Tag Archives: Systems biology


“Systems biology…is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different….It means changing our philosophy, in the full sense of the term” (Denis Noble).[5]

From Wikipedia, the free encyclopedia
For the journal Proteomics, see Proteomics (journal).

Proteomics is the large-scale study of proteins, particularly their structuresand functions.[1][2] Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells.

The term proteomics was first coined in 1997[3] to make an analogy with genomics, the study of the genome. The word proteome is a blend of protein and genome, and was coined by Marc Wilkins in 1994 while working on the concept as a PhD student.[4][5]

The proteome is the entire set of proteins,[4] produced or modified by an organism or system. This varies with time and distinct requirements, or stresses, that a cell or organism undergoes.

Proteomics is an interdisciplinary domain formed on the basis of the research and development of the Human Genome Project;[citation needed] it is also emerging scientific research and exploration of proteomes from the overall level of intracellular protein composition, structure, and its own unique activity patterns. It is an important component of functional genomics.

While proteomics generally refers to the large-scale experimental analysis of proteins, it is often specifically used for protein purification and mass spectrometry.

Contents [hide]
1 Complexity of the problem
1.1 Post-translational modifications
1.1.1 Phosphorylation
1.1.2 Ubiquitination
1.1.3 Additional modifications
1.2 Distinct proteins are made under distinct settings
2 Limitations of genomics and proteomics studies
3 Methods of studying proteins
3.1 Protein Detection with Immunoassays
3.2 Identifying proteins that are post-translationally modified
3.3 Determining the existence of proteins in complex mixtures
3.4 Computational methods in studying protein biomarkers
4 Establishing protein–protein interactions
5 Practical applications of proteomics
5.1 Biomarkers
5.2 Proteogenomics
5.3 Current research methodologies
6 Bioinformatics for Proteomics
7 Structural proteomics
8 Expression proteomics
9 Interaction proteomics
10 Proteomics and System Biology
11 Current Proteomic Technologies
11.1 Mass Spectrometry and Protein Profiling
11.2 Protein Chips
11.3 Reverse Phased Protein Microarrays
12 Emerging trends in Proteomics
12.1 Human Plasma Proteome
13 See also
13.1 Protein databases
13.2 Research centers
14 References
15 Bibliography
16 External links

Complexity of the problem

After genomics and transcriptomics, proteomics is the next step in the study of biological systems. It is more complicated than genomics because an organism’s genome is more or less constant, whereas the proteome differs from cell to cell and from time to time. Distinct genes are expressed in different cell types, which means that even the basic set of proteins that are produced in a cell needs to be identified.
In the past this phenomenon was done by mRNA analysis, but it was found not to correlate with protein content.[6][7] It is now known that mRNA is not always translated into protein,[8] and the amount of protein produced for a given amount of mRNA depends on the gene it is transcribed from and on the current physiological state of the cell. Proteomics confirms the presence of the protein and provides a direct measure of the quantity present.

Post-translational modifications
Not only does the translation from mRNA cause differences, but many proteins are also subjected to a wide variety of chemical modifications after translation. Many of these post-translational modifications are critical to the protein’s function.

One such modification is phosphorylation, which happens to many enzymes and structural proteins in the process of cell signaling. The addition of a phosphate to particular amino acids—most commonly serine and threonine[9] mediated by serine/threonine kinases, or more rarely tyrosine mediated by tyrosine kinases—causes a protein to become a target for binding or interacting with a distinct set of other proteins that recognize the phosphorylated domain.
Because protein phosphorylation is one of the most-studied protein modifications, many “proteomic” efforts are geared to determining the set of phosphorylated proteins in a particular cell or tissue-type under particular circumstances. This alerts the scientist to the signaling pathways that may be active in that instance.

Ubiquitin is a small protein that can be affixed to certain protein substrates by enzymes called E3 ubiquitin ligases. Determining which proteins are poly-ubiquitinated helps understand how protein pathways are regulated. This is, therefore, an additional legitimate “proteomic” study. Similarly, once a researcher determines which substrates are ubiquitinated by each ligase, determining the set of ligases expressed in a particular cell type is helpful.
Additional modifications[edit]
Listing all the protein modifications that might be studied in a “proteomics” project would require a discussion of most of biochemistry. Therefore, a short list illustrates the complexity of the problem. In addition to phosphorylation and ubiquitination, proteins can be subjected to (among others) methylation, acetylation, glycosylation, oxidation and nitrosylation. Some proteins undergo all these modifications, often in time-dependent combinations. This illustrates the potential complexity of studying protein structure and function.


