A graph is a set of nodes and edges where the nodes are objects & the edges the connections between these objects. These can be used to describe diagrams, networks, hierarchies, data flow diagrams, data structures and more. Quite a few packages are available for performing various tasks relating to graphics. These packages include those for creating and manipulating graphics & images, for building 2- & 3-D models for ray tracing, for volume visualization and for converting between various graphics formats. They range from standalone programs to collections of tools to libraries, toolkits for implementing graph editors & graph drawing algorithms. Graphlet is a graph editor, a toolkit for graph editors, a programming language for graphs and a toolkit for graph drawing algorithms. The graph editor toolkit is a powerful toolkit for graph editors & is implemented in C++, LEDA, Tcl/Tk and Graphscript. Graphscript is a high level scripting language based on Tcl/TK with which users can customize and extend graph editors. Graph drawing algorithms help turn abstract graph structures into nicely arranged sets of nodes and edges.

Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology. With the availability of different types of AI algorithms, it has become common for the researchers to apply the off-shelf systems to classify and mine their databases. At present, with various intelligent methods available in the literature, researchers are facing difficulties in choosing the best method that could be applied to a specific data set. Researchers need tools, which present the data in a comprehensible fashion, annotated with context, estimates of accuracy and explanation. Important problems where AI approaches are particularly promising (and often already very successful) include the prediction of protein's structure & function, semiautomatic drug design, the interpretation of nucleotide sequences and knowledge acquisition from genetic data.

Barcode of Life Data System (bold) is an informatics workbench aiding the acquisition, storage, analysis and publication of DNA barcode records. By assembling molecular, morphological and distributional data, it bridges a traditional bioinformatics chasm.boldis freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances. Molecular barcode arrays have been used for phenotypic profiling, drug sensitivity and systematic synthetic lethal analysis .These microarray-based methods facilitate the prediction & definition of gene function and have a broad application in drug discovery & development

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 & sub-cellular localization, morphometrics, clinical image analysis & visualization, infrared measurements for metabolic activity determination. Image analysis related techniques (like wavelet) have also been found useful in bioinformatics problems, such as sequence analysis. The potential of mining the information in bioimages to answer biological questions is enormous and it cries for advanced techniques of bioimage data mining & informatics

Bioimaging is the application of microscopy to the study of cells and organisms. Knowledge of bioimaging techniques is now essential in many types of biological and biomedical research. With the development of advanced imaging techniques, the number of biological images (like cellular and molecular images, as well as medical images) acquired in digital forms is growing rapidly. Large-scale bioimage databases are becoming available. Analyzing these images sheds new light for biologists to seek answers to many biological problems. For example, analysis of the spatial distribution of proteins in molecular images can differentiate cancer cell phenotypes. Comparison of in situ gene expression pattern images during embryogenesis helps to delineate the underlying gene networks

Image processing techniques are widely used in analysing biological images, including images of microarrays and two-dimensional electrophoresis gels from which gene & protein expression levels can be measured. Electron microscopy allows the study of cells and tissues at magnification & resolution well beyond those possible by light microscopy. Scanning electron microscopy is used to investigate the surface structure of biological specimens. Image analysis and handling techniques are necessary for dealing with the large amount of data generated by microscopy techniques.The overview of such techniques will give you the background you need to help design your research project. CT or Computerized Tomography refers to a diagnostic imaging technique which uses X-rays and a computer to view organs & other features inside the body. It is a method of examining body organs by scanning them with X- rays and using a computer to construct a three-dimensional image of that structure. Magnetic Resonance Imaging (MRI) refers to a non-invasive nuclear procedure for imaging tissues of high fat and water content that cannot be seen with other radiological techniques

Currently, the Bioinformatics Core has two available servers, a Sun Enterprise 450 Unix server with RAID array and dedicated backup. This is used for DNA sequence analysis & assembly, and is essential for some services that are provided by the Bioinformatics Core & a Dell PowerEdge 4400 running Red Hat Linux. A BASE database is installed on this server for supporting microarray data capture and archival. The BASE database format can export microarray data in MIAME format, using the MAGE-ML standard.

