This chapter summarizes the current biomaterials and associated technologies used to mimic and characterize the tumor microenvironment (TME) for developing preclinical therapeutics. Research in conventional 2D cancer models systematically fails to provide physiological significance due to their discrepancy with diseased tissue’s native complexity and dynamic nature. The recent developments in biomaterials and microfabrication have enabled the popularization of 3D models, displacing the traditional use of Petri dishes and microscope slides to bioprinters or microfluidic devices. These technologies allow us to gather large amounts of time-dependent information on tissue-tissue, tissue-cell, and cell-cell interactions, fluid flows, and biomechanical cues at the cellular level that were inaccessible by traditional methods. Lorlatinib solubility dmso In addition, the wave of new tools producing unprecedented amounts of data is also triggering a new revolution in the development and use of new tools for analysis, interpretation, and prediction, fueled by the concurrent development of artificial intelligence. Together, all these advances are crystalizing a new era for biomedical engineering characterized by high-throughput experiments and high-quality data.Furthermore, this new detailed understanding of disease and its multifaceted characteristics is enabling the long searched transition to personalized medicine.Here we outline the various biomaterials used to mimic the extracellular matrix (ECM) and redesign the tumor microenvironment, providing a comprehensive overview of cancer research’s state of the art and future.The tumor microenvironment (TME) is like the Referee of a soccer match who has constant eyes on the activity of all players, such as cells, acellular stroma components, and signaling molecules for the successful completion of the game, that is, tumorigenesis. The cooperation among all the “team members” determines the characteristics of tumor, such as the hypoxic and acidic niche, stiffer mechanical properties, or dilated vasculature. Like in soccer, each TME is different. This heterogeneity makes it challenging to fully understand the intratumor dynamics, particularly among different tumor subpopulations and their role in therapeutic response or resistance. Further, during metastasis, tumor cells can disseminate to a secondary organ, a critical event responsible for approximately 90% of the deaths in cancer patients. The recapitulation of the rapidly changing TME in the laboratory is crucial to improve patients’ prognosis for unraveling key mechanisms of tumorigenesis and developing better drugs. Hence, in this chapter, we provide an overview of the characteristic features of the TME and how to model them, followed by a brief description of the limitations of existing in vitro platforms. Finally, various attempts at simulating the TME using microfluidic platforms are highlighted. The chapter ends with the concerns that need to be addressed for designing more realistic and predictive tumor-on-a-chip platforms.Despite the significant amount of resources invested, cancer remains a considerable burden in our modern society and a leading cause of death. There is still a lack of knowledge about the mechanistic determinants of the disease, the mechanism of action of drugs, and the process of tumor relapse. Current methodologies to study all these events fail to provide accurate information, threatening the prognosis of cancer patients. This failure is due to the inadequate procedure in how tumorigenesis is studied and how drug discovery and screening are currently made. Traditionally, they both rely on seeding cells on static flat cultures and on the immunolabelling of cellular structures, which are usually limited in their ability to reproduce the complexity of the native cellular habitat and provide quantitative data. Similarly, more complex animal models are employed for-unsuccessfully-mimicking the human physiology and evaluating the etiology of the disease or the efficacy/toxicity of pharmacological compounds. Despite some breakthroughs and success obtained in understanding the disease and developing novel therapeutic approaches, cancer still kills millions of people worldwide, remaining a global healthcare problem with a high social and economic impact. There is a need for novel integrative methodologies and technologies capable of providing valuable readouts. In this regard, the combination of microfluidics technology with miniaturized biosensors offers unprecedented advantages to accelerate the development of drugs. This integrated technology have the potential to unravel the key pathophysiological processes of cancer progression and metastasis, overcoming the existing gap on in vitro predictive platforms and in vivo model systems. Herein, we discuss how this combination may boost the field of cancer theranostics and drug discovery/screening toward more precise devices with clinical relevance.Biosensors represent a powerful analytical tool for analyzing biomolecular interactions with the potential to achieve real-time quantitative analysis with high accuracy using low sample volumes, minimum sample pretreatment with high potential for the development of in situ and highly integrated monitoring platforms. Considering these advantages, their use in cell-culture systems has increased over the last few years. Between