Practical applications of proteomics

One major development to come from the study of human genes and proteins has been the identification of potential new drugs for the treatment of disease. This relies on genome and proteome information to identify proteins associated with a disease, which computer software can then use as targets for new drugs. For example, if a certain protein is implicated in a disease, its 3D structure provides the information to design drugs to interfere with the action of the protein. A molecule that fits the active site of an enzyme, but cannot be released by the enzyme, inactivates the enzyme. This is the basis of new drug-discovery tools, which aim to find new drugs to inactivate proteins involved in disease. As genetic differences among individuals are found, researchers expect to use these techniques to develop personalized drugs that are more effective for the individual.[19]
Proteomics is also used to reveal complex plant-insect interactions that help identify candidate genes involved in the defensive response of plants to herbivory.[20][21][


Proteomics and System Biology

Proteomics has recently come into the act as a promising force to transform biology and medicine. It is becoming increasingly apparent that changes in mRNA expression correlate poorly with protein expression changes. Proteins changes enormously in patterns of expressions across developmental and physiological responses. Proteins also face changes on the act of environmental perturbations. Proteins are the actual effectors driving cell behavior. The field of proteomics strives to characterize protein structure and function, protein-protein,protein-nucleic acid, protein-lipid, and enzyme-substrate interactions, protein processing and folding, protein activation, cellular and sub-cellular localization, protein turnover and synthesis rates, and even promoter usage. Integrating proteomic data with information such as gene, mRNA and metabolic profiles helps in better understanding of how the system works.[37]

See also[edit]

Activity based proteomics
Bottom-up proteomics
Functional genomics
Heat stabilization
List of biological databases
List of omics topics in biology
Proteomic chemistry
Shotgun proteomics
Top-down proteomics
Systems biology
Yeast two-hybrid system
Protein databases[edit]
Human Protein Atlas
Cardiac Organellar Protein Atlas Knowledgebase (COPaKB)
Human Protein Reference Database
Model Organism Protein Expression Database (MOPED)
National Center for Biotechnology Information (NCBI)
Protein Data Bank (PDB)
Protein Information Resource (PIR)
Proteomics Identifications Database (PRIDE)
Proteopedia The collaborative, 3D encyclopedia of proteins and other molecules
Research centers[edit]
European Bioinformatics Institute
Netherlands Proteomics Centre (NPC)
Proteomics Research Resource for Integrative Biology (NIH)
Global map of proteomics labs

