Bioinformatics Computational Biology Molecular Medicine

Role of Bioinformatics in Personalized Medicine

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Personalized medicine is medical care built-in to each patient’s genetic makeup. It means bulk, assembly line-like medicine comes to an end, and medicine designed to deliver maximum benefit to the individual becomes the norm. This would eradicate a lot of bad side effects related with standard treatments now, reduce or eliminate allergic reactions, reduce the cost of healthcare, and reduce patient suffering through more effective treatments.

In order to actually perform personalized medicine, each patient’s genome must first be translated into digital data which is then processed, stored and retrieved as needed. Thus the triple play of genomics, bioinformatics and personalized medicine is necessary. 

Extensive molecular biological data on the patient are increasingly included in diagnosis and treatment. This trend is based on the development of targeted drugs and accompanying diagnostics, which serve the purpose of providing advanced evidence that the medication promises therapy success for the patient. According to this concept drugs are often given in combination. The sizes of patient groups for which a given therapy out of many possible alternatives can be expected to be successful are quite limited. The relationship between the molecular data pertaining to a patient and their disease phenotype are complex and cannot be determined manually. Thus, computer-based bioinformatics methods play a central role in interpreting the molecular data and as an instrument for providing recommendations for the practicing physician. Bioinformatics is an essential component in basic research, in the development of new concepts for diagnosis and therapy as well as in clinical practice, in which these concepts are used to treat patients. 

  • New methods are needed in four areas to realize the potential of personalized medicine: 
  • Processing large-scale robust genomic data
  • Interpreting the functional effect and the impact of genomic variation
  • Integrating systems data to relate complex genetic interactions with phenotypes
  • Translating these discoveries into medical practice

Two methods stand out, the randomized algorithm and computer assisted drug design (CADD).

Precision medicine requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice depends strongly on the availability of an efficient bioinformatics system that helps in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of;

  • Warranting the integration and the traceability of data
  • Ensuring the correct processing and analyses of genomic data
  • Applying well-defined and reproducible procedures for workflow management and decision-making 

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