There has been a surge in use of computers for studying the mechanisms and functions of cell biology. Computational biology is the use of computers to test hypotheses of biology. Scientists are striving hard to understand the various ways to decipher the interactions of drug on the body and vice-a-versa, also called as pharmacokinetics and pharmacodynamics respectively. In addition rise in drug failures in late stage of clinical trials have compelled the pharmaceutical companies to identify new modes of drug discovery. Transparency Market Research (http://www.transparencymarketresearch.com/), a leading U.S.-based Market Research firm, analyses the market for computational biology and predicts a compounded annual growth rate of 21.3% for the period 2012 to 2018. Computational biology assists in curbing these problems in initial stages since it is based on predictive models.
Over the decade, computational biology field has evolved and is being considered crucial especially for pediatric clinical research. Major challenges faced by companies in this type of research are stringent regulatory guidelines, getting consent, limited sampling volumes and invasiveness of these procedures for assessment which leads to pain and blood loss through sampling. These challenges are now being well thought-out to resolve through use of modeling and simulation techniques. Apart from this, the study of computational biology has been measured valuable for orphan diseases.
Computational biology is a capital intensive sector and hence, understanding its importance governments across the globe are funding for infrastructure to advance research on data mining, networks and high end systems like supercomputers. For instance, The European Molecular Biology Laboratory’s European Institute (EMBL-EBI) and BBSRC with support by U.K. government, made a commitment of approximately USD 93 million for ELIXIR research infrastructure in 2012.
Since, application of computer biology is in its nascent stage, the industry is facing challenge of resource crisis. This study requires skill sets of biology, mathematics, statistics and algorithms. To get the mix of all these specialties is an upheaval task for the companies involved in computational biology. Nonetheless, companies are now setting up their training centers and governments providing academic programs to overcome this impediment.
The computational biology industry is thus expected to grow leaps and bounds owing to patent expiry, rise in orphan diseases, and late stage failure of clinical trials with an advantage of introducing new drugs with minimal financial and human risks and within less time as compared to traditional clinical trials.