Jump up^ Anderson NL, Anderson NG (1998). “Proteome and proteomics: new technologies, new concepts, and new words”.Electrophoresis 19 (11): 1853–61.doi:10.1002/elps.1150191103. PMID 9740045.
Jump up^ Blackstock WP, Weir MP (1999). “Proteomics: quantitative and physical mapping of cellular proteins”. Trends Biotechnol. 17 (3): 121–7. doi:10.1016/S0167-7799(98)01245-1.PMID 10189717.
Jump up^ P. James (1997). “Protein identification in the post-genome era: the rapid rise of proteomics”. Quarterly reviews of biophysics 30 (4): 279–331.doi:10.1017/S0033583597003399. PMID 9634650.
^ Jump up to:a b Marc R. Wilkins, Christian Pasquali, Ron D. Appel, Keli Ou, Olivier Golaz, Jean-Charles Sanchez, Jun X. Yan, Andrew. A. Gooley, Graham Hughes, Ian Humphery-Smith, Keith L. Williams & Denis F. Hochstrasser (1996). “From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Arnino Acid Analysis”. Nature Biotechnology 14 (1): 61–65. doi:10.1038/nbt0196-61.PMID 9636313.
Jump up^ UNSW Staff Bio: Professor Marc Wilkins
Jump up^ Simon Rogers, Mark Girolami, Walter Kolch, Katrina M. Waters, Tao Liu, Brian Thrall and H. Steven Wiley (2008). “Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models”.Bioinformatics 24 (24): 2894–2900.doi:10.1093/bioinformatics/btn553. PMID 18974169.
Jump up^ Vikas Dhingraa, Mukta Gupta, Tracy Andacht and Zhen F. Fu (2005). “New frontiers in proteomics research: A perspective”.International Journal of Pharmaceutics 299 (1–2): 1–18.doi:10.1016/j.ijpharm.2005.04.010. PMID 15979831.
Jump up^ Buckingham, Steven (May 2003). “The major world of microRNAs”. Retrieved 2009-01-14.
Jump up^ Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006). “Global, in vivo, and site-specific phosphorylation dynamics in signaling networks”. Cell 127 (3): 635–648. doi:10.1016/j.cell.2006.09.026. PMID 17081983.
Jump up^ Gygi, S. P.; Rochon, Y.; Franza, B. R.; Aebersold, R. (1999).”Correlation between protein and mRNA abundance in yeast”.Molecular and Cellular Biology 19 (3): 1720–1730.PMC 83965. PMID 10022859. edit
Jump up^ Archana Belle, Amos Tanay, Ledion Bitincka, Ron Shamir and Erin K. O’Shea (2006). “Quantification of protein half-lives in the budding yeast proteome”. PNAS 103 (35): 13004–13009.Bibcode:2006PNAS..10313004B.doi:10.1073/pnas.0605420103. PMC 1550773.PMID 16916930.
Jump up^ Peng, J.; Elias, J. E.; Thoreen, C. C.; Licklider, L. J.; Gygi, S. P. (2003). “Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: The yeast proteome”. Journal of proteome research 2 (1): 43–50. PMID 12643542. edit
Jump up^ Washburn, M. P.; Wolters, D.; Yates, J. R. (2001). “Large-scale analysis of the yeast proteome by multidimensional protein identification technology”. Nature Biotechnology 19 (3): 242–247. doi:10.1038/85686. PMID 11231557. edit
^ Jump up to:a b c d Klopfleisch R, Klose P, Weise C, Bondzio A, Multhaup G, Einspanier R, Gruber AD. (2010). “Proteome of metastatic canine mammary carcinomas: similarities to and differences from human breast cancer”. J Proteome Res 9 (12): 6380–91.doi:10.1021/pr100671c. PMID 20932060.
Jump up^ Dix MM, Simon GM, Cravatt BF (August 2008). “Global mapping of the topography and magnitude of proteolytic events in apoptosis”. Cell 134 (4): 679–91.doi:10.1016/j.cell.2008.06.038. PMC 2597167.PMID 18724940.
Jump up^ Minde DP (2012). “Determining biophysical protein stability in lysates by a fast proteolysis assay, FASTpp”. PLOS ONE 7(10): e46147. Bibcode:2012PLoSO…746147M.doi:10.1371/journal.pone.0046147. PMC 3463568.PMID 23056252.
Jump up^ Maron JL, Alterovitz G, Ramoni M, Johnson KL, Bianchi DW (December 2009). “High-throughput discovery and characterization of fetal protein trafficking in the blood of pregnant women”. Proteomics Clin Appl 3 (12): 1389–96.doi:10.1002/prca.200900109. PMC 2825712.PMID 20186258.
Jump up^ Alterovitz G, Xiang M, Liu J, Chang A, Ramoni MF (2008).”System-wide peripheral biomarker discovery using information theory”. Pacific Symposium on Biocomputing: 231–42.PMID 18229689.
Jump up^ Vaidyanathan G (March 2012). “Redefining clinical trials: the age of personalized medicine”. Cell 148 (6): 1079–80.doi:10.1016/j.cell.2012.02.041. PMID 22424218.
Jump up^ Rakwal, Randeep; Komatsu, Setsuko (2000). “Role of jasmonate in the rice (Oryza sativa L.) self-defense mechanism using proteome analysis”. Electrophoresis 21 (12): 2492–500.doi:10.1002/1522-2683(20000701)21:12<2492::AID-ELPS2492>3.0.CO;2-2. PMID 10939463.
Jump up^ Wu, Jianqiang; Baldwin, Ian T. (2010). “New Insights into Plant Responses to the Attack from Insect Herbivores”. Annual Review of Genetics 44: 1–24. doi:10.1146/annurev-genet-102209-163500. PMID 20649414.
Jump up^ Sangha J.S., Chen Y.H., Kaur Jatinder, Khan Wajahatullah, Abduljaleel Zainularifeen, Alanazi Mohammed S., Mills Aaron, Adalla Candida B., Bennett John et al. (2013). “Proteome Analysis of Rice (Oryza sativa L.) Mutants Reveals Differentially Induced Proteins during Brown Planthopper (Nilaparvata lugens) Infestation”. Int. J. Mo Sci 14 (2): 3921–3945.doi:10.3390/ijms14023921. PMC 3588078.PMID 23434671.
Jump up^ Strimbu, Kyle; Tavel, Jorge A (2010). “What are biomarkers?”. Current Opinion in HIV and AIDS 5 (6): 463–6.doi:10.1097/COH.0b013e32833ed177. PMC 3078627.PMID 20978388.
Jump up^ Biomarkers Definitions Working Group (2001). “Biomarkers and surrogate endpoints: preferred definitions and conceptual framework”. Clinical Pharmacology & Therapeutics 69 (3): 89–95. doi:10.1067/mcp.2001.113989. PMID 11240971.
Jump up^ Klopfleisch R, Gruber AD (2009). “Increased expression of BRCA2 and RAD51 in lymph node metastases of canine mammary adenocarcinomas”. Veterinary Pathology 46 (3): 416–22. doi:10.1354/vp.08-VP-0212-K-FL. PMID 19176491.
Jump up^ Hathout, Yetrib (2007). “Approaches to the study of the cell secretome”. Expert Review of Proteomics 4 (2): 239–48.doi:10.1586/14789450.4.2.239. PMID 17425459.
Jump up^ Gupta N, Tanner S, Jaitly N, et al. (September 2007). “Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation”. Genome Res. 17 (9): 1362–77.doi:10.1101/gr.6427907. PMC 1950905.PMID 17690205.
Jump up^ Gupta N, Benhamida J, Bhargava V, et al. (July 2008).”Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes”.Genome Res. 18 (7): 1133–42. doi:10.1101/gr.074344.107.PMC 2493402. PMID 18426904.
^ Jump up to:a b Tonge R, Shaw J, Middleton B, et al. (March 2001). “Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology”.Proteomics 1 (3): 377–96. doi:10.1002/1615-9861(200103)1:3<377::AID-PROT377>3.0.CO;2-6.PMID 11680884.
Jump up^ Li-Ping Wang, Jun Shen, Lin-Quan Ge, Jin-Cai Wu, Guo-Qin Yang, Gary C. Jahn (November 2010). “Insecticide-induced increase in the protein content of male accessory glands and its effect on the fecundity of females in the brown planthopper,Nilaparvata lugens Stål (Hemiptera: Delphacidae)”. Crop Protection 29 (11): 1280–5. doi:10.1016/j.cropro.2010.07.009.
^ Jump up to:a b Ge, Lin-Quan; Cheng, Yao; Wu, Jin-Cai; Jahn, Gary C. (2011). “Proteomic Analysis of Insecticide Triazophos-Induced Mating-Responsive Proteins ofNilaparvata lugensStål (Hemiptera: Delphacidae)”. Journal of Proteome Research 10(10): 4597–612. doi:10.1021/pr200414g. PMID 21800909.
^ Jump up to:a b Reumann S (May 2011). “Toward a definition of the complete proteome of plant peroxisomes: Where experimental proteomics must be complemented by bioinformatics”.Proteomics 11 (9): 1764–79. doi:10.1002/pmic.201000681.PMID 21472859.
Jump up^ Uhlen M, Ponten F (April 2005). “Antibody-based proteomics for human tissue profiling”. Mol. Cell Proteomics 4 (4): 384–93.doi:10.1074/mcp.R500009-MCP200. PMID 15695805.
Jump up^ Ole Nørregaard Jensen (2004). “Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry”. Current Opinion in Chemical Biology 8(1): 33–41. doi:10.1016/j.cbpa.2003.12.009.PMID 15036154.
Jump up^ Chandramouli, Kondethimmanahalli; Qian, Pei-Yuan (2009). “Proteomics: Challenges, Techniques and Possibilities to Overcome Biological Sample Complexity”. Human Genomics and Proteomics 2009: 1. doi:10.4061/2009/239204.
^ Jump up to:a b c d e f “What is Proteomics?”. ProteoConsult.[unreliable medical source?]
^ Jump up to:a b c d e f g Weston, Andrea D.; Hood, Leroy (2004). “Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative, and Personalized Medicine”. Journal of Proteome Research 3 (2): 179–96. doi:10.1021/pr0499693.PMID 15113093.

