INTRODUCTION:

In-silico is an expression used to mean "performed on computer or via computer simulation." The phrase was coined in 1989as an analogy to the Latin phrases in-vivo and in-vitro which are commonly used in biology and refer to experiments done in living organisms and outside of living organisms, respectively.

Drug discovery with virtual screening:

In-silico research in medicine is thought to have the potential to speed the rate of discovery while reducing the need for expensive lab work and clinical trials. One way to achieve this is by producing and screening drug candidates more effectively. In 2010, for example, using the protein docking algorithm EADock, researchers found potential inhibitors to an enzyme associated with cancer activity in-silico.

Fifty percent of the molecules were later shown to be active inhibitors in-vitro. This approach differs from use of expensive high-throughput screening (HTS) robotic labs to physically test thousands of diverse compounds a day often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following further testing.¹

Cell models:

Efforts have been made to establish computer models of cellular behavior. For example, in 2007 researchers developed an in-silico model of tuberculosis to aid in drug discovery with a prime benefit cited as being faster than real time simulated growth rates allowing phenomena of interest to be observed in minutes rather than months. More work can be found that focus on modeling a particular cellular process like, for example, the growth cycle of Caulobacter crescentus. These efforts fall far short of an exact, fully predictive, computer model of a cell's entire behavior. Limitations in the understanding of molecular dynamics and cell biology as well as the absence of available computer processing power force large simplifying assumptions that constrain the usefulness of present in-silico models.²

Genetics:

Digital genetic sequences obtained from DNA sequencing may be stored in sequence databases, be analyzed, be digitally altered and/or be used as templates for creating new actual DNA using artificial gene synthesis.

Other examples:

In-silico computer-based modeling technologies have also been applied in:

· Whole cell analysis of prokaryotic and eukaryotic hosts e.g. E. coli, B. subtilis, yeast, CHO- or human cell lines

· Bioprocess development and optimization e.g. optimization of product yields

· Analysis, interpretation and visualization of heterologous data sets from various sources e.g. genome, transcriptome or proteome data

Figure-1: Drug design²

History:

The expression in-silico was first used in public in 1989 in the workshop "Cellular Automata: Theory and Applications" in Los Alamos, New Mexico. Pedro Miramontes, a mathematician from National Autonomous University of Mexico (UNAM) presented the report "DNA and RNA Physicochemical Constraints, Cellular Automata and Molecular Evolution". In his talk, Miramontes used the term "in-silico" to characterize biological experiments carried out entirely in a computer. The work was later presented by Miramontes as his PhD dissertation. In-silico has been used in white papers written to support the creation of bacterial genome programs by the Commission of the European Community. The first referenced paper where "in-silico" appears was written by a French team in 1991. The first referenced book chapter where "in-silico" appears was written by Hans B. Sieburg in 1990 and presented during a Summer School on Complex Systems at the Santa Fe Institute.³

The phrase "in-silico" originally applied only to computer simulations that modeled natural or laboratory processes (in all the natural sciences), and did not refer to calculations done by computer generically.

In-silico versus in-silicio

"In-silico" was briefly challenged by "in-silicio," which is correct Latin for "in-silicon" (the Latin term for silicon, silicium, was created at the beginning of the 19th century by Berzelius. Silex is also a correct latin word). But the phrase "in-silice" means "in flint" in Latin. "In-silico" was perceived as catchier, possibly through similarity to the words "vivo" and "vitro". "In-silico" is now almost universal; it even occurs in a journal title (In-Silico Biology: http://www.bioinfo.de/isb/).