Abstract Bioinformatics, the application of computational tools to the management and analysis of biological data, has stimulated rapid research advances in genomics through the development of data archives such as GenBank & similar progress is just beginning within ecology. One reason for the belated adoption of informatics approaches in ecology is the breadth of ecologically pertinent data (from genes to the biosphere) and its highly heterogeneous nature. The variety of formats, logical structures and sampling methods in ecology create significant challenges. Cultural barriers further impede progress, especially for the creation and adoption of data standards. Informatics frameworks for ecology has been developed from subject-specific data warehouses, to generic data collections that use detailed metadata descriptions and formal ontologies to catalog & cross-reference information. Combining these approaches with automated data integration techniques and scientific workflow systems will maximize the value of data & open new frontiers for research in ecology.

The distribution and sharing of data & methods is essential not only for distributing the computational demands implied by the large databases, but also to cater to effective interaction between members of multidisciplinary teams. Networking can be performed locally (by LANs) and on a wide scale (Internet). Software engineering skills are needed to build systems & software packages that can be used by scientists, and designing effective user interfaces for these requires human-computer interaction principles to be applied. Web technologies are often used to make bioinformatics data sets and tools available to the scientific community. Many other areas of IT are also used in bioinformatics. Advances in internet technology have largely affected the bioinformatics resources as heterogeneous sources of information. It facilitates the uniform access to the educational, academic & research information sources to bioinformaticians for their research & developmental activities. Access to sequence data is critical, and much of the new sequence data are distributed over the internet. Currently there are at least 400 internet-accessible databases of biological data. The internet also provides a means to distribute software and enables researchers to perform sophisticated analyses on remote servers.

Modelling and simulation of biological systems is used as a predictive method to investigate the potential impact of manipulations at the genetic or molecular level. Modelling itself can help to capture the knowledge of biological systems in an unambiguous way to obtain a comprehensive yet precise & detailed overview of any given system. It requires a deep understanding of biological processes, as well as a range of mathematical and computational techniques. The many potential applications in drug development, genetic engineering, as well as applied and basic research in modern biology has led to systems' biology emerging as a key theme in the post-genome research agenda for the biological sciences community. Despite the current enthusiasm for harnessing mathematical & computational methods to formalise & integrate data and process descriptions in the biological sciences, there are several important challenges & unsolved problems that still exist before models of whole organisms can be considered a realistic goal.

Buffer is a freeware program that accepts a valid pH value & returns one or more methods of preparation of suitable buffers and a number of pH-indicators that change colors close to the pH value entered

MathType applies the rules of mathematical typesetting as one type. It automatically chooses fonts, style, spacing and position as one enters the equation. One can modify MathType's rules to accommodate your own style, or switch between automatic formatting and plain text modes with a single keystroke. For maximum flexibility and control, MathType gives you the ability to nudge equation elements in 1/4 point increments. MathType comes with over 500 mathematical symbols and templates. Fractions, radicals, sums, integrals, products, matrices, various types of brackets and braces . One can also use any character from any font installed on his system. MathType also includes Euclid Math Font Set, to give a document's text and mathematical notation a consistent, industry-standard TeX/LaTeX Computer Modern look.

Although bacterial in vivo expression is widely used for high-throughput production of heterologous proteins, many of the steps in the process, such as transformation, synchronization of cultures, addition of the transcription inducing agents, cell lysis and centrifugation/filtration/separation steps, are time consuming & often require manual intervention during automation thereby reducing throughput. An alternative for proteomic

Proteomics is the study of proteins and their interactions in a cell. Though proteome reflects more accurately on the dynamic state of a cell, tissue, or organism, much is expected from proteomics to yield better disease markers for diagnosis and therapy monitoring. The advent of proteomics technologies for global detection and quantitation of proteins creates new opportunities & challenges for those seeking to gain greater understanding of diseases. High-throughput proteomics technologies combining with advanced bioinformatics are extensively used to identify molecular signatures of diseases based on protein pathways & signaling cascades. Mass spectrometry plays a vital role in proteomics and has become an indispensable tool for molecular & cellular biology. While the potential is great, many challenges and issues remain to be solved, such as mining low abundant proteins & integration of proteomics with genomics & metabolomics data. Nevertheless, proteomics is the foundation for constructing and extracting useful knowledge to biomedical research.

