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Molecular medicine and the digitisation of biology



Molecular medicine involves the understanding of the process of diseases on the molecular level, and the application of suitable treatments to impair this process. Many research areas are involved in determining the link between the disease state and genes. In order to do this, you need to not only describe molecular structures and mechanisms of the disease but also find the errors of disease on the genetic (e.g. single nucleotide polymorphism/SNP) and higher levels (e.g. cellular networks). From this knowledge we could potentially personalise medicine and develop preventative treatments.

History of molecular medicine
Linus Pauling and his colleagues were pioneers of early molecular medicine researching the disease sickle cell anaemia on a molecular level in 1949. It had a major impact on studying diseases and understanding their relationship to genes. Albeit even though Pauling’s research gained tremendous amounts of information about molecular diseases and human variability, the Human Genome Project (2003) arguably had the most effective influence on personalised medicine. Several approaches have been attempted to use personalised medicine with the help of Human Genome Project. However, because of the complexity of human genome functionality, it is hard to find linkages between genes that lead to disease states and suitable treatments.

Since the late 20th century, biology was powered by a largely reductionist approaches, paying attention to generating information about individual cellular components, their chemical composition and their functionality. From this we have significantly improved our knowledge of diseases and also how they relate to genomes. Subsequently the development started to drive a fundamental shift in biology that lead to the understanding of human health and disease conditions and it has been followed by developments in areas such as systems biology that has a unique approach (see Fig. 1) while implementing methods such as imaging, computation, mathematical biology and others.



Future Direction of Molecular Medicine
The success of molecular medicine and the awareness it has generated is driving us towards increasingly better treatments and personalised medicine. Personalised medicine involves obtaining information from patients’ genomes which is utilised to form a decision on the most suitable treatment. One also needs to take into account many environmental factors and its impact on our genome. Another aspect of molecular medicine is based on the prevention of putative side-effects, resistance and susceptibility of the drug and developing other prophylactic strategies. The aim is to employ personalised medicine to regular health care practices and not limit it to severe and rare diseases.

P4 medicine
Leroy Hood, one of the founding fathers of systems biology, has a promising vision called P4 medicine: personalised, predictive, preventive, participatory medicine. P4 involves utilising molecular information gathered from patient’s genome, environmental factors and history in order to prevent diseases rather than treat diseases when they occur. In short, diseases could be prevented according to individual’s health status.

Why we don’t have P4 already
Further research is required before P4 medicine is used in practices. The major problems in this field include fully understanding the human genome and how genotype translates to phenotype. In addition, genetic information as well as other biological data (e.g. metabolomics) needs to be converted to a digital form where the linkage between disease and genome is defined not only qualitatively but also quantitatively in silico. This way of studying biology is called a “systems” approach (see understanding digital biology section). If this is achieved, P4 medicine would also require the digitisation of medical records and creating secure databases for those records. Moreover, mathematical and computational methods are also necessary in order to translate information from individuals such as patient history. Furthermore, to obtain dynamic disease-predictive networks from gigantic amounts of data, novel computational techniques are required.

The understanding of digital biology
DNA is the parts list for cells, regulated by its molecular surroundings. In turn, its molecular surroundings are influenced by diverse environmental factors such as diet, microbes, toxins, childhood development and socioeconomic status. For personalised medicine to be perfected using a systems approach, environmental and genetic factors (e.g. SNP’s) expressed in terms of digital information that affect biological functions need to be understood and modelled in silico.

This proves difficult as one can argue that biology (not limited to human biology) is hierarchical (see Fig. 2) and at each hierarchical point, environmental cues change biological function.



A systems approach is applied to a range of reductionist datasets including genomic, transcriptomic, translatomic, proteomic and metabolomics (for glossary of omics see http://www.genomicglossaries.com/content/omes.asp) to better understand cellular and biological systems on a holistic level. One can build top down models from bottom up data (omic’s) or vice versa and this will ease decision making by pharmaceutical companies in terms of choosing drug targets and predicting outcomes (see Fig. 3). It essentially can be a hypothesis generating tool for drug targets and the effects potential drugs will cause. It’s important to note that this is modelled in silico by perturbing modelled systems.



Applications of personalised medicine
Personalised medicine grasps a very different view point to approaching biology when compared to other disciplines of molecular biology. An intriguing aspect developing personalised medicine would be its ability to predict biological activities at the molecular, cellular and organ levels having direct consequences to the development of novel drugs as well as diagnostic and prognostic tools for the advantage of all human beings.

The likely sectors that will be majorly impacted by personalised medicine within the next couple of years include pharmaceutical and biopharmacy companies (see Fig.4). Personalised medicine could lead to significant economic profit by reducing drug discovery costs and shortening drug development times. A critical issue with the development of pharmaceutical drugs is that there are plenty of predisposing factors that can lead to several complex diseases on the basis of their significance as key ‘nodes’ within an overall network. Furthermore, it can translate some of the evocative biomedical information into functional data, which in turn makes it convenient to elucidate any unusual responsiveness of some individuals to a specific drug.