In-silico is reasonable from the viewpoint of (ancient) Greek case endings; the "-on" ending for certain elements is from Greek. In Greek, "silicon" would take the form "silico" in such a phrase. Latin typically uses the correct Greek forms for Greek words when they are used in Latin.⁴

Virtual screening (VS) is a computational technique used in drug discovery research. It involves the rapid in silico assessment of large libraries of chemical structures in order to identify those structures that most likely to bind to a drug target, typically a protein receptor or enzyme.⁵ Virtual screening has become an integral part of the drug discovery process. Related to the more general and long pursued concept of database searching, the term "virtual screening" is relatively new. Walters, et al. define virtual screening as "automatically evaluating very large libraries of compounds" using computer programs.⁶ As this definition suggests, VS has largely been a numbers game focusing on questions like how can we filter down the enormous chemical space of over 10⁶⁰ conceivable compoundsto a manageable number that can be synthesized, purchased, and tested. Although filtering the entire chemical universe might be a fascinating question, more practical VS scenarios focus on designing and optimizing targeted combinatorial libraries and enriching libraries of available compounds from in-house compound repositories or vendor offerings. The purpose of virtual screening to come up with hits of novel chemical structure that bind to the macromolecular target of interest. Thus, success of a virtual screen is defined in terms of finding interesting new scaffolds rather than many hits. Interpretations of VS accuracy should therefore be considered with caution. Low hit rates of interesting scaffolds are clearly preferable over high hit rates of already known scaffolds.

Figure-2: In-silico technique³

Method:

There are two broad categories of screening techniques: ligand-based and structure-based.⁷

Ligand-based:

Given a set of structurally diverse ligands that binds to a receptor, a model of the receptor can be built based on what binds to it. These are known as pharmacophore models. A candidate ligand can then be compared to the pharmacophore model to determine whether it is compatible with it and therefore likely to bind.⁸ Another approach to ligand-based virtual screening is to use chemical similarity analysis methods to scan a database of molecules against one active ligand structure.⁹

Structure-based:

Structure-based virtual screening involves docking of candidate ligands into a protein target followed by applying a scoring function to estimate the likelihood that the ligand will bind to the protein with high affinity.^10-12

Computing Infrastructure:

The computation of pair-wise interactions between atoms, which is a prerequisite for the operation of many virtual screening programs, is of O(N²) computational complexity, where N is the number of atoms in the system. Because of the exponential scaling with respect to the number of atoms, the computing infrastructure may vary from a laptop computer for a ligand-based method to a mainframe for a structure-based method.

Ligand-based:

Ligand-based methods typically require a fraction of a second for a single structure comparison operation. A single CPU is enough to perform a large screening within hours. However, several comparisons can be made in parallel in order to expedite the processing of a large database of compounds.

Structure-based:

The size of the task requires a parallel computing infrastructure, such as a cluster of Linux systems, running a batch queue processor to handle the work, such as Sun Grid Engine or Torque PBS.

A means of handling the input from large compound libraries is needed. This requires a form of compound database that can be queried by the parallel cluster, delivering compounds in parallel to the various compute nodes. Commercial database engines may be too ponderous, and a high speed indexing engine, such as Berkeley DB, may be a better choice. Furthermore, it may not be efficient to run one comparison per job, because the ramp up time of the cluster nodes could easily outstrip the amount of useful work. To work around this, it is necessary to process batches of compounds in each cluster job, aggregating the results into some kind of log file. A secondary process, to mine the log files and extract high scoring candidates, can then be run after the whole experiment has been run.

Computational biology is an interdisciplinary field that applies the techniques of computer science, applied mathematics and statistics to address biological problems. The main focus lies in the development of computational and statistical data analysis methods and in developing mathematical modeling and computational simulation techniques. By these means it addresses scientific research topics with their theoretical and experimental questions without a laboratory. It is connected to the following fields:

· Computational biomodeling, a field concerned with building computer models of biological systems.

· Bioinformatics, which applies algorithms and statistical techniques to the interpretation, classification and understanding of biological datasets. These typically consist of large numbers of DNA, RNA, or protein sequences. Sequence alignment is used to assemble the datasets for analysis. Comparisons of homologous sequences, gene finding, and prediction of gene expression are the most common techniques used on assembled datasets; however, analysis of such datasets have many applications throughout all fields of biology.

· Mathematical biology aims at the mathematical representation, treatment and modeling of biological processes, using a variety of applied mathematical techniques and tools.