Belhajjame, K. et al. Proteome Data Integration: Characteristics and Challenges. Proceedings of the UK e-Science All Hands Meeting, ISBN 1-904425-53-4, September 2005, Nottingham, UK.
Twyman RM (2004). Principles Of Proteomics (Advanced Text Series). Oxford, UK: BIOS Scientific Publishers. ISBN 1-85996-273-4. (covers almost all branches of proteomics)
Naven T, Westermeier R (2002). Proteomics in Practice: A Laboratory Manual of Proteome Analysis. Weinheim: Wiley-VCH.ISBN 3-527-30354-5. (focused on 2D-gels, good on detail)
Liebler DC (2002). Introduction to proteomics: tools for the new biology. Totowa, NJ: Humana Press. ISBN 0-89603-992-7. ISBN 0-585-41879-9 (electronic, on Netlibrary?), ISBN 0-89603-991-9 hbk
Wilkins MR, Williams KL, Appel RD, Hochstrasser DF (1997).Proteome Research: New Frontiers in Functional Genomics (Principles and Practice). Berlin: Springer. ISBN 3-540-62753-7.
Arora PS, Yamagiwa H, Srivastava A, Bolander ME, Sarkar G (2005). “Comparative evaluation of two two-dimensional gel electrophoresis image analysis software applications using synovial fluids from patients with joint disease”. J Orthop Sci 10 (2): 160–6.doi:10.1007/s00776-004-0878-0. PMID 15815863.
Rediscovering Biology Online Textbook. Unit 2 Proteins and Proteomics. 1997–2006.
Weaver RF (2005). Molecular biology (3rd ed.). New York: McGraw-Hill. pp. 840–9. ISBN 0-07-284611-9.
Reece J, Campbell N (2002). Biology (6th ed.). San Francisco: Benjamin Cummings. pp. 392–3. ISBN 0-8053-6624-5.
Hye A, Lynham S, Thambisetty M et al. (November 2006). “Proteome-based plasma biomarkers for Alzheimer’s disease”.Brain 129 (Pt 11): 3042–50. doi:10.1093/brain/awl279.PMID 17071923.
Perroud B, Lee J, Valkova N et al. (2006). “Pathway analysis of kidney cancer using proteomics and metabolic profiling”. Mol Cancer 5: 64. doi:10.1186/1476-4598-5-64. PMC 1665458.PMID 17123452.
Yohannes E, Chang J, Christ GJ, Davies KP, Chance MR (July 2008). “Proteomics analysis identifies molecular targets related to diabetes mellitus-associated bladder dysfunction”. Mol. Cell Proteomics 7 (7): 1270–85. doi:10.1074/mcp.M700563-MCP200.PMC 2493381. PMID 18337374.
Macaulay IC, Carr P, Gusnanto A, Ouwehand WH, Fitzgerald D, Watkins NA (December 2005). “Platelet genomics and proteomics in human health and disease”. J Clin Invest. 115 (12): 3370–7.doi:10.1172/JCI26885. PMC 1297260. PMID 16322782.
Rogers MA, Clarke P, Noble J et al. (15 October 2003). “Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility”.Cancer Res. 63 (20): 6971–83. PMID 14583499.
Vasan RS (May 2006). “Biomarkers of cardiovascular disease: molecular basis and practical considerations”. Circulation 113 (19): 2335–62. doi:10.1161/CIRCULATIONAHA.104.482570.PMID 16702488.
“Myocardial Infarction”. (Retrieved 29 November 2006)
Introduction to Antibodies – Enzyme-Linked Immunosorbent Assay (ELISA). (Retrieved 29 November 2006)
Decramer, Stephane; Wittke, Stefan; Mischak, Harald; Zürbig, Petra; Walden, Michael; Bouissou, François; Bascands, Jean-Loup; Schanstra, Joost P (2006). “Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis”. Nature Medicine 12 (4): 398–400.doi:10.1038/nm1384. PMID 16550189.
Mayer U (January 2008). “Protein Information Crawler (PIC): extensive spidering of multiple protein information resources for large protein sets”. Proteomics 8 (1): 42–4.doi:10.1002/pmic.200700865. PMID 18095364.
Jörg von Hagen, VCH-Wiley 2008 Proteomics Sample Preparation.ISBN 978-3-527-31796-7
External links[edit]

