The immune system consists of a collection of cells with a high ability to migrate that work together to remove harmful foreign material from the body. Each immune cell can migrate between tissues, fulfilling specific functions in different microenvironments. However, this immune-surveillance response is not very effective in those tissues with a high non-physiological stiffness and a significant level of residual stresses, which are characteristics of solid tumors. Understanding the mechanisms that govern the cellular immune response to solid tumors is crucial to strengthen the development of novel immunotherapies. ICoMICS aims to develop a novel predictive modeling platform to investigate how therapeutic immune cells (TICs) sense, migrate and interact with cancerous cells and with the tumor microenvironment (TME). This platform will be built on two key pillars: in-vitro 3D tumor organoids and multicellular simulations, which will be combined and integrated by means of Bayesian optimization and machine learning techniques. On the one hand, cell culture microfluidic chips will be microfabricated, allowing continuous perfusion of chemical modulators through hydrogels (including decellularized matrices from murine stroma) inhabited by human tumor cells arranged to recreate 3D solid tumor organoids. On the other hand, an agent-based model will be developed to simulate cells as deformable objects, including cell-cell and cell-matrix interactions, combined with a continuum approach to model matrix mechanics and chemical reactions of cells, such as reactive oxygen species (ROS) and nutrients diffusion. Finally, ICoMICS will originally develop two innovative mechanistic-based immunotherapies. First, TICs will be subjected to high strains in micro-channels to induce them higher migration capacity. Second, TICs will be clustered as bio-bots, to ensure that they have improved functionality. All this research will be applied to 3 main solid tumors: lung, liver and pancreas.

Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data. We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.

The high prevalence of childhood obesity in several parts of the world indicates a worrying future health scenario, making prevention a high priority. This prevention should focus on diet and physical activity. Regarding food, the beginning of establishing eating habits and preferences occurs in childhood, being necessary that this prevention and treatment is individualized. In this context, precision nutrition offers a new paradigm for health, where the focus is to prevention and treatment strategies based on an individual's unique characteristics. Metabolomics (which is in the omics group - transcriptomics, genomics and proteomics) is being considered an important aspect in precision nutrition, reflecting more realistically effect of food on health and disease. Therefore, the aim of this project is to identify, the metabolomic profile in children aged 3-6 years, and through a longitudinal intervention, if eating behaviour and physical inactivity modify the profile of metabolites that can lead to obesity. The data for this proposal will be derived from a multicenter, parallel, randomized and controlled clinical trial that will be carried out with a cohort of children, followed initially by one year of intervention, with the intention of 10 years in total. The intervention will be based on healthy eating and physical activity. Metabolomics data, anthropometric, dietary, socioeconomic, behavioural, metabolic, biochemical and food environment variables will be collected. Similar studies to this are not available in the literature, and have the potential to increase knowledge about nutritional metabolomics, especially in early childhood and serve as a database for other studies with this population. This can reduce childhood obesity, minimize nutritional and metabolic risks in childhood and reduce the economic, environmental and social impact this disease.