· Computational genomics, a field within genomics which studies the genomes of cells and organisms. High-throughput genome sequencing produces lots of data, which requires extensive post-processing (genome assembly) and uses DNA microarray technologies to perform statistical analyses on the genes expressed in individual cell types. This can help find genes of interest for certain diseases or conditions. This field also studies the mathematical foundations of sequencing.

· Molecular modeling, which consists of modelling the behaviour of molecules of biological importance.

· Protein structure prediction and structural genomics, which attempt to systematically produce accurate structural models for three-dimensional protein structures that have not been determined experimentally.

· Computational biochemistry and biophysics, which make extensive use of structural modeling and simulation methods such as molecular dynamics and Monte Carlo method-inspired Boltzmann sampling methods in an attempt to elucidate the kinetics and thermodynamics of protein functions.

· Modeling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of biological systems, like 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.

· It is also directly associated with bioinformatics and computational biology. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

Overview:

It is understood that an unexpected emergent property of a complex system is a result of the interplay of the cause-and-effect among simpler, integrated parts. Biological systems manifest many important examples of emergent properties in the complex interplay of components. Traditional study of biological systems requires reductive methods in which quantities of data are gathered by category, such as concentration over time in response to a certain stimulus. Computers are critical to analysis and modeling of these data. The goal is to create accurate real-time models of a system's response to environmental and internal stimuli, such as a model of a cancer cell in order to find weaknesses in its signaling pathways, or modeling of ion channel mutations to see effects on cardiomyocytes and in turn, the function of a beating heart. A monograph on this topic summarizes an extensive amount of published research in this area up to 1987, including subsections in the following areas: computer modeling in biology and medicine, arterial system models, neuron models, biochemical and oscillation networks, quantum automata, quantum computers in molecular biology and genetics, cancer modeling, neural nets, genetic networks, abstract relational biology, metabolic-replication systems, category theory applications in biology and medicine, automata theory, cellular automata, tessallation models and complete self-reproduction, chaotic systems in organisms, relational biology and organismic theories. This published report also includes 390 references to peer-reviewed articles by a large number of authors.^13-23

Standards:

By far the most widely accepted standard format for storing and exchanging models in the field is the Systems Biology Markup Language (SBML). The SBML.org website includes a guide to many important software packages used in computational systems biology. Other markup languages with different emphases include BioPAX and CellML.²⁴

Cellular model:

Main article: Cellular model is as follows: Fig-3

Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves the use of computer simulations of the many 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.

The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century.

Membrane computing is the task of modeling specifically a cell membrane.

Protein folding:

Main article: Protein folding problem:

Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of a protein's tertiary structure from its primary structure. It is one of the most important goals pursued by bioinformatics and theoretical chemistry. Protein structure prediction is of high importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). Every two years, the performance of current methods is assessed in the CASP experiment.

Human biological systems:

Brain model:

The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. The aim of the project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique in Lausanne, Switzerland, is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a biologically realistic model of neurons.^25-26 It is hoped that it will eventually shed light on the nature of consciousness. There are a number of sub-projects, including the Cajal Blue Brain, coordinated by the Supercomputing and Visualization Center of Madrid (CeSViMa), and others run by universities and independent laboratories in the UK, U.S., and Israel.

Model of the immune system:

The last decade has seen the emergence of a growing number of simulations of the immune system.

Tree model:

Main article: Simulated growth of plants:

Electronic trees (e-trees) usually use L-systems to simulate growth. L-systems are very important in the field of complexity science and A-life. A universally accepted system for describing changes in plant morphology at the cellular or modular level has yet to be devised. The most widely implemented tree generating algorithms are described in the papers "Creation and Rendering of Realistic Trees", and Real-Time Tree Rendering

Ecological models:

Main article: Ecosystem model:

Ecosystem models are mathematical representations of ecosystems. Typically they simplify complex foodwebs down to their major components or trophic levels, and quantify these as either numbers of organisms, biomass or the inventory/concentration of some pertinent chemical element (for instance, carbon or a nutrient species such as nitrogen or phosphorus).

