Modern Usage

In many countries, cocaine is a popular recreational drug. In the United States, the development of "crack" cocaine introduced the substance to a generally poorer inner-city market. Use of the powder form has stayed relatively constant, experiencing a new height of use during the late 1990s and early 2000s in the U.S., and has become much more popular in the last few years in the UK.

Cocaine use is prevalent across all socioeconomic strata, including age, demographics, economic, social, political, religious, and livelihood. Cocaine in its various forms comes in second only to cannabis as the most popular illegal recreational drug in the United States, and is number one in street value sold each year.^{[citation needed]}

The estimated U.S. cocaine market exceeded $70 billion in street value for the year 2005, exceeding revenues by corporations such as Starbucks^[18][19]. There is a tremendous demand for cocaine in the U.S. market, particularly among those who are making incomes affording luxury spending, such as single adults and professionals with discretionary income. Cocaine’s status as a club drug shows its immense popularity among the “party crowd”.

In 1995 the World Health Organization (WHO) and the United Nations Interregional Crime and Justice Research Institute (UNICRI) announced in a press release the publication of the results of the largest global study on cocaine use ever undertaken. However, a decision in the World Health Assembly banned the publication of the study. In the sixth meeting of the B committee the US representative threatened that "If WHO activities relating to drugs failed to reinforce proven drug control approaches, funds for the relevant programs should be curtailed". This led to the decision to discontinue publication. A part of the study has been recuperated.^[20] Available are profiles of cocaine use in 20 countries.

A problem with illegal cocaine use, especially in the higher volumes used to combat fatigue (rather than increase euphoria) by long-term users is the risk of ill effects or damage caused by the compounds used in adulteration. Cutting or "stamping on" the drug is commonplace, using compounds which simulate ingestion effects, such as Novocain (procaine) producing temporary anaesthaesia as many users believe a strong numbing effect is the result of strong and/or pure cocaine, ephedrine or similar stimulants that are to produce an increased heart rate. The normal adulterants for profit are inactive sugars, usually mannitol, creatine or glucose, so introducing active adulterants gives the illusion of purity and to 'stretch' or make it so a dealer can sell more product than without the adulterants. The adulterant of sugars therefore allows the dealer to sell the product for a higher price because of the illusion of purity and allows to sell more of the product at that higher price, enabling dealers to make a lot of revenue with little cost of the adulterants. Cocaine trading carries large penalties in most jurisdictions, so user deception about purity and consequent high profits for dealers are the norm.

Prohibition

By the turn of the twentieth century, the addictive properties of cocaine had become clear, and the problem of cocaine abuse began to capture public attention in the United States. The dangers of cocaine abuse became part of a moral panic that was tied to the dominant racial and social anxieties of the day. In 1903, the American Journal of Pharmacy stressed that most cocaine abusers were “bohemians, gamblers, high- and low-class prostitutes, night porters, bell boys, burglars, racketeers, pimps, and casual laborers.” In 1914, Dr. Christopher Koch of Pennsylvania’s State Pharmacy Board made the racial innuendo explicit, testifying that, “Most of the attacks upon the white women of the South are the direct result of a cocaine-crazed Negro brain.” Mass media manufactured an epidemic of cocaine use among African Americans in the Southern United States to play upon racial prejudices of the era, though there is little evidence that such an epidemic actually took place. In the same year, the Harrison Narcotics Tax Act outlawed the sale and distribution of cocaine in the United States. This law incorrectly referred to cocaine as a narcotic, and the misclassification passed into popular culture. As stated above, cocaine is a stimulant, not a narcotic. Although technically illegal for purposes of distribution and use, the distribution, sale and use of cocaine was still legal for registered companies and individuals. Because of the misclassification of cocaine as a narcotic, the debate is still open on whether the government actually enforced these laws strictly. Cocaine was not considered a controlled substance until 1970, when the United States listed it as such in the Controlled Substances Act. Until that point, the use of cocaine was open and rarely prosecuted in the US due to the moral and physical debates commonly discussed.

Popularization

Paolo Mantegazza, returned from Peru, where he had witnessed first-hand the use of coca by the natives. He proceeded to experiment on himself and upon his return to Milan he wrote a paper in which he described the effects. In this paper he declared coca and cocaine (at the time they were assumed to be the same) as being useful medicinally, in the treatment of “a furred tongue in the morning, flatulence, [and] whitening of the teeth.”