Proteomics on the Open Directory Project
Look up proteomics in Wiktionary, the free dictionary.
Wikibooks has more on the topic of: Proteomics
At Wikiversity you can learn more and teach others aboutProteomics at:
The Department of Proteomics
Genomics (Environmental genomics or Metagenomics)TranscriptomicsProteomicsMetabolomics (Glycomics, Lipidomics)
BioinformaticsCheminformaticsChemogenomicsComputational genomicsGenome projectHuman Genome ProjectImmunomicsNutrigenomicsPaleopolyploidyPersonal genomicsPharmacogeneticsPharmacogenomicsStructural genomicsSystems biologyToxicogenomics
Biology portal Medicine portal
Categories: ProteomicsGenomicsBioinformaticsProteins

Personalized medicine
From Wikipedia, the free encyclopedia
Personalized medicine or PM is a medical model that proposes the customization of healthcare – with medical decisions, practices, and/or products being tailored to the individual patient. In this model, diagnostic testing is essential for selecting appropriate therapies; terms used to describe these tests include “”companion diagnostics”, “theranostics” (a portmanteau of therapeutics and diagnostics), and “therapygenetics”. The use of genetic information has played a major role in certain aspects of personalized medicine, and the term was even first coined in the context of genetics (though it has since broadened to encompass all sorts of personalization measures). To distinguish from the sense in which medicine has always been inherently “personal” to each patient, PM commonly denotes the use of some kind of technology or discovery enabling a level of personalization not previously feasible or practical.
Contents [hide]
1 Background
2 Technologies
3 Examples
3.1 Cancer management
4 Psychiatry and psychological therapy
5 See also
6 References
7 Further reading
8 External links