Modeling of infectious disease:

Main articles: Mathematical modeling of infectious disease and Epidemic model:

It is possible to model the progress of most infectious diseases mathematically to discover the likely outcome of an epidemic or to help manage them by vaccination. This field tries to find parameters for various infectious diseases and to use those parameters to make useful calculations about the effects of a mass vaccination programme.²⁷Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It is also directly associated with bioinformatics, computational biology and artificial life. It involves the use of computer simulations of the many 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. The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century.

Overview:

The eukaryotic cell cycle is very complex and is one of the most studied topics, since its misregulation leads to cancers. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups have produced several models of the cell cycle simulating several organisms. They have recently produced a generic eukaryotic cell cycle model which can represent a particular eukaryote depending on the values of the parameters, demonstrating that the idiosyncrasies of the individual cell cycles are due to different protein concentrations and affinities, while the underlying mechanisms are conserved (Csikasz-Nagy et al., 2006).
By means of a system of ordinary differential equations these models show the change in time (dynamical system) of the protein inside a single typical cell; this type of model is called a deterministic process (whereas a model describing a statistical distribution of protein concentrations in a population of cells is called a stochastic process).

To obtain these equations an iterative series of steps must be done: first the several models and observations are combined to form a consensus diagram and the appropriate kinetic laws are chosen to write the differential equations, such as rate kinetics for stoichiometric reactions, Michaelis-Menten kinetics for enzyme substrate reactions and Goldbeter–Koshland kinetics for ultrasensitive transcription factors, afterwards the parameters of the equations (rate constants, enzyme efficiency coefficients and Michealis constants) must be fitted to match observations; when they cannot be fitted the kinetic equation is revised and when that is not possible the wiring diagram is modified. The parameters are fitted and validated using observations of both wild type and mutants, such as protein half-life and cell size. In order to fit the parameters the differential equations need to be studied. This can be done either by simulation or by analysis. In a simulation, given a starting vector (list of the values of the variables), the progression of the system is calculated by solving the equations at each time-frame in small increments.

In analysis, the proprieties of the equations are used to investigate the behavior of the system depending of the values of the parameters and variables. A system of differential equations can be represented as a vector field, where each vector described the change (in concentration of two or more protein) determining where and how fast the trajectory (simulation) is heading. Vector fields can have several special points: a stable point, called a sink, that attracts in all directions (forcing the concentrations to be at a certain value), an unstable point, either a source or a saddle point which repels (forcing the concentrations to change away from a certain value), and a limit cycle, a closed trajectory towards which several trajectories spiral towards (making the concentrations oscillate).
A better representation which can handle the large number of variables and parameters is called a bifurcation diagram (bifurcation theory): the presence of these special steady-state points at certain values of a parameter (e.g. mass) is represented by a point and once the parameter passes a certain value, a qualitative change occurs, called a bifurcation, in which the nature of the space changes, with profound consequences for the protein concentrations: the cell cycle has phases (partially corresponding to G1 and G2) in which mass, via a stable point, controls cyclin levels, and phases (S and M phases) in which the concentrations change independently, but once the phase has changed at a bifurcation event (cell cycle checkpoint), the system cannot go back to the previous levels since at the current mass the vector field is profoundly different and the mass cannot be reversed back through the bifurcation event, making a checkpoint irreversible. In particular the S and M checkpoints are regulated by means of special bifurcations called a Hopf bifurcation and an infinite period bifurcation.²⁸

Nonclinical or Pre-Clinical studies are research studies that are conducted, typically on animals, before a permit for a clinical trial on humans can be obtained. Pre-clinical studies serve a vital role in the drug discovery and development processes. These studies can be used to identify lead compounds likely to possess favorable biopharmaceutic and pharmacokinetic properties in humans. In addition, they can facilitate transition of discovery to development, as well as decrease the need for expensive and time-consuming clinical studies. Pre-clinical studies include a wide range of studies in a variety of systems to characterize biopharmaceutic and pharmacokinetic properties. Systems used include: in-vivo animal models, isolated perfused liver, kidney, intestine, hind limb and heart, Caco-2 cell monolayer absorption model, animal and human liver microsomes. Studies include: bioavailability studies, pharmacokinetic studies, prediction of oral absorption in humans, determination of mechanisms of intestinal absorption, assessment of transport, distribution and elimination of compounds, validated models for cytochrome P450 enzymes, metabolism studies in human liver microsomes, assessment of potential for metabolic drug - drug interactions, analysis of drugs and metabolites in biological matrices, synthetic chemistry, in-silico modeling.