Molecular drawing programs are mostly used to create simple, two-dimensional illustrations for research, lectures, academic publications and similar purposes. The graphic representation of molecular structures is a fundamental language for describing physical reality, on the atomic level, and to accurately represent these images with high precision on a two-dimensional surface. It is an incredibly powerful tool for the process of understanding the nature of the molecule. ChemDraw 9.0, is a great molecular drawing program chalk to draw molecular structures, formulas, mechanisms and reaction pathways. Suddenly, it is possible for almost anyone to electronically produce these important two-dimensional images. ChemDraw has continued to develop in both breadth & depth over the last 20 years, and ChemDraw 9.0 offers an incredibly powerful package that continues to maintain itself as the chemical structure drawing standard

Research in the Chemical Engineering focuses on manipulation of cells, DNA, and proteins for applications in biotechnology & clinical medicine. Both theoretical and experimental tools are developed to address current scientific & engineering issues of high societal impact. Current efforts of bioengineering research are in the areas of genomics and metabolic engineering, bioinformatics, biosensors, biomimetic systems, blood flow regulation & artificial blood substitutes. Cutting edge technology, such as DNA microarray and nano fabrication technology are being used in conjunction with mathematical tools to advance bioscience & engineering. Significant progress includes the development of an artificial gene circuit for production of a novel product in E. coli, a hierarchical model for a DNA microarray, discovery of a novel thermostable, chiral-specific enzyme for L-amino acid synthesis, construction of a novel metabolic pathway in E. coli that synthesizes a large variety of important bioactive molecules & discovery of a novel mechanism that regulates blood flow.

Chemistry has a tradition of ensuring quality through reporting properties & analysis, so every new compound (and many re-synthesised ones) must have published measurements of properties to justify their identity & purity. Because structure predicts properties, the pharmaceutical industry tests many compounds for biological activity for drug discovery. The data in these public publications is a major feedstock for the chemical information industry. Increased availability of large repositories of chemical compounds is creating new challenges and opportunities for the application of data-mining techniques to problems in chemical informatics. Applications of cheminformatics lie in performing virtual high-throughput screening to identify active compounds in a virtual library and predicting structure-activity relationships. Often chemical compounds are represented as graphs where the nodes represent atoms and the edges represent covalent bonds between them. Thus the challenge is to develop techniques capable of answering queries of interest to chemists over large chemical libraries. Such queries include similarity searches, sub-structure queries and finding motifs. Finding motifs can be investigated from two perspectives, one of finding the frequent ones, and the other of finding the significant structures. Algorithms that combine domain knowledge of chemical compounds with computer science techniques to efficiently answer these queries have been developed.

. Chemical inventory management focuses specifically on controlling the activities involved with chemicals used by the organization. Best practices in chemical inventory management require taking advantage of the latest strategies, such as Pareto analysis, Just in Time (JIT) and Material Requirements Planning (MRP) & implementing them in the chemical inventory system. Doing so will enable your organisation to perform effective chemical stock taking. With the increased use of commercial software and the outsourcing of Information technology tasks, many organisations have implemented an off-the-shelf Chemical Inventory System (CIS) solution to ensure that the processes used to manage the chemical inventory are the most effective possible.