Traditional clinical diagnosis and management focuses on the individual patient’s clinical signs and symptoms, medical and family history, and data from laboratory and imaging evaluation to diagnose and treat illnesses. This is often a reactive approach to treatment, i.e., treatment/medication starts after the signs and symptoms appear.
Advances in medical genetics and human genetics have enabled a more detailed understanding of the impact of genetics in disease. Large collaborative research projects (for example, the Human genome project) have laid the groundwork for the understanding of the roles of genes in normal human development and physiology, revealed single nucleotide polymorphisms (SNPs) that account for some of the genetic variability between individuals, and made possible the use of genome-wide association studies (GWAS) to examine genetic variation and risk for many common diseases.
Historically, the pharmaceutical industry has developed medications based on empiric observations and more recently, known disease mechanisms.[citation needed] For example, antibiotics were based on the observation that microbes produce substances that inhibit other species. Agents that lower blood pressure have typically been designed to act on certain pathways involved in hypertension(such as renal salt and water absorption, vascular contractility, and cardiac output). Medications for high cholesterol target the absorption, metabolism, and generation of cholesterol. Treatments for diabetes are aimed at improving insulin release from the pancreas and sensitivity of the muscle and fat tissues to insulin action. Thus, medications are developed based on mechanisms of disease that have been extensively studied over the past century. It is hoped that recent advancements in the genetic etiologies of common diseases will improve pharmaceutical development.

Since the late 1990s, the advent of research using biobanks has brought advances in molecular biology, proteomics, metabolomicanalysis, genetic testing, and molecular medicine. Another significant development has been the notion of companion diagnostics, whereby molecular assays that measure levels of proteins, genes, or specific mutations are used to provide a specific therapy for an individual’s condition – by stratifying disease status, selecting the proper medication, and tailoring dosages to that patient’s specific needs. Additionally, such methods might be used to assess a patient’s risk factor for a number of conditions and tailor individualpreventative treatments.
Pharmacogenetics (also termed pharmacogenomics) is the field of study that examines the impact of genetic variation and responses to therapeutic interventions by biomarker (medicine).[1] This approach is aimed at tailoring drug therapy at a dosage that is most appropriate for an individual patient, with the potential benefits of increasing the efficacy and safety of medications.[2] Other benefits include reduced time, cost, and failure rates of clinical trials in the production of new drugs by using precise biomarkers.[3] Gene-centered research may also speed the development of novel therapeutics.[4]
The field of proteomics, or the comprehensive analysis and characterization of all of the proteins and protein isoforms encoded by thehuman genome, may eventually have a significant impact on medicine. This is because while the DNA genome[5] is the information archive, it is the proteins that do the work of the cell: the functional aspects of the cell are controlled by and through proteins, not genes.
It has also been demonstrated that pre-dose metabolic profiles from urine can be used to predict drug metabolism.[6][7]Pharmacometabolomics refers to the direct measurement of metabolites in an individual’s bodily fluids, in order to predict or evaluate the metabolism of pharmaceutical compounds.

Some examples of personalized medicine include:
Genotyping for SNPs in genes involved in the action and metabolism of warfarin (Coumadin). This medication is used clinically as an anticoagulant but requires periodic monitoring and is associated with adverse side affects. Recently, genetic variants in the gene encoding Cytochrome P450 enzyme CYP2C9, which metabolizes warfarin,[8] and the Vitamin K epoxide reductase gene (VKORC1), a target of coumarins,[9] have led to commercially-available testing that enables more accurate dosing based on algorithms that take into account the age, gender, weight, and genotype of an individual.
Genotyping variants in genes encoding Cytochrome P450 enzymes (CYP2D6, CYP2C19, and CYP2C9), which metabolize neuroleptic medications, to improve drug response and reduce side-effects.[10]
Cancer management[edit]
Oncology is a field of medicine with a long history of classifying tumor stages and subtypes based on anatomic and pathologic findings. This approach includes histological examination of tumor specimens from individual patients (such as HER2/NEU in breast cancer) to look for markers associated with prognosis and likely treatment responses. Thus, “personalized medicine” was in practice long before the term was coined. New molecular testing methods have enabled an extension of this approach to include testing for global gene, protein, and protein pathway activation expression profiles and/or somatic mutations in cancer cells from patients in order to better define the prognosis in these patients and to suggest treatment options that are most likely to succeed.[11][12]
Examples of personalized cancer management include:
Companion diagnostics for targeted therapies.
Trastuzumab (trade names Herclon, Herceptin) is a monoclonal antibody drug that interferes with the HER2/neu receptor. Its main use is to treat certain breast cancers. This drug is only used if a patient’s cancer is tested for overexpression the HER2/neu receptor. Two of the most common tests used are the (Dako) HercepTest and Genentech’s Herceptin.[13] Only Her2+ patients will be treated with Herceptin therapy (trastuzumab)[14]
Tyrosine kinase inhibitors such as imatinib (marketed as Gleevec) have been developed to treat chronic myeloid leukemia(CML), in which the BCR-ABL fusion gene (the product of a reciprocal translocation between chromosome 9 and chromosome 22) is present in >95% of cases and produces hyperactivated abl-driven protein signaling. These medications specifically inhibit the Ableson tyrosine kinase (ABL) protein and are thus a prime example of “rational drug design” based on knowledge of disease pathophysiology.[15]
Testing for disease-causing mutations in the BRCA1 and BRCA2 genes, which are implicated in hereditary breast–ovarian cancer syndromes. Discovery of a disease-causing mutation in a family can inform “at-risk” individuals as to whether they are at higher risk for cancer and may prompt individualized prophylactic therapy including mastectomy and removal of the ovaries. This testing involves complicated personal decisions and is undertaken in the context of detailed genetic counseling. More detailed molecular stratification of breast tumors may pave the way for future tailored treatments.[16] These tests are part of the emerging field ofcancer genetics, which is a specialized field of medical genetics concerned with hereditary cancer risk.
Psychiatry and psychological therapy[edit]

