Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

Breast cancer is a highly complex, heterogeneous, and multifactorial disease that poses challenges for rapid and efficient treatment and development of personalized therapy. Here, we describe a rapid and reliable method to generate threedimensional (3D) tumor spheroids in vitro that recapitulate an individual patient's tumor for testing treatments. By employing droplet microfluidics and scaffold materials, tumor cells were encapsulated into a large number of Matrigel-in-oil droplets with precise control over cell numbers and components per droplet. After removal of the oil, large numbers of uniform tumor spheroids were formed within a few hours via Matrigel-supported cell selfassembly. Our microfluidic technique produces uniform-sized tumor spheroids in less than 1 day. This method was used to reproducibly and rapidly generate uniform-sized tumor spheroids derived from patients' breast tumor tissues. As a proof-of-concept application, this method was used to quickly evaluate cancer treatments. We demonstrated that our microfluidic patient-derived tumor cultures not only preserve the genetic characteristics of the original tumor tissue but also provide heterogeneous responses to targeted therapies within 2 days. We believe this method will enable a timely and reliable 3D in vitro culture model, which may be applicable to personalized treatment prediction, drug discovery, and toxicity testing.

The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and vulnerabilities, to provide mechanistic frameworks for the interpretation of experimental and clinical data and to suggest drug and therapeutic targets and to design optimized personalized interventions all require the development of detailed predictive quantitative models of all aspects of COVID-19. Many of these models will require the use of common submodels describing specific aspects of infection ( e.g. , viral replication) but combine them in novel configurations. As a contribution to this development and as a proof-of-concept for some components of these models, we present a multi-layered 2D multiscale, multi-cell model and associated computer simulations of the infection of epithelial tissue by a virus, the proliferation and spread of the virus, the cellular immune response and tissue damage. Our initial, proof-of-concept model is built of modular components to allow it to be easily extended and adapted to describe specific viral infections, tissue types and immune responses. Immediately after a cell becomes infected, the virus replicates inside the cell. After an eclipse period, the infected cells start shedding diffusing infectious virus, infecting nearby cells, and secretes a short-diffusing cytokine signal. Neighboring cells can take up the diffusing extracellular virus and become infected. The cytokine signal calls for immune cells from a simple model of the systemic immune response. These immune cells chemotax and activate within the tissue in response to the cytokine profile. Activated immune cells can kill underlying epithelial cells directly or by secreting a short-diffusible toxic chemical. Infected cells can also die by apoptosis due to the stress of viral replication. We do not include direct cytokine mediated protective factors in the tissue or distinguish the complexity of the immune response in this simple model. Despite unrealistically fast viral production and immune response, the current base model allows us to define three parameter regimes, where the immune system rapidly controls the virus, where it controls the virus after extensive tissue damage, and where the virus escapes control and infects and kills all / cells. We can simulate a number of drug therapy concepts, like delayed rate of production of viral RNAs, reduced viral entry, and higher and lower levels of immune response, which we demonstrate with simulation results of parameter sweeps of select model parameters. From results of these sweeps, we found that successful containment of infection in simulation directly relates to inhibited viral internalization and rapid immune cell recruitment, while spread of infection occurs in simulations with fast viral internalization and slower immune response. In contrast to other simulations of viral infection, our simulated tissue demonstrates...

Bioprinting of vascular tissues holds great potential in tissue engineering and regenerative medicine. However, challenges remain in fabricating biocompatible and versatile scaffolds for the rapid engineering of vascular tissues and vascularized organs. Here, novel bioink‐enabled microfluidic printing of tunable hollow microfibers is reported for the rapid formation of blood vessels. By compositing biomaterials including sodium alginate, gelatin methacrylate, and glycidyl‐methacrylate silk fibroin, a novel composite bioink with excellent printability and biocompatibility is prepared. This composite bioink can be printed into hollow microfibers with tunable dimensions using a microfluidic co‐axial printing device. After seeding human umbilical vein endothelial cells into the hollow chambers via a microfluidic perfusion device, these cells can adhere to, grow, proliferate, and then cover the internal surface of the printed hollow scaffolds to form vessel‐like tissue structures within 3 days. By combining the unique composite bioink, microfluidic printing of vascular scaffolds, and microfluidic cell seeding and culturing, the strategy can rapidly fabricate vascular‐like tissue structures with high viability and tunable dimensions. The presented method may engineer in vitro vasculatures for the broad applications in basic research and translational medicine including in vitro disease models, tissue microcirculation, and tissue transplantation.

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