Cheminformatics, the combination of chemical synthesis, biological screening & data mining approaches used to guide drug discovery and development. Cheminformatic tools that allow for the rational selection of designed compounds with drug like properties from an almost infinite number of synthetic possibilities, building smarter focused libraries for virtual and high throughput screening & the exploitation of previously obtained discovery data to guide, lead optimization efforts that are all important. Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug/lead identification & optimization. This area covers especially expert systems, algorithm development, information processing, storing chemical information, data mining, machine learning, structured data mining, databases, optimization problems, statistical analysis, chemometrics & data analysis.

Chemometrics is the application of statistics to the analysis of chemical data (from organic, analytical or medicinal chemistry) and design of chemical experiments & simulations. Chemometrics is an approach to analytical chemistry based on the idea of indirect observation. Measurements related to the chemical composition of a substance are taken, and the value of a property of interest is inferred from them through some mathematical relation. Basically, chemometrics is a process. Measurements are made, data is collected, and information is obtained to periodically assess & acquire knowledge. This, in turn, has led to a new approach for solving methodical problems: (1) measure a phenomenon or process using chemical instrumentation that generates data inexpensively, (2) analyze the multivariate data, (3) iterate if necessary, (4) create & test the model, and (5) develop fundamental multivariate understanding of the process. Chemoinformatics is a subfield of chemometrics, which encompasses the analysis, visualization, and for use of chemical structural information as a surrogate variable evidently has this term been coined. Chemometrics techniques are applied to characterize and differentiate diverse microbial species & strains using Fourier transform infrared spectroscopy.

HPLC is probably the most common method used for targeted analysis of plant materials, and for metabolic profiling of individual classes. A derivatisation step is not essential (unless needed for detection), since involatile and volatile substances may be measured equally well. Selection of compounds arises initially from the type of solvent used for extraction (as with all methods that use an extraction step), and then from the type of column & detector. For example HPLC/UV will only detect compounds with a suitable chromophore, a column selected for its ability to separate one class of compounds will not generally be useful for other types. HPLC profiling methods all rely to a great extent on comparisons with reference compounds. The full UV spectrum (measured for each peak when UV-diode array detectors are used) gives some useful information on the nature of compounds in complex profiles, but often indicates the class of the compound rather than its exact identity.

The development of robust high-performance liquid chromatography (HPLC) technologies continues to improve the detailed analysis and sequencing of glycan structures released from glycoproteins. A database (GlycoBase) & analytical tool (autoGU) to assist the interpretation and assignment of HPLC-glycan profiles is presented. GlycoBase is a relational database which contains the HPLC elution positions for over 350 2-AB labelled N-glycan structures together with predicted products of exoglycosidase digestions. AutoGU assigns provisional structures to each integrated HPLC peak and, when used in combination with exoglycosidase digestions, progressively assigns each structure automatically based on the footprint data. These tools are potentially very promising and facilitate basic research, as well as the quantitative high-throughput analysis of low concentrations of glycans released from glycoproteins

A database was developed to store, organize & retrieve the data associated with electrophoresis and chromatography separations. It allows laboratories to store extensive data on separation techniques (analytical & preparative). The data for gel electrophoresis includes gel composition, staining methods, electric fields, analysis & samples loaded. The database stores data on chromatography conditions, the samples used and the fractions collected. The data structure of this database was designed to maintain the link between samples (including fractions) from chromatography separations and their analysis by gel electrophoresis. The database will allow laboratories to organize and maintain a large amount of separation & sample data in a uniform data environment. It will facilitate the retrieval of the separation history of important samples and the separation conditions used.

Using a combinatorial process to prepare sets of compounds from sets of building blocks is called Combinatorial Chemistry. The design & synthesis of compound libraries for identification of lead structures is long and costly. Synthesis of an almost unlimited number of organic compounds covering as much of chemistry space as possible is no longer the most cost effective & time saving approach to hit identification. Creating libraries, using biological target structures to inform chemical design, facilitated by quantum advances in structural genomics and computational capabilities, is a smarter, more efficient way to produce good initial leads. Considering solubility, permeability & other drug-like properties early in library design and introducing both target & lead structural constraints in lead development are further ways to ensure more compounds make it to trial. There is not enough matter in the universe to prepare all possible combinatorial variations. Combinatorial chemistry generates lead compounds that could exhibit biological activity against a particular target. Since hundreds of thousands, even millions, of compounds can be involved, combinatorial chemistry is characterized by the vast amounts of data involved and the information management challenges they pose.