Ex-vivo (Latin: out of the living) means that which takes place outside an organism. In science, ex-vivo refers to experimentation or measurements done in or on tissue in an artificial environment outside the organism with the minimum alteration of natural conditions. Ex-vivo conditions allow experimentation under more controlled conditions than possible in the intact organism, at the expense of altering the "natural" environment.

A primary advantage of using ex-vivo tissues is the ability to perform tests or measurements that would otherwise not be possible or ethical in living subjects. Tissues may be removed in many ways, including in part, as whole organs, or as larger organ systems. Examples of ex-vivo specimen use include:

· assays;

· measurements of physical, thermal, electrical, mechanical, optical and other tissue properties, especially in various environments that may not be life-sustaining (for example, at extreme pressures or temperatures);

· realistic models for surgical procedure development;

· investigations into the interaction of different energy types with tissues;

· or as phantoms in imaging technique development.

· The term ex-vivo is often differentiated from the term in-vitro in that the tissue or cells need not be in culture; these two terms are not necessarily synonymous.

· In cell biology, ex-vivo procedures often involve living cells or tissues taken from an organism and cultured in a laboratory apparatus, usually under sterile conditions with no alterations for up to 24 hours. Experiments lasting longer than this using living cells or tissue are typically considered to be in-vitro. One widely performed ex-vivo study is the chick chorioallantoic membrane (CAM) assay.

In this assay, angiogenesis is promoted on the CAM membrane of a chicken embryo outside the organism (chicken).

In-situ is a Latin phrase meaning in the place. It is used in many different contexts.

Aerospace:

In the aerospace industry, equipment on board aircraft must be tested in-situ, or in place, to confirm everything functions properly as a system. Individually, each piece may work but interference from nearby equipment may create unanticipated problems. Special test equipment is available for this in-situ testing.

Archaeology:

In archaeology, in-situ refers to an artifact that has not been moved from its original place of deposition. In other words, it is stationary, meaning "Still". An artifact being in-situ is critical to the interpretation of that artifact and, consequently, to the culture which formed it. Once an artifact's 'find-site' has been recorded, the artifact can then be moved for conservation, further interpretation and display. An artifact that is not discovered in-situ is considered out of context and will not provide an accurate picture of the associated culture. However, the out of context artifact can provide scientists with an example of types and locations of in-situ artifacts yet to be discovered.

In-situ only expresses that the object has not been "newly" moved. Thus, an archaeological in-situ-find may be an object that was historically looted from another place, an item of "booty" of a past war, a traded item, or otherwise of foreign origin. Consequently, the in-situ find site may still not reveal its provenance but with further detective work may help uncover links that otherwise would remain unknown. It is also possible for archaeological layers to be reworked on purpose or by accident (by humans, natural forces or animals). For example, in a "tell-tell mound", where layers are not typically uniform or horizontal, or in land cleared or tilled for farming. The term in-situ is often used to describe ancient sculpture that was carved in place such as the Sphinx or Petra. This distinguishes it from statues that were carved and moved like the Colossi of Memnon which was moved in ancient times.

Art:

In art, in-situ refers to a work of art made specifically for a host site, or that a work of art takes into account the site in which it is installed or exhibited. For a more detailed account see: Site-specific art.

· in-situ / live art creation

Astronomy:

A fraction of the globular star clusters in our Galaxy, as well as those in other massive galaxies, might have formed in situ. The rest might have been accreted from now defunct dwarf galaxies.

