Abstract Macrophages in Greek means “big eaters" are powerful cellular components of innate immunity. They play a pivotal role in immune defense by ‘eating’ pathogens, dead or cancerous cells. They also contribute to tissue homeostasis, development and repair. When doing their job, macrophages react to their surroundings and trigger acute inflammation to resolve the problems. They do so by assuming one of the two states that have been widely recognized, i.e., immunoreactive (proinflammatory) and immunotolerant (a.k.a, M1 and M2, respectively). While finite degrees of reactivity and tolerance are desirable in physiology, excess of either state is undesirable and invariably associated with disease pathogenesis (i.e., the Goldilocks conundrum). For example, hyperreactivity is recognized as the root cause of tissue injury in a wide array of diseases (colitis, sepsis, NASH) and hypertolerance is a common determinant that drives most, if not all chronic diseases that are incurable, e.g., cancers. Consensus on the definition of these physiologic and pathologic macrophage states has not been reached, perhaps because of 4 major challenges: heterogeneity, biological robustness, the temporal evolution of the network, and artifacts (tremendous plasticity of macrophages as they drift rapidly when isolated from tissues). We have used a novel computational methodology, Boolean Implication Network [Sahoo 2008], to analyze pooled human macrophage gene expression datasets. This method, which identifies asymmetric gene expression patterns, blurs noise (heterogeneity/artifacts) but reveals a temporal model of events that is invariably seen across all datasets. The analysis revealed hitherto unknown continuum transition states between reactive to tolerant states along five paths; machine-learning identified one of them as the major path which subsequently stood the rigorous test/validation on multiple publicly available transcriptomic datasets, across species (mouse and human), macrophage subtypes and disease states. Most importantly, unlike other commonly used gene cluster signatures, the Boolean path can prognosticate outcomes across diverse diseases. Preliminary validation studies on a genetic model confirm that the path could be exploited for modulating macrophage polarization by altering LPS/TLR4 responses. We will now interrogate the impact of these discoveries using an iterative approach, i.e., model-driven experimentation and experiment-driven model refinement, through three aims: Unravel the importance of novel molecular drivers in the newly identified gene signatures of macrophage polarization using semi-HTP chemical/genetic screens on murine and human monocyte-derived macrophages (Aim 1), in murine disease models of hyperreactivity and hypertolerance (Aim 2) and in “Humanoids”, i.e., human organoid-based microbe/immune cells co-culture models (“gut-in-a-dish”; Aim 3). Although our focus is gastrointestinal infection and inflammation, the findings will defi...