Pope Leo XIII purportedly carried a hipflask of Vin Mariani with him, and awarded a Vatican gold medal to Angelo Mariani.

A chemist named Angelo Mariani who read Mantegazza’s paper became immediately intrigued with coca and its economic potential. In 1863, Mariani started marketing a wine called Vin Mariani, which had been treated with coca leaves, to become cocawine. The ethanol in wine acted as a solvent and extracted the cocaine from the coca leaves, altering the drink’s effect. It contained 6 mg cocaine per ounce of wine, but Vin Mariani, which was to be exported, contained 7.2 mg per ounce to compete with the higher cocaine content of similar drinks in the United States. A “pinch of coca leaves” was included in John Styth Pemberton's original 1886 recipe for Coca-Cola, though the company began using decocainized leaves in 1906 when the Pure Food and Drug Act was passed. The only known measure of the amount of cocaine in Coca-Cola was determined in 1902 as being as little as 1/400 of a grain (0.2 mg) per ounce of syrup (6 ppm). The actual amount of cocaine that Coca-Cola contained during the first twenty years of its production is practically impossible to determine.

In 1885 the U.S. manufacturer Parke-Davis sold cocaine in various forms, including cigarettes, powder, and even a cocaine mixture that could be injected directly into the user’s veins with the included needle. The company promised that its cocaine products would “supply the place of food, make the coward brave, the silent eloquent and ... render the sufferer insensitive to pain.”

By the late Victorian era cocaine use had appeared as a vice in literature, for example as the cucaine injected by Arthur Conan Doyle’s fictional Sherlock Holmes.

In early 20th-century Memphis, Tennessee, cocaine was sold in neighborhood drugstores on Beale Street, costing five or ten cents for a small boxful. Stevedores along the Mississippi River used the drug as a stimulant, and white employers encouraged its use by black laborers.^[16]

In 1909, Ernest Shackleton took “Forced March” brand cocaine tablets to Antarctica, as did Captain Scott a year later on his ill-fated journey to the South Pole.^[17] Even as late as 1938, the Larousse Gastronomique was published carrying a recipe for “cocaine pudding”

Medicalization

With the discovery of this new alkaloid, Western medicine was quick to exploit the possible uses of this plant.

In 1879, Vassili von Anrep, of the University of Würzburg, devised an experiment to demonstrate the analgesic properties of the newly-discovered alkaloid. He prepared two separate jars, one containing a cocaine-salt solution, with the other containing merely salt water. He then submerged a frog's legs into the two jars, one leg in the treatment and one in the control solution, and proceeded to stimulate the legs in several different ways. The leg that had been immersed in the cocaine solution reacted very differently than the leg that had been immersed in salt water.^[12]

Carl Koller (a close associate of Sigmund Freud, who would write about cocaine later) experimented with cocaine for ophthalmic usage. In an infamous experiment in 1884, he experimented upon himself by applying a cocaine solution to his own eye and then pricking it with pins. His findings were presented to the Heidelberg Ophthalmological Society. Also in 1884, Jellinek demonstrated the effects of cocaine as a respiratory system anesthetic. In 1885, William Halsted demonstrated nerve-block anesthesia,^[13] and James Corning demonstrated peridural anesthesia.^[14] 1898 saw Heinrich Quincke use cocaine for spinal anaesthesia.

In 2005, researchers from Kyoto University Hospital proposed the use of cocaine in conjunction with phenylephrine administered in the form of an eye drop as a diagnostic test for Parkinson's disease.

Isolation

Although the stimulant and hunger-suppressant properties of coca had been known for many centuries, the isolation of the cocaine alkaloid was not achieved until 1855 . Many scientists had attempted to isolate cocaine, but none had been successful for two reasons: the knowledge of chemistry required was insufficient at the time, and the cocaine was worsened because coca does not grow in Europe and ruins easily during travel.