Computational chemistry is the branch of theoretical chemistry whose major goals are to create efficient computer programs that calculate the properties of molecules (such as total energy, dipole moment, vibrational frequencies) and to apply these programs to concrete chemical objects. It is also sometimes used to cover the areas of overlap between computer science and chemistry. Computational chemistry methods are being developed & utilized to quantitatively characterize molecular components & interactions at a local (ligand-receptor) scale. Deterministic and stochastic system process analysis & optimization techniques, are applied towards elucidation of larger network structures (interlinked signaling, regulatory & metabolic pathways). The latter process relies on interpretation of data from network perturbation experiments, that may include consideration of genetic perturbations, environmental perturbations and disease state. Meanwhile, a great deal of software development activity is being expended on the software firms' core computational chemistry programs. Indeed, new versions of many flagship software packages have just been released, and they & some entirely new programs are bringing new levels of capability, flexibility, speed & accuracy to the application of the technology.

Computational Fluid Dynamics (CFD) is an area in which the governing equations for fluid flow are solved in discrete form on computers by simulating the fluid flow problem. This helps in reducing the time and effort required in narrowing down on the design configurations of various engineering components. The use of CFD can also help in enhancing the quality of the products, as the designers can look at the product from all possible angles before prototyping it. For example, in case of engineering equipment like industrial heat exchangers, the design can be optimized by the use of CFD to save energy in operation, which is a very vital & precious input for design industries. A realistic CFD problem simulation to resolve all possible phenomena places very high demands on speed and memory of the computer systems. It has been proved quite convincingly that the only way to perform such computations is by the use of parallel computers. In the case of aircraft design the time to arrive at an efficient aerodynamic configuration can be reduced by orders of magnitude by performing simulations

Computed Tomography (CT) is a medical imaging method employing tomography. Digital geometry processing is used to generate a three-dimensional image of the inside of an object from a large series of two-dimensional X-ray images taken around a single axis of rotation. Computed tomography was originally known as the EMI scan, as it was developed at a research branch of EMI, a company best known today for its music and recording business. CT produces a volume of data which can be manipulated, through a process known as windowing, in order to demonstrate various structures based on their ability to block the X-ray/Röntgen beam. Although, the images generated were in the axial or transverse plane (orthogonal to the long axis of the body), modern scanners allow this volume of data to be reformatted in various planes or even as volumetric (3D) representations of structures. Although most common in healthcare, CT is also used in other fields, for example nondestructive materials testing and to study biological & paleontological specimens.

The Computer Automated Measurement And Control system is a modular instrumentation and digital interface system defined as a standardized instrumentation system. It features a fully specified data highway together with modular functional units that are completely compatible and that are available from diverse sources. The CAMAC system is used at almost every nuclear physics research laboratory and many industrial sites, primarily for data acquisition but also for remotely programmable trigger & logic applications. Its function is to provide a scheme allowing a wide range of modular instruments to be interfaced to a standardized backplane called a DATAWAY, which is interfaced with a computer. The DATAWAY provides module power & address, control & data bases, and the lines include digital data transfer, strobe signal, addressing & control lines. CAMAC-related software is for Linux systems.

There are two main ways in which the structures of proteins are solved, X-ray crystallography and NMR spectroscopy. Xraycrystallography involves exposing crystallized proteins to beams of X-rays which are diffracted onto & capture by an X-ray film or digital camera. From these diffractions, the exact x, y and z coordinates of the heavy atoms can be determined. NMRspectroscopy, however, measures radio wave absorption by atomic nuclei to determine how much nuclear magnetism is being transferred from one atom to another. Plugging in the values to a computer based program returns several protein structures, which provides an ensemble of different structural configurations for that protein.

