<p dir="ltr">Tuberculosis (TB) remains a global public health crisis, causing over 10 million new infections and 1.3 million deaths in 2022 alone. TB is caused by <i>Mycobacterium tuberculosis </i>(<i>Mtb</i>), which initiates heterogeneous pathology in the lungs, including granulomas and cavities. Granulomas are organized structures of immune cells, traditionally thought to contain bacteria. Cavities are pathological spaces caused by the destruction of extracellular matrix (ECM), which can worsen disease outcomes and cause long-lasting pulmonary impairment.<i> In vitro </i>methods are commonly used to study host-pathogen interactions in <i>Mtb</i> infection, and recent developments have led to models that represent the TB granuloma environment more closely than traditional cell culture. These advances include the development of 3D models and the inclusion of physiological ECM components like collagen. Increasing complexity has been accomplished in a piece-wise manner – minimally necessary components are included to minimize cost while maintaining throughput and tractability. This creates a need for tools to analyze these systems and, more importantly, integrate the independent data created. We developed an agent-based model to characterize multiple <i>in vitro</i> models of TB and apply it to 1) separate the contributions of dimension and structure to bacterial control in granuloma-like spheroids and 2) explore how the interactions of collagen and matrix metalloproteinases (MMP) contribute to clinically relevant outputs such as bacterial load and ECM destruction. The model provides insights into the role of granuloma structure and the conflicting results of MMP inhibition, generating new hypotheses to be tested in tandem with <i>in vitro</i> models.</p>

<p dir="ltr">Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>), and human immunodeficiency virus-1 (HIV) are major public health concerns, individually and in combination. The status of the host immune system, previous <i>Mtb</i> infection and HIV-mediated T cell exhaustion, can have significant impacts on immune dynamics during reinfection. Individuals with asymptomatic latent TB infection (LTBI) may be protected against <i>Mtb </i>reinfection, as demonstrated by animal and <i>in vitro </i>studies. However, the underlying dynamics and protective mechanisms of LTBI are poorly understood. In HIV, long-term infection in children and associated T cell exhaustion leads to weakened immune responses to HIV reinfection. The complexity of these infections, particularly in the context of the heightened vulnerability of HIV+ individuals to TB, underscores the need for novel investigative approaches to study host-pathogen and pathogen-pathogen interactions. To this, we have developed an agent-based model (ABM) as a mechanistic computational tool to simulate the immune response to <i>Mtb </i>and HIV, separately and during coinfection. Our ABM integrates clinical and experimental data; simulates immune cell dynamics between macrophages, CD4+ and CD8+ T cells; and produces emergent granuloma-like structures – a critical response to <i>Mtb</i>. This <i>in silico</i> approach allows us to efficiently explore host-pathogen interactions and their clinical implications. By unraveling the complex interplay of immune cell activation, T cell exhaustion, and pathogen dynamics, our model offers insights that could guide the development of targeted therapies. By quantifying the multifaceted nature of these diseases and their interactions, we highlight the potential of computational approaches in understanding and treating complex diseases, individually and in combination.</p>