On chip reconstitution of complex biological systems: a bridge between biology and mathematical models

Modern biology exploits mathematical models to shed light on complex and multiparameter processes occurring from molecular to ecological level. The development of these models depends on the availability of robust data, which are not always easy to be collected, particularly at organism level. Organs on chip technology allows to reconstitute "under the microscope" the cross talk between different cell populations, thus becoming one of the best candidates to give access to spatial and temporal evolution of complex biological systems at organ and organism level. From this perspective the immune system may be regarded as computational living reactive system structured in a multitude of molecules, cells and organs that exert full functionality only through effective collaboration and communication in order to "compute the state of human body" [1,2] and react as needed. In this activity we present the results obtained with our on-chip experiments[3-7], summarized in fig. 1, and the associated automated tracking analysis of time-lapse videos, (see fig.2) as a versatile and smart system to assess the migratory/ response of immune cells to danger signals. These were applied in the context of onco-immunology target topics in order to determine and quantify parameters relevant to describe and classify the system behavior. With regard to immune cell reactions to the tumor we were able to: i) monitor and quantify the recruitment and interactions of immune cells in a controlled heterogenous cancer environment, ii) evaluate the immunogenic effects of anti-cancer therapeutic and immunomodulatory agents iii) visualize the effect of stromal components (endothelial, fibroblast) to modulate immune efficacy. The mathematical modelization consist of a reaction-diffusion-transport system with chemotaxis, thus it is able to describe birth and death processes, interaction with chemoattractant, interaction and competition between different cell species. The microfluidic chip is schematized as a network of channels connecting two boxes (the microfluidic chambers), then, following the ideas in recent papers [8], ad hoc transmission conditions were introduced to ensure the mass conservation. The parameters of the model, such as the velocity of different cell populations, the turning rates will be calibrated with observed data. We believe that the outcome correlation of the two on-chip and in-silico models with clinical and in vivo data will provide new insights to explore fundamental and applied modern biology, with potential capabilities of optimization of drug testing pipeline in clinical trials.