Biology

Figure-5: Live individual of the sea snail Natica hebraea photographed in-situ²⁹

In biology, in-situ means to examine the phenomenon exactly in place where it occurs (i.e. without moving it to some special medium). In the case of observations or photographs of living animals, it means that the organism was observed (and photographed) in the wild, exactly as it was found and exactly where it was found. The organism had not been moved to another (perhaps more convenient) location such as an aquarium. This phrase in-situ when used in laboratory science such as cell science can mean something intermediate between in-vivo and in-vitro. For example, examining a cell within a whole organ intact and under perfusion may be in-situ investigation. This would not be in-vivo as the donor is sacrificed before experimentation, but it would not be the same as working with the cell alone (a common scenario for in-vitro experiments). In-vitro was among the first attempts to qualitatively and quantitatively analyze natural occurrences in the lab. Eventually, the limitation of in-vitro experimentation was that they were not conducted in natural environments. To compensate for this problem, in-vivo experimentation allowed testing to occur in the originate organism or environment. To bridge the dichotomy of benefits associated with both methodologies, in-situ experimentation allowed the controlled aspects of in-vitro to become coalesced with the natural environmental compositions of in-vivo experimentation. In conservation of genetic resources, "in-situ conservation" (also "on-site conservation") is the process of protecting an endangered plant or animal species in its natural habitat, as opposed to ex-situ conservation (also "off-site conservation").

Chemistry and chemical engineering:

In chemistry, in-situ typically means "in the reaction mixture." There are numerous situations in which chemical intermediates are synthesized in-situ in various processes. This may be done because the species is unstable, and cannot be isolated, or simply out of convenience. Examples of the former include the Corey-Chaykovsky reagent and adrenochrome. In chemical engineering, in-situ often refers to industrial plant "operations or procedures that are performed in place". For example, aged catalysts in industrial reactors may be regenerated in place (in-situ) without being removed from the reactors.

Civil Engineering:

In architecture and building, in-situ refers to construction which is carried out at the building site using raw materials. Compare that with prefabricated construction, in which building components are made in a factory and then transported to the building site for assembly. For example, concrete slabs may be in-situ or prefabricated. In-situ techniques are often more labour-intensive, and take longer, but the materials are cheaper, and the work is versatile and adaptable. Prefabricated techniques are usually much quicker, therefore saving money, but factory-made parts can be expensive. They are also inflexible, and must often be designed on a grid, with all details fully calculated in advance. Finished units may require special handling due to excessive dimensions.

Computer science:

In computer science an in-situ operation is one that occurs without interrupting the normal state of a system. For example, a file backup may be restored over a running system, without needing to take the system down to perform the restore. In the context of a database, a restore would allow the database system to continue to be available to users while a restore happened. An in-situ upgrade would allow an operating system, firmware or application to be upgraded while the system was still running, perhaps without the need to reboot it, depending on the sophistication of the system.

An algorithm is said to be an in-situ algorithm, or in-place algorithm, if the amount of memory required to execute the algorithm is O(1), that is, does not exceed a constant no matter how large the input. For example, heapsort is an in- situ sorting algorithm. In designing user interfaces, the term in-situ means that a particular user action can be performed without going to another window, for example, if a word processor displays an image and allows you to edit the image without launching a separate image editor, this is called in-situ editing.

Earth and atmospheric sciences:

In physical geography and the Earth sciences, in-situ typically describes natural material or processes prior to transport. For example, in-situ is used in relation to the distinction between weathering and erosion, the difference being that erosion requires a transport medium (such as wind, ice, or water), whereas weathering occurs in-situ. Geochemical processes are also often described as occurring to material in-situ. In the atmospheric sciences, in-situ refers to obtained through direct contact with the respective subject, such as a radiosonde measuring a parcel of air or an anemometer measuring wind, as opposed to remote sensing such as weather radar or satellites.

Electrochemistry:

In electrochemistry, the phrase in-situ refers to performing electrochemical experiments under operating conditions of the electrochemical cell, i.e., under potential control. This is opposed to doing ex situ experiments that are performed under the absence of potential control. Potential control preserves the electrochemical environment essential to maintain the double layer structure intact and the electron transfer reactions occurring at that particular potential in the electrode/electrolyte interphasial region.

Environmental engineering:

In-situ can refer to where a clean up or remediation of a polluted site is performed using and simulating the natural processes in the soil, contrary to ex-situ where contaminated soil is excavated and cleaned elsewhere, off site.

