Leveraging cell research has become integral to the operation of genomics, screening, biomarker discovery, and biotherapeutics development rules around the world.
- Gene and protein expression
- Functional assays based on target molecules
- Primary and transfected cells
All these areas are crucial for bioscience disciplines. By automating these processes, researchers achieve consistent results and regain hours that can be spent on more intensive aspects of the process.
There are three common types of artificial cloning. The first one, reproductive cloning, is the one that sparks the most scientific and ethical debate. It’s also one of the most exciting fields in gene cloning research since the possibilities are virtually limitless.
The second type is therapeutic cloning. This process reproduces embryonic stem cells, with the goal of recreating injured or diseased tissue. If successful, experiments in this field could be revolutionary for reversing injury or degenerative disease.
The third type of artificial cloning is gene cloning. This process reproduces genes and involves inserting a gene from one organism into a carrier. These carriers are referred to as vectors, and often include viruses, bacteria, and plasmids. After the gene is placed in the vector, the vector is exposed to manipulated lab conditions that cause it to multiply on repeat. This produces many, many identical copies of the gene initially inserted into the vector.
Transfection is a scientific process that exposes protein expression and gene function. In most cases, labs leverage this process to understand the following:
- Gene regulation and function
- Protein production and expression
- Analyzing mutations
- Biochemical mapping
At its core, transfection is the process of introducing foreign genes or materials into the cell’s pre-existing DNA. By introducing eukaryotic cells to nucleic acids, scientists can study genes in a controlled environment.
Assay-based technology, paired with automated selection methods, allows researchers to keep DNA expressions stable. This allows the process to be mostly automated, rather than spending precious manhours on repetitive tasks that our systems can do. By reducing human error, giving time back, and allowing researchers to focus on the work that really matters to them, automation is the way to go.