Differential Scanning Calorimetry (DSC) is a relatively rapid, straightforward and nonperturbing technique for studying the thermotropic phase behavior of hydrated lipid dispersions & of reconstituted lipid models or biological membranes. However, because of the diversity of lipid thermotropic phase behavior, data-acquisition and data-analysis protocols must be modified according to the nature of the phase transition. The information content of DSC thermograms is affected by the nature of the lipid phase transition. A wide variety of temperature-induced transitions in biological systems may be possible by differential scanning calorimetry (DSC). This includes processes, such as thermal unfolding (denaturation) of proteins, lipid membrane phase transitions, nucleic acid melting & so forth.

Digital Signal Processing (DSP) is the study of signals in a digital representation and of the processing methods of these signals. Examples of digital signals include speech in mobile phones, digital audio in CDs & mp3-players, biomedical imaging in digital tomography, images & video in digital cameras, and measurement signals in different industrial devices. Digital signal processing is useful in situations where the physical world and computers meet, which means that there are lot of applications & new ones will emerge as the methods used in the field are developed. Digital signal processing technique has been applied on the genomic data sequences in order to identify and compare DNA & protein sequences of Eucalyptus grandis to the other available higher plant plastomes.

Stability testing of biological drugs should be more comprehensive than that of small molecule compounds. It should cover many more aspects of drug development & manufacturing, including holding time, shipping, freeze/thaw cycles, temperature, exposure to oxygen & light, physical stress, and excipient & container compatibility. Some characterization work should be done early in the development of a drug to identify the degradation pathways so that potential problems associated with stability, storage conditions and formulation design can be avoided during clinical trials. Forced degradation studies exposes a drug substance to harsher conditions than the product would be expected to experience and determining at what point & how it degrades. Some characterization work should be done early in the development of a drug to identify the degradation pathways so that potential problems associated with stability, storage conditions and formulation design can be avoided during clinical trials. A good understanding of product stability early on can help companies make assessments and comparisons on product comparability following manufacturing changes.

Electronic Data Interchange (EDI) can be formally defined as, 'the transfer of structured data, by agreed message standards, from one computer system to another without human intervention'. EDI implies a sequence of messages between two parties, either of whom may serve as originator or recipient. The formatted data representing the documents may be transmitted from originator to recipient via telecommunications or physically transported on electronic storage media. It goes on further to say that, in EDI, the usual processing of received messages is by computer only. Human intervention in the processing of a received message is typically intended only for error conditions, for quality review and for special situations. For example, the transmission of binary or textual data is not EDI as defined here unless the data is treated as one or more data elements of an EDI message & are not normally intended for human interpretation as part of on-line data processing. The EDI process provides many benefits. Computer-to-computer exchange of information is much less expensive than handling paper documents. EDI transactions between companies flow faster and more reliably than paper documents. Faster transactions support reduction in inventory levels, better use of warehouse space, fewer out-of-stock occurrences and lower freight costs through fewer emergency expedites.

One of the main problems of writing Systems Biology Markup Language ( SBML) file by hand is the use of the Mathematical Markup Language (MathML) to describe all the mathematical portions of the model. MathML expressions are long, complex & therefore error-prone. To facilitate the editing of mathematical expressions, a small equation editor has been designed based on jex. The user can type the equation as free text, in a natural infix notation, that is subsequently transformed into valid MathML using the subset of MathML elements supported by SBML . The math editor also provides several drop-down menus to display the different elements that are already defined in the model & can be included in the equation (again using the name instead of the id to improve the readability of the equation). There is also the possibility to add new SBML elements compartments, species or parameters from within the math editor window. This avoids the necessity to go back to the main window if the declaration of an element has been forgotten.