Efforts are underway to apply the tools of personalized medicine to psychiatry and psychological therapy; these technologies are still under development as of 2013.
In 2012 Professor Thalia Eley and her research team coined the term “therapygenetics” refers to a branch of psychiatric genetic research looking at the relationship between specific genetic variants and differences in the level of success of psychological therapy.[17][18] The field is parallel to pharmacogenetics, which explores the association between specific genetic variants and the efficacy of drug treatments. Therapygenetics work also relates to the differential susceptibility hypothesis [19] which proposes that individuals have a genetic predisposition to respond to a greater or lesser extent to their environment, be it positive or negative.
See also[edit]

Predictive medicine
Whole genome sequencing
Drug development
Translational Research
$1,000 genome

Jump up^ Shastry BS (2006). “Pharmacogenetics and the concept of individualized medicine”. Pharmacogenomics J. 6 (1): 16–21.doi:10.1038/sj.tpj.6500338. PMID 16302022.
Jump up^ Ozdemir, Vural; Williams-Jones, Bryn; Glatt, Stephen J; Tsuang, Ming T; Lohr, James B; Reist, Christopher (August 2006). “Shifting emphasis from pharmacogenomics to theranostics”. Nature Biotechnology 24 (8): 942–946. Retrieved 30 March 2013.
Jump up^ Galas, D. J., & Hood, L. (2009). “Systems Biology and Emerging Technologies Will Catalyze the Transition from Reactive Medicine to Predictive, Personalized, Preventive and Participatory (P4) Medicine”. Interdisciplinary Bio Central 1: 1–4.doi:10.4051/ibc.2009.2.0006.
Jump up^ Shastry BS (2006). “Pharmacogenetics and the concept of individualized medicine”. Pharmacogenomics J. 6 (1): 16–21.doi:10.1038/sj.tpj.6500338. PMID 16302022.
Jump up^ Harmon, Katherine (2010-06-28). “Genome Sequencing for the Rest of Us”. Scientific American. Retrieved 2010-08-13.
Jump up^ Clayton TA, Lindon JC, Cloarec O, et al. (April 2006). “Pharmaco-metabonomic phenotyping and personalized drug treatment”. Nature 440 (7087): 1073–7.doi:10.1038/nature04648. PMID 16625200.
Jump up^ Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK (August 2009). “Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism”. Proc. Natl. Acad. Sci. U.S.A. 106(34): 14728–33. doi:10.1073/pnas.0904489106.PMC 2731842. PMID 19667173.
Jump up^ Schwarz UI (November 2003). “Clinical relevance of genetic polymorphisms in the human CYP2C9 gene”. Eur. J. Clin. Invest. 33. Suppl 2: 23–30. doi:10.1046/j.1365-2362.33.s2.6.x. PMID 14641553.
Jump up^ Oldenburg J, Watzka M, Rost S, Müller CR (July 2007). “VKORC1: molecular target of coumarins”. J. Thromb. Haemost. 5. Suppl 1: 1–6. doi:10.1111/j.1538-7836.2007.02549.x.PMID 17635701.
Jump up^ Cichon S, Nöthen MM, Rietschel M, Propping P (2000). “Pharmacogenetics of schizophrenia”. Am. J. Med. Genet. 97(1): 98–106. doi:10.1002/(SICI)1096-8628(200021)97:1<98::AID-AJMG12>3.0.CO;2-W.PMID 10813809.
Jump up^ Mansour JC, Schwarz RE (August 2008). “Molecular mechanisms for individualized cancer care”. J. Am. Coll. Surg.207 (2): 250–8. doi:10.1016/j.jamcollsurg.2008.03.003.PMID 18656055.
Jump up^ van’t Veer LJ, Bernards R (April 2008). “Enabling personalized cancer medicine through analysis of gene-expression patterns”.Nature 452 (7187): 564–70. doi:10.1038/nature06915.PMID 18385730.
Jump up^ Carney, Walter (2006). “HER2/neu Status is an Important Biomarker in Guiding Personalized HER2/neu Therapy”.Connection 9: 25–27.
Jump up^ Telli, M. L.; Hunt, S. A.; Carlson, R. W.; Guardino, A. E. (2007). “Trastuzumab-Related Cardiotoxicity: Calling Into Question the Concept of Reversibility”. Journal of Clinical Oncology 25 (23): 3525–3533.doi:10.1200/JCO.2007.11.0106. ISSN 0732-183X.PMID 17687157.
Jump up^ Saglio G, Morotti A, Mattioli G, et al. (December 2004). “Rational approaches to the design of therapeutics targeting molecular markers: the case of chronic myelogenous leukemia”.Ann. N. Y. Acad. Sci. 1028 (1): 423–31.doi:10.1196/annals.1322.050. PMID 15650267.
Jump up^ Gallagher, James (19 April 2012). “Breast cancer rules rewritten in ‘landmark’ study”. BBC News. Retrieved 19 April 2012.
Jump up^ Lester, KJ; Eley TC (2013). “Therapygenetics: Using genetic markers to predict response to psychological treatment for mood and anxiety disorders”. Biology of mood & anxiety disorders 3(1): 1–16. doi:10.1186/2045-5380-3-4. PMC 3575379.PMID 23388219.
Jump up^ Beevers, CG; McGeary JE (2012). “Therapygenetics: moving towards personalized psychotherapy treatment”. Trends in Cognitive Sciences 16 (1): 11–12.doi:10.1016/j.tics.2011.11.004. PMC 3253222.PMID 22104133.
Jump up^ Belsky J, Jonassaint C, Pluess M, Stanton M, Brummett B, Williams R (August 2009). “Vulnerability genes or plasticity genes?”. Mol. Psychiatry 14 (8): 746–54.doi:10.1038/mp.2009.44. PMC 2834322.PMID 19455150.
Further reading[edit]

