Synthetic biology is a developing science at the turn of biology, mathematics, computer science, physics and chemistry. It is based on the progress made in recent decades in the field of biotechnology, in particular sequencing and Gene Synthesis, as well as on the methods of engineering sciences, for the deployment of the procedure at the subsequent stages of mathematical modeling, computer modeling, implementation of biological and validation.

    What is Oligonucleotide Synthesis. Using this methodological approach, synthetic biology designs and creates more or less complex biological systems, reproducing the behavior of natural systems or with new functions that are absent in nature or unknown. These new systems will allow progress in life knowledge and should lead to widespread application. Synthetic biology is a new science that differs from classical genetic engineering in its systematic methodology, original in biology, the complexity of the systems it creates in comparison with one or more genes carried by genetic engineering, and the construction of DNA sequences.

    Synthetic biology currently combines three different scientific approaches and uses the Gene Synthesis Kit for such purposes: the construction of elementary DNA compounds to be assembled, the synthesis of complete genomes, and the propagation of vestigial cells. Synthetic biology applications are emerging, especially in the medical field. They also relate to energy, materials, agri-food products or the environment and should represent a significant market. The United States is showing progress in the field of synthetic biology, but Europe, and France in particular, is clearly demonstrating a desire to scientifically establish itself in this area. In France, it represents one of the largest groups of laboratories and companies specializing in synthetic biology.

    In addition, traditional genetic analyzes – a targeted search for one or more specific variants – have also been improved and are still widely used today. Analytical “kits” are usually used for specific gene pools (panels) associated with a specific disease. For example, the diagnosis of most rare diseases or tumor genes is usually done using this targeted sequencing. However, it is important to note that, regardless of the methodology used, identifying options is not enough to diagnose a hereditary disease; These are results that should always be interpreted in accordance with the clinical context and family information.

    Biotechnology will also play a large role in “personalized medicine,” which can be defined as an approach to understanding, treating, and / or preventing diseases based on a person’s genetic characteristics and interactions with them. Environment. Some of these guidelines have ethical implications that require discussion at the community level. These aspects are not considered in this study. In the longer term, it can replace the vast majority of genetic tests currently being performed, but it will also depend on the dynamics of the cost of these tests. Indeed, for some of the aforementioned features, well-established technologies with lower costs already exist, for which biotechnology will probably not be competitive for a long time.

    For example, for concomitant diagnostics, NGS technology remains competitive, and gene synthesis is likely to become interesting or effective only when prices have fallen significantly. From the point of view of clinical diagnosis, until recently, genetic testing was mainly used for rare diseases. But they are increasingly required for other medical specialties to identify the genetic component of common diseases, such as cardiovascular diseases.

    The three sequencing technologies that currently dominate the market are distinguished from each other by the chemical compositions that make them up. However, they are all divided into four main stages: preparing libraries containing the PCR amplification step, sequencing reaction cycles, obtaining images after each of these cycles to determine the corresponding nucleotide, then analyzing the data. These new generations of machines have the advantage of being able to analyze large, high-resolution genomes by parallelizing reactions.