments in HostSim, our whole-host mechanistic, multi-scale computational model of Mtb-infection. HostSim tracks dynamics of pulmonary Mtb-infection over molecular, cellular, tissue, organ, and whole-host scales. We create a heterogenous virtual cohort, comprising distinct hosts, for virtual clinical trials. We represent drug treatments by newly-integrating pharmacokinetics / pharmacodynamics into HostSim, simulating treatment with commonly-prescribed TB antibiotic regimens (e.g., HRZE or BPaL). Our approach allows us to identify both (1) which hosts/granulomas most improve with treatment, and (2) which mechanisms influence outcome heterogeneity. By tracking experimental and clinical measurements, we virtually recreate several drug rankings from literature. We find that many methods of ranking treatment efficacy are strongly influenced by the definition of improvement used and, in some cases, the detection threshold of CFU. Other rankings depend on initial bacterial burden of hosts/granulomas. Our work suggests that metrics for regimen optimality may be orthogonal, which could explain seemingly-contradictory findings from prior studies.

systems biology↗

bioRxiv2023

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

AbstractAlthough infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.

systems biology↗