Personalised medicine includes predictive and prognostic tests of therapeutic importance. For instance, personalised medicine will have the capability to generate the right tools for some biological indicators known as biomarkers. Biomarkers enhance make assessing biological predispositions to acquiring diseases, which in turn informs the progression of a disease and consequently the treatment or preventative strategies.

Similarly to biomarkers, personalised medicine utilises imaging technique which provide essential insight to disease states and disease progression. Recent studies demonstrate that new in vivo imaging and analysis techniques are used to follow disease, drug response, drug efficacy, drug dosage establishment, etc. This method is advancing rapidly and the data is integrated with other measurement data to create prediction and diagnosis. Some of the best applications to personalised medicine will be to the therapeutic targeting of complex multi-factorial diseases. These include diseases such as hypertension, diabetes, obesity and rheumatoid arthritis. In addition, it can be vital to coordinate the functions of neural cell populations and signals processing as well as adapting this to normal behaviours, but also in several pathological states like depression, schizophrenia and autism.

Another application is the rapid progressing of understanding heart physiology which could be a key factor in reducing drug-induced cardiac arrhythmias. In addition to this, it can also be utilized in the modulation of liver toxicity, one of the crucial factors underlying failures of drug usage.

Finally, personalised medicine may be essential in the treatment of cancer and the understanding of its mechanisms, in capturing the essence of the process of ageing in order to make better the quality of life. An example of personalised cancer treatment is the examination of mutation on the BRCA1 and BRCA2 genes that may have lead to familial breast or ovarian cancer syndromes. Discovering a disease causing mutation in a family can warn individuals of the danger of higher risk of cancer. This in turn leads to prompt individualised prophylactic therapy including mastectomy or surgical removal of the ovaries.

Limitations
A great advantage of personalised medicine and systems biology lies in its high throughput mechanism of gathering lots of data; however there are limitations both in methods to obtain this data and the usage of it. As high throughput testing usually needs automation and has to be cost effective, only tests which fit these requirements can be utilised. This excludes analytical tools such as western blotting or electrophoresis. Large datasets such as microarrays can characterise gene expression in pathways but one has to be careful. One cannot be absolutely sure that the parameters defining the experiment are in line or can be applied to their own data or analysis.

The expense of sequencing
One limitation currently being tackled effectively is DNA sequencing. Current biomedical research owes a lot to the Human Reference Genome (HRG) sequenced a decade ago. DNA sequencing technology is acquiring a central role in biological studies, because it offers the possibility to develop a new era based on the understanding of health and diseases, and furthermore the applications for personalised medicine.



The development of new instruments, such as Solexa (Illumina) and SOLID are helping to lower costs and accelerate the process of DNA sequencing; moreover, the interest in bioinformatics to find out the entire genome has led a lot of manufactures investing money in finding new important evidences and have become important from the point of view of marketing. Data from the The National Human Genome Research Institute (NHGRI) Large-Scale Genome Sequencing Program elucidates clearly the changes in the cost for sequence the DNA from July 2001 to October 2011 (Figure 5). In July 2001 the cost per genome was $ 100,000,000 after which it decreased at a slow pace for a short period (in September 2001 it was 95,263,072) and a year later the fall began to pick up pace as it decreased to $ 61,448,422. In 2005 new types of platform to sequence the DNA were created and they were different from the capillary sequencers used before. They produced far more sequence reads per instrument run and at a significantly lower expense. This was possible with the introduction of new technologies from Sanger sequencing reaction to ‘next generation’ DNA sequencing technologies (e.g. pyrosequencing, SMRT sequencing, and nanopore technology). During October 2011 the cost for sequencing the human genome lowered to $ 7,743. The goal of NHGRI is be able to sequence the human genomes for $ 1,000 by 2014. Although Fig. 5 shows elegantly the decrease in the cost of sequencing it is important to remember the following - “These data, however, do not capture all of the costs associated with the NHGRI Large-Scale Genome Sequencing Program. The sequencing centres perform a number of additional activities whose costs are not appropriate to include when calculating costs for production-oriented DNA sequencing”.

Ethical concerns of molecular medicine
As the personalised treatment of patients would need genome screening and the formation of population genome databases, several ethical, legal and social issues have arisen. Common questions that come to mind are regarding how we could guarantee that genomes are subject to strict fair policy and not taken advantage of, for example by health insurance companies, and who this information would be available to for access. Additionally, genomic data should not be used for discriminatory purposes, for example by employers. Transparency in who owns and controls genetic information is essential to gain public trust. Another issue is about how patients would react to the knowledge regarding their genetic information and how that would that affect choices in their life.

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