the different technologies for cell culture, organs-on-a-chip (OOCs) represent a novel technology that tries to mimic an organ’s functionality by combining tissue engineering/organoid with microfluidics. Although there are still challenges to achieving OOC models with high organ mimicking relevance, these devices can offer effective models for drug treatment development by identifying drug targets, screening toxicity, and determining the potential effects of drugs in living beings. Consequently, in the future, we might replace animal studies by offering more ethical test models. Considering the relevance that different physiological and biochemical parameters have in the correct functionality of cells, sensing and biosensing platforms can offer an effective way for the real-time monitoring of physiological parameters and, in our opinion, more relevant, the secretion of biomarkers such as cytokines, growth factors, and others related with the influence of drugs or other types of stimulus in cell metabolism. Keeping this concept in mind, in this chapter, we focus on describing the potential use of sensors and biosensors in OOC devices to achieve fully integrated platforms that monitor physiological parameters and cell metabolism.Biomolecular gradients are widely present in multiple biological processes. Historically they were reproduced in vitro by using micropipettes, Boyden and Zigmond chambers, or hydrogels. Despite the great utility of these setups in the study of gradient-related problems such as chemotaxis, they face limitations when trying to translate more complex in vivo-like scenarios to in vitro systems. In the last 20 years, the advances in manufacturing of micromechanical systems (MEMS) had opened the possibility of applying this technology to biology (BioMEMS). In particular, microfluidics has proven extremely efficient in setting-up biomolecular gradients which are stable, controllable, reproducible and at length scales that are relevant to cells. In this chapter, we give an overview of different methods to generate molecular gradients using microfluidics, then we discuss the different steps of the pipeline to fabricate a gradient generator microfluidic device, and at the end, we show an application example of the fabrication of a microfluidic device that can be used to generate a surface-bound biomolecular gradient.Biosensors have a great impact on our society to enhance the life quality, playing an important role in the development of Point-of-Care (POC) technologies for rapid diagnostics, and monitoring of disease progression. COVID-19 rapid antigen tests, home pregnancy tests, and glucose monitoring sensors represent three examples of successful biosensor POC devices. Biosensors have extensively been used in applications related to the control of diseases, food quality and safety, and environment quality. They can provide great specificity and portability at significantly reduced costs. In this chapter are described the fundamentals of biosensors including the working principles, general configurations, performance factors, and their classifications according to the type of bioreceptors and transducers. It is also briefly illustrated the general strategies applied to immobilize biorecognition elements on the transducer surface for the construction of biosensors. Moreover, the principal detection methods used in biosensors are described, giving special emphasis on optical, electrochemical, and mass-based methods. Finally, the challenges for biosensing in real applications are addressed at the end of this chapter.The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.Demersal fisheries are one of the top anthropic stressors in marine environments. In the long term, some species are more vulnerable to fishery impacts than others, which can lead to permanent changes on the food web. The trophic relationships between predator and prey constitute the food web and it represents a network of the energy channels in an ecosystem. In turn, the network structure influences ecosystem diversity and stability. The first aim of this study was to describe for the first time the food web of the San Jorge Gulf (Patagonia Argentina) with high resolution, i.e. to the species level when information is available. The San Jorge Gulf was subject to intense fisheries thus our second aim is to analyse the food web structure with and without fishery to evaluate if the bottom-trawl industrial fishery altered the network structure and stability. We used several network metrics like mean trophic level, omnivory, modularity and quasi-sign stability. We included these metrics because they are related to stability and can be evaluated using predator diets that can weight the links between predators and prey. The network presented 165 species organized in almost five trophic levels. The inclusion of a fishery node adds 69 new trophic links. All weighted and unweighted metrics showed differences between the two networks, reflecting a decrease in stability when fishery was included in the system. Thus, our results suggested a probable change of state of the system. The observed changes in species abundances since the fishery was established, could represent the state change predicted by network analysis. Our results suggests that changes in the stability of food webs can be used to evaluate the impacts of human activity on ecosystems.