Biotechnology is an area of great innovations that promises to have deep impact on everyday life thanks to profound changes in biology, medicine, and health care. This article will span from the description of the biochemical principles of molecular biology to the definition of the physics that supports the technology and to the devices and algorithms necessary to observe molecular events in a controlled, portable, and highly parallel manner.

Regenerative medicine is a novel clinical branch aiming at the cure of diseases by replacement of damaged tissues. The crucial use of stem cells makes this area rich of challenges, given the poorly understood mechanisms of differentiation. One highly needed and yet unavailable technology should allow us to monitor the exact (metabolic) state of stem cells differentiation to maximize the effectiveness of their implant in vivo. This is challenged by the fact that not all relevant metabolites in stem cells differentiation are known and not all metabolites can currently be continuously monitored.

Background: Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals.

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyper-spectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400-2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400-750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021.

Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes and experiments. This "biclustering "paradigm aims at discovering clusters that consist of a subset of the genes showing a coherent expression pattern over a subset of conditions. The pClustering approach is a technique that belongs to this paradigm. Despite many theoretical advantages, this technique has been rarely applied to actual gene expression data analysis.

The importance of circuits and systems for high-throughput biological data acquisition in biomedical research are discussed. High-throughput biological data acquisition and processing technologies have shifted the focus of biological research from the the traditional experimental science to that of information science. Powerful computation and communication means can be applied to a very large amount of apparently incoherent data coming from biomedical research.

We describe preliminary results from a multiobjective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on highend parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package PSCToolkit to exploit multi-threaded parallelism at the node level on multi-core processors.