Experimental Psychology:

In experiments, In-situ typically refers to those experiments done in a field setting as opposed to a laboratory setting.

Gastronomy:

In Gastronomy, "In-situ" or "In-situs" refers to the art of cooking with the different resources that are available on the site of the event. Here you are not going to the restaurant, but the restaurant comes to your home.

Law:

In legal contexts, in-situ is often used for its literal meaning. For example, in Hong Kong, "in-situ land exchange" involves the government exchanging the original or expired lease of a piece of land with a new grant or re-grant with the same piece of land or a portion of that.

Linguistics:

In linguistics, specifically syntax, an element may be said to be in-situ if it is pronounced in the position where it is interpreted. For example, questions in languages such as Chinese have in-situ wh-elements, with structures comparable to "John bought what?" while English wh-elements are not in-situ (wh-movement): "What did John buy?"

Literature:

In literature in-situ is used to describe a condition. The Rosetta Stone, for example, was originally erected in a courtyard, for public viewing. Most pictures of the famous stone are not in situ pictures of it erected, as it would have been originally. The stone was uncovered as part of building material, within a wall. Its in-situ condition today is that it is erected, vertically, on public display at the British Museum in London, England.

Medicine:

In oncology: for a carcinoma, in-situ means that malignant cells are present as a tumor but has not metastasized, or invaded, beyond the original site where the tumor was discovered. This can happen anywhere in the body, such as the skin, breast tissue, or lung. This type of tumor can often, depending on where it is located, be removed by surgery.

In medicine, in-situ means that cancer cells have not passed through the basal lamina. Basically, it means the tumor has not invaded the lamina propria or the deeper portions of the tissue. Because metastasis generally requires a carcinoma to 'break through' the basement membrane, chances of metastasis are very low.

Petroleum production:

In-situ refers to recovery techniques which apply heat or solvents to heavy oil or bitumen reservoirs beneath the earth. There are several varieties of in-situ technique, but the ones which work best in the oil sands use heat.

in that petrolem means ,a dark oil consisting mainly of hydrocarbons

RF transmission:

In radio frequency (RF) transmission systems, in-situ is often used to describe the location of various components while the system is in its standard transmission mode, rather than operation in a test mode. For example, if an in-situ wattmeter is used in a commercial broadcast transmission system, the wattmeter can accurately measure power while the station is "on the air".

Space-related:

Future space exploration or terraforming may rely on obtaining supplies in-situ, such as previous plans to power the Orion space vehicle with fuel minable on the moon. The Mars Direct mission concept is based primarily on the in-situ fuel production using Sabatier reaction. In the space sciences, in-situ refers to measurements of the particle and field environment that the satellite is embedded in, such as the detection of energetic particles in the solar wind, or magnetic field measurements from a magnetometer.

In-utero is a Latin term literally meaning "in the uterus". It is used in biology to describe the state of an embryo or fetus.

In-vivo (Latin for "within the living") is experimentation using a whole, living organism as opposed to a partial or dead organism, or an in-vitro controlled environment. Animal testing and clinical trials are two forms of in-vivo research. In-vivo testing is often employed over in-vitro because it is better suited for observing the overall effects of an experiment on a living subject. This is often described by the maxim in-vivo veritas.

In-vitro:

Figure-6: Human embryos photographed developing in-vitro³⁰

A procedure performed in-vitro (Latin: within the glass) is performed not in a living organism but in a controlled environment, such as in a test tube or Petri dish. Many experiments in cellular biology are conducted outside of organisms or cells; because the test conditions may not correspond to the conditions inside of the organism, this may lead to results that do not correspond to the situation that arises in a living organism. Consequently, such experimental results are often annotated with in-vitro, in contradistinction with in-vivo.

In-vitro research:

This type of research aims at describing the effects of an experimental variable on a subset of an organism's constituent parts. It tends to focus on organs, tissues, cells, cellular components, proteins, and/or biomolecules.