The cocaine alkaloid was first isolated by the German chemist Friedrich Gaedcke in 1855. Gaedcke named the alkaloid "erythroxyline", and published a description in the journal Archiv der Pharmazie.^[9]

In 1856, Friedrich Wöhler asked Dr. Carl Scherzer, a scientist aboard the Novara (an Austrian frigate sent by Emperor Franz Joseph to circle the globe), to bring him a large amount of coca leaves from South America. In 1859, the ship finished its travels and Wöhler received a trunk full of coca. Wöhler passed on the leaves to Albert Niemann, a Ph.D. student at the University of Göttingen in Germany, who then developed an improved purification process.^[10]

Niemann described every step he took to isolate cocaine in his dissertation titled Über eine neue organische Base in den Cocablättern (On a New Organic Base in the Coca Leaves), which was published in 1860—it earned him his Ph.D. and is now in the British Library. He wrote of the alkaloid's “colourless transparent prisms” and said that, “Its solutions have an alkaline reaction, a bitter taste, promote the flow of saliva and leave a peculiar numbness, followed by a sense of cold when applied to the tongue.” Niemann named the alkaloid “cocaine”—as with other alkaloids its name carried the “-ine” suffix (from Latin -ina).^[10]

The first synthesis and elucidation of the structure of the cocaine molecule was by Richard Willstätter in 1898.^[11] The synthesis started from tropinone, a related natural product and took five steps.

Coca Leaf

For over a thousand years South American indigenous peoples have chewed the coca leaf (Erythroxylon coca), a plant that contains vital nutrients as well as numerous alkaloids, including cocaine. The leaf was, and is, chewed almost universally by some indigenous communities—ancient Peruvian mummies have been found with the remains of coca leaves, and pottery from the time period depicts humans, cheeks bulged with the presence of something on which they are chewing.^[6] There is also evidence that these cultures used a mixture of coca leaves and saliva as an anesthetic for the performance of trepanation

When the Spaniards conquered South America, they at first ignored aboriginal claims that the leaf gave them strength and energy, and declared the practice of chewing it the work of the Devil. But after discovering that these claims were true, they legalized and taxed the leaf, taking 10% off the value of each crop. These taxes were for a time the main source of support for the Roman Catholic Church in the region.

COCAINE

Cocaine (benzoylmethyl ecgonine) is a crystalline tropane alkaloid that is obtained from the leaves of the coca plant.^[5] The name comes from "coca" in addition to the alkaloid suffix -ine, forming cocaine. It is both a stimulant of the central nervous system and an appetite suppressant. Specifically, it is a dopamine reuptake inhibitor, a noradrenaline reuptake inhibitor and a serotonin reuptake inhibitor. Because of the way it affects the mesolimbic reward pathway, cocaine is addictive. Nevertheless, cocaine is used in medicine as a topical anesthetic, even in children, specifically in eye, nose and throat surgery.

Its possession, cultivation, and distribution are illegal for non-medicinal and non-government sanctioned purposes in virtually all parts of the world. Although its free commercialization is illegal and has been severely penalized in virtually all countries, its use worldwide remains widespread in many social, cultural, and personal settings.

ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chessplayer, and countless other feats never before possible. Find out how the military is applying AI logic to its hi-tech systems, and how in the near future Artificial Intelligence may impact our lives.

TECHNIQUES ASSOCIATED WITH SYSTEMS BIOLOGY

According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are:

• Transcriptomics: whole cell or tissue gene expression measurements by DNA microarrays or serial analysis of gene expression
• Proteomics: complete identification of proteins and protein expression patterns of a cell or tissue through two-dimensional gel electrophoresis and mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins.
• Metabolomics: identification and measurement of all small-molecules metabolites within a cell or tissue
• Glycomics: identification of the entirety of all carbohydrates in a cell or tissue.

In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:

• Interactomics which is used mostly in the context of protein-protein interaction but in theory encompasses interactions between all molecules within a cell
• Fluxomics, which deals with the dynamic changes of molecules within a cell over time
• Biomics: systems analysis of the biome.

The investigations are frequently combined with large scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.

The investigations of a single level of biological organization (such as those listed above) are usually referred to as Systematic Systems Biology. Other areas of Systems Biology includes Integrative Systems Biology, which seeks to integrate different types of information to advance the understanding the biological whole, and Dynamic Systems Biology, which aims to uncover how the biological whole changes over time (during evolution, for example, the onset of disease or in response to a perturbation). Functional Genomics may also be considered a sub-field of Systems Biology.

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks.^[13][14] For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used. Other aspects of computer science and informatics are also used in systems biology. These include new forms of computational model, such as the use of process calculi to model biological processes, the integration of information from the literature, using techniques of information extraction and text mining, the development of online databases and repositories for sharing data and models (such as BioModels Database), approaches to database integration and software interoperability via loose coupling of software, websites and databases ^[15] and the development of syntactically and semantically sound ways of representing biological models, such as the Systems Biology Markup Language.