Daskalaki A, Wierling C, Herwig R (2009), Computational tools and resources for systems biology approaches in cancer.In Computational Biology – Issues and Applications in Oncology, Series: Applied Bioinformatics and Biostatistics in Cancer Research, Pham, Tuan (Ed.), Springer, New York Dordrecht Heidelberg London. 2009:227-242.
Acharya et al. (2008), Gene Expression Signatures, clinicopathological features, and individualized therapy in breast cancer, JAMA 299: 1574.
Sadee W, Dai Z. (2005), Pharmacogenetics/genomics and personalized medicine, Hum Mol Genet. 2005 October 15;14 Spec No. 2:R207-14.
Steven H. Y. Wong (2006), Pharmacogenomics and Proteomics: Enabling the Practice of Personalized Medicine, American Association for Clinical Chemistry, ISBN 1-59425-046-4
Qing Yan (2008), Pharmacogenomics in Drug Discovery and Development, Humana Press, 2008, ISBN 1-58829-887-6.
Willard, H.W., and Ginsburg, G.S., (eds), (2009), Genomic and Personalized Medicine, Academic Press, 2009, ISBN 0-12-369420-5.
Haile, Lisa A. (2008), Making Personalized Medicine a Reality, Genetic Engineering & Biotechnology News Vol. 28, No. 1.
Hornberger J, Habraken H, Bloch DA. Minimum data needed on patient preferences for accurate, efficient medical decision making. Medical Care 1995; 33:297-310.
Lyman GH, Cosler LE, Kuderer NM, Hornberger J. Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer: an economic analysis based on prognostic and predictive validation studies. Cancer 2007; 109(6):1011-8.
Hornberger J, Cosler L and Lyman G. Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node–negative, estrogen-receptor–positive, early-stage breast cancer. Am J Managed Care 2005; 11:313-24.
A.Daskalaki & A.Lazakidou (2011). Quality Assurance in Healthcare Service Delivery, Nursing and Personalized Medicine: Technologies and Processes. IGI Global. ISBN 978-1-61350-120-7
Picard FJ, Bergeron MG., Rapid molecular theranostics in infectious diseases, Drug Discov Today. 2002 Nov 1;7(21):1092-101.
Hooper JW., The genetic map to theranostics, MLO Med Lab Obs. 2006 Jun;38(6):22-3, 25.
External links[edit]

CancerDriver : a free and open database to promote personalized medicine in oncology.
Personal genomics
Emerging technologies
Categories: Evidence-based medicineGeneticsGenomicsHealth informaticsMonoclonal antibodiesPharmacologyEmerging technologies

Adam Arkin

Adam Arkin (colleague of Vijay Vaserani)

http://Berkeley BIO

Research Expertise and Interest
Systems and Synthetic Biology, Environmental Microbiology of Bacteria and Viruses, bioenergy, Biomedicine, Bioremediation

The Arkin laboratory for systems and synthetic biology seeks to uncover the evolutionary design principles of cellular networks and populations and to exploit them for applications. To do so they are developing a framework to effectively combine comparative functional genomics, quantitative measurement of cellular dynamics, biophysical modeling of cellular networks, and cellular circuit design to ultimately facilitate applications in health, the environment, and bioenergy.