Peptide Mass Fingerprinting (PMF) is a technique used to identify proteins by matching their constituent fragment masses (peptide masses) to the theoretical peptide masses generated from a protein or DNA database. The first step in PMF is that an intact, unknown protein is cleaved with a proteolytic enzyme to generate peptides. With PMF, heterogeneity is most commonly imparted to the unknown protein with a trypsin digestion. The premise of peptide mass finger printing is that every unique protein will have a unique set of peptides and hence unique peptide masses. Identification is accomplished by matching the observed peptide masses to the theoretical masses derived from a sequence database. PMF identification relies on observing a large number of peptides, 5+, from the same protein at high mass accuracy. This technique does well with 2D gel spots where the protein purity is high. PMF protein identification can run into difficulties with complex mixtures of proteins. Low level ID also becomes difficult due to commonplace contamination by keratin.

The finite element method is a method for solving partial differential equations (PDEs). For example a PDE will involve a function u(x) defined for all x in the domain with respect to some given boundary condition. The purpose of the method is to determine an approximation to the function u(x). This method requires the discretisation of the domain into subregions or cells. For example, a two-dimensional domain can be divided and approximated by a set of triangles (the cells). On each cell the function is approximated by a characteristic form. For example u(x) can be approximated by a linear function on each triangle. The method is applicable to a wide range of physical and engineering problems. Finite element method is a powerful technique originally developed for numerical solutions of complex problems in structural mechanics, and it remains the method of choice for complex systems. In FEM, the structural system is modeled by a set of appropriate finite elements interconnected at points called nodes. Elements may have physical properties such as thickness, coefficient of thermal expansion, density, Young's modulus, shear modulus and Poisson's ratio.

Flow cytometry describes the analysis of light diffraction and fluorescent properties of a population of cells or particles as they pass through one or more lasers. It is a powerful technique as it provides real quantitative information on a cell by cell or particle by particle basis. Flow cytometry can be used to determine a number of cell based parameters including size and granularity (internal complexity) of the cell, the cell surface expression of particular proteins using antibodies & fluorescent reporters, intracellular protein levels like chemokines, DNA content & cell cycle analysis, cellular uptake of particular fluorescent agents including CLIO-based particles & cell proliferation using cytoplasmic dyes.

Infrared (IR) spectroscopy is well established as a valuable technique for assessing protein secondary structure in solutions. One particular form of IR spectroscopy, Fourier transform infrared spectroscopy (FTIR), has become an especially preferred form of IR spectroscopy for the study of protein secondary structure. FTIR has great utility in the rapid determination of secondary structure because it offers accurate, high-resolution spectra with excellent sensitivity & signal-to-noise (S/N) ratios, as compared to other forms of infrared spectroscopy. Over the last thirty years, these properties of FTIR have been increasingly recognized and FTIR has developed into a reliable & accurate technique for the identification of structural features of a variety of samples, including protein secondary structure. The possibility of Fourier transform infrared (FT-IR) spectroscopy to assess the overall molecular composition of microbial cells in a non-destructive manner is reflected in the specific spectral fingerprints highly typical for different microorganisms. With the objective of using FT-IR spectroscopy for discrimination between diverse microbial species and strains on a routine basis, a wide range of chemometrics techniques need to be applied.

Gas Chromatography (GC) is a chemical analysis instrument for separating chemicals in a complex sample. A known volume of gaseous or liquid analyte is injected into a column. Since each type of molecule has a different rate of progression, the various components of the analyte mixture are separated as they progress along the column and reach the end of the column at different times. The time at which each component reaches the outlet and the amount of that component can be determined. GC combined with mass spectrometry ( MS) is even more powerful & a widely used method where MS is an analytical technique used to measure the mass-to-charge ratio of ions. It is most generally used to find the composition of a physical sample by generating a mass spectrum representing the masses of sample components. Due to GC/MS popularity as a chemical analysis tool, various chemometrics algorithms have been utilized to classify chromatograms from the instrument output.