SYSTEMS BIOLOGY

Systems biology is a relatively new biological study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (integration instead of reduction) to study them. Particularly from year 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts. Because the scientific method has been used primarily toward reductionism, one of the goals of systems biology is to discover new emergent properties that may arise from the systemic view used by this discipline in order to understand better the entirety of processes that happen in a biological system.

Systems biology can be considered from a number of different aspects:

• Some sources discuss systems biology as a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway).^[1][2]

• Other sources consider systems biology as a paradigm, usually defined in antithesis to the so-called reductionist paradigm, although fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations:

"The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models" Science^[3]

"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^[4]

• Still other sources view systems biology in terms of the operational protocols used for performing research, namely a cycle composed of theory, computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.^[5].^[6] Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.

• Finally, some sources see it as a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

SILENCING GENOMES

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HOMOLOGY MODELING

In protein structure prediction, homology modeling, also known as comparative modeling, is a class of methods for constructing an atomic-resolution model of a protein from its amino acid sequence (the "query sequence" or "target"). Almost all homology modeling techniques rely on the identification of one or more known protein structures (known as "templates" or "parent structures") likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. The sequence alignment and template structure are then used to produce a structural model of the target. Because protein structures are more conserved than protein sequences, detectable levels of sequence similarity usually imply significant structural similarity.^[1]

The quality of the homology model is dependent on the quality of the sequence alignment and template structure. The approach can be complicated by the presence of alignment gaps (commonly called indels) that indicate a structural region present in the target but not in the template, and by structure gaps in the template that arise from poor resolution in the experimental procedure (usually X-ray crystallography) used to solve the structure. Model quality declines with decreasing sequence identity; a typical model has ~2 Å agreement between the matched C^α atoms at 70% sequence identity but only 4-5 Å agreement at 25% sequence identity. Regions of the model that were constructed without a template, usually by loop modeling, are generally much less accurate than the rest of the model, particularly if the loop is long. Errors in side chain packing and position also increase with decreasing identity, and variations in these packing configurations have been suggested as a major reason for poor model quality at low identity.^[2] Taken together, these various atomic-position errors are significant and impede the use of homology models for purposes that require atomic-resolution data, such as drug design and protein-protein interaction predictions; even the quaternary structure of a protein may be difficult to predict from homology models of its subunit(s). Nevertheless, homology models can be useful in reaching qualitative conclusions about the biochemistry of the query sequence, especially in formulating hypotheses about why certain residues are conserved, which may in turn lead to experiments to test those hypotheses. For example, the spatial arrangement of conserved residues may suggest whether a particular residue is conserved to stabilize the folding, to participate in binding some small molecule, or to foster association with another protein or nucleic acid.

Homology modeling can produce high-quality structural models when the target and template are closely related, which has inspired the formation of a structural genomics consortium dedicated to the production of representative experimental structures for all classes of protein folds.^[3] The chief inaccuracies in homology modeling, which worsen with lower sequence identity, derive from errors in the initial sequence alignment and from improper template selection.^[4] Like other methods of structure prediction, current practice in homology modeling is assessed in a biannual large-scale experiment known as the Critical Assessment of Techniques for Protein Structure Prediction, or CASP.

PROTEIN-PROTEIN DOCKING

In the last two decades, tens of thousands of protein three-dimensional structures have been determined by X-ray crystallography and Protein nuclear magnetic resonance spectroscopy (protein NMR). One central question for the biological scientist is whether it is practical to predict possible protein-protein interactions only based on these 3D shapes, without doing protein-protein interaction experiments. A variety of methods have been developed to tackle the Protein-protein docking problem, though it seems that there is still much place to work on in this field.

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services. The computational biology tool best-known among biologists is probably BLAST, an algorithm for determining the similarity of arbitrary sequences against other sequences, possibly from curated databases of protein or DNA sequences. The NCBI provides a popular web-based implementation that searches their databases. BLAST is one of a number of generally available programs for doing sequence alignment.

SOAP and REST-based interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. The main advantages lay in the end user not having to deal with software and database maintanance overheads^[1] Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment) and BSA (Biological Sequence Analysis). The availability of these service-oriented bioinformatics resources demonstrate the applicability of web based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.

HIGH-THROUGHPUT IMAGE ANALYSIS

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:

• high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology)
• morphometrics
• clinical image analysis and visualization
• determining the real-time air-flow patterns in breathing lungs of living animals
• quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
• making behavioral observations from extended video recordings of laboratory animals
• infrared measurements for metabolic activity determination

MODELING BIOLOGICAL SYSTEMS

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

COMPARITIVE GENOMICS

The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov Chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.

Many of these studies are based on the homology detection and protein families computation.

PREDICTION OF PROTEIN STRUCTURE

Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy - aka Mad Cow Disease - prion.) Knowledge of this structure is vital in understanding the function of the protein. For lack of better terms, structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. As of now, most efforts have been directed towards heuristics that work most of the time.

One of the key ideas in bioinformatics is the notion of homology. In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. This currently remains the only way to predict protein structures reliably.

One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

ANALYSIS OF MUTATIONS IN CANCER

In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. New physical detection technology are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors .

ANALYSIS OF PROTEIN EXPRESSION

Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.

ANALYSIS OF REGULATION

Regulation is the complex orchestration of events starting with an extracellular signal such as a hormone and leading to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process. For example, promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements.

ANALYSIS OF GENE EXPRESSION

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

MEASURING BIODIVERSITY

Biodiversity of an ecosystem might be defined as the total genomic complement of a particular environment, from all of the species present, whether it is a biofilm in an abandoned mine, a drop of sea water, a scoop of soil, or the entire biosphere of the planet Earth. Databases are used to collect the species names, descriptions, distributions, genetic information, status and size of populations, habitat needs, and how each organism interacts with other species. Specialized software programs are used to find, visualize, and analyze the information, and most importantly, communicate it to other people. Computer simulations model such things as population dynamics, or calculate the cumulative genetic health of a breeding pool (in agriculture) or endangered population (in conservation). One very exciting potential of this field is that entire DNA sequences, or genomes of endangered species can be preserved, allowing the results of Nature's genetic experiment to be remembered in silico, and possibly reused in the future, even if that species is eventually lost.

COMPUTATIONAL EVOLUTIONARY BIOLOGY

Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists in several key ways; it has enabled researchers to:

• trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
• more recently, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, lateral gene transfer, and the prediction of factors important in bacterial speciation,
• build complex computational models of populations to predict the outcome of the system over time
• track and share information on an increasingly large number of species and organisms

GENOME ANNOTATION

In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. The first genome annotation software system was designed in 1995 by Dr. Owen White, who was part of the team that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. Dr. White built a software system to find the genes (places in the DNA sequence that encode a protein), the transfer RNA, and other features, and to make initial assignments of function to those genes. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA are constantly changing and improving.

SEQUENCE ANALYSIS

Since the Phage Φ-X174 was sequenced in 1977, the DNA sequences of hundreds of organisms have been decoded and stored in databases. The information is analyzed to determine genes that encode polypeptides, as well as regulatory sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Today, computer programs are used to search the genome of thousands of organisms, containing billions of nucleotides. These programs would compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, in order to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae) does not give a sequential list of nucleotides, but instead the sequences of thousands of small DNA fragments (each about 600-800 nucleotides long). The ends of these fragments overlap and, when aligned in the right way, make up the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. In the case of the Human Genome Project, it took several months of CPU time (on a circa-2000 vintage DEC Alpha computer) to assemble the fragments. Shotgun sequencing is the method of choice for virtually all genomes sequenced today, and genome assembly algorithms are a critical area of bioinformatics research.

Another aspect of bioinformatics in sequence analysis is the automatic search for genes and regulatory sequences within a genome. Not all of the nucleotides within a genome are genes. Within the genome of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects--for example, in the use of DNA sequences for protein identification.

MAJOR RESEARCH AREAS

1) Sequence analysis
2) Genome annotation
3) Computational evolutionary biology
4) Measuring biodiversity
5) Analysis of gene expression
6) Analysis of regulation
7) Analysis of protein expression
8) Analysis of mutations in cancer
9) Prediction of protein structure
10) Comparative genomics
11) Modeling biological systems
12) High-throughput image analysis
13) Protein-protein docking

BIOINFORMATICS , WHAT IS IT?

Bioinformatics is the application of computer technology to the management of biological information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development. The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project. The goal of this project - determination of the sequence of the entire human genome (approximately three billion base pairs) - will be reached by the year 2002. The science of Bioinformatics, which is the melding of molecular biology with computer science, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists. Such groups will be key to unraveling the mass of information generated by large scale sequencing efforts underway in laboratories around the world.