Table-1

NAME

View

Model

Builder

Min

REM

Imp

Comments

License

Website

ABALONE

Biomolecular simulations, protein folding

Free

http://www.biomolecular-modeling.com/Abalone/index.html

ACEMD

Molecular dynamics with CHARMM, Amber force fields. Running on NVIDIA GPUs. Heavily optimized with CUDA

Basic version free. Commercial version available

http://www.acellera.com/acemd/

ADUN

Charmm, AMBER, user specified (through force field markup language, FFML), QM/MM calculations with Empirical Valence Bond (EVB); Framework based (GNUStep/cocoa); SCAAS for spherical boundary conditions

Free

http://adun.imim.es/

AMBER

Not free

http://ambermd.org

ASCALAPH DESIGNER

Molecular building (DNA, proteins, hydrocarbons, nanotubes).Molecular dynamics. GPU acceleration

Free and Commercial

http://www.biomolecular-modeling.com/Ascalaph/Packages.html

AUTOMATED TOPOLOGY BUILDER

Automated molecular topology building service for small molecules (< 99 atoms). GROMOS, GROMACS, CNS formats with validation
Repository for molecular topologies and pre-equilibrated systems

Free for academic use

Automated Topology Builder

AVOGADRO

Molecule building, editing (Peptides, small molecules, crystals), Conformational analysis, 2D/3D conversion. Extensible interfaces to other tools

Free, open source

http://avogadro.openmolecules.net/wiki/Main_Page

BALLOON

2D/3D conversion and conformational analysis

Free to use, closed source

http://users.abo.fi/mivainio/balloon/

BOSS

OPLS

Commercial

http://zarbi.chem.yale.edu/software.html#boss

CHARMM

Commercial version with multiple graphical front ends is sold by Accelrys (as CHARMm)

Not free

http://www.charmm.org/

CHEMITORIUM

Free 2D/3D graphical organic molecule builder, viewer and visualisation tool

Free

http://weltweitimnetz.de/software/Chemistry.en.page

CHEMSKETCH

Fast 2-D graphical molecule builder and 3-D viewer. Contains simplified CHARMM for fast stable inaccurate optimization of single molecules up to 1000 atoms

http://www.acdlabs.com/products/chem_dsn_lab/chemsketch/

COSMOS

Hybrid QM/MM COSMOS-NMR force field with fast semi-empirical calculation of electrostatic and/or NMR properties. 3-D graphical molecule builder and viewer

Free (without GUI) and commercial

http://www.cosmos-software.de/ce_intro.html

CULGI

Atomistic simulations and mesoscale methods

Not free

http://www.culgi.com/

DESMOND

High Performance MD

Free and commercial

http://deshawresearch.com/resources.html

DISCOVERY STUDIO

Discovery Studio is a comprehensive life science modeling and simulation suite of applications focused on optimizing the drug discovery process. Capabilities include, small molecule simulations, QM/MM, pharmacophore modeling, QSAR, protein-ligand docking, protein homology modeling, sequence analysis, protein-protein docking, antibody modeling, etc

Closed source/Trial available

http://accelrys.com/products/discovery-studio/

FOLDX

Energy calculations and protein design

Free for academic use

http://foldx.crg.es/

GOVASP

GoVASP is a sophisticated graphical user interface for the Vienna Ab-Initio Simulation Package (VASP). GoVASP comprises tools to prepare, perform and monitor VASP calculations and to evaluate and visualize the computed data

Closed source/Not free/Trial available

http://www.govasp.com/

GROMACS

High performance MD

Free

http://www.gromacs.org/

GROMOS

Geared towards biomolecules

Not free

http://www.igc.ethz.ch/GROMOS/index

ICM

Powerful global optimizer in an arbitrary subset of internal variables, NOEs, Protein docking, Ligand docking, Peptide docking, EM, Density placement

Not free

http://www.molsoft.com/

LAMMPS

Has potentials for soft and solid-state materials and coarse-grain systems

Free

http://lammps.sandia.gov/

MACROMODEL

OPLS-AA, GBSA solvent model, conformational sampling, minimization, MD

Not free

http://www.schrodinger.com/

MATERIALS STUDIO

Materials Studio is a software environment that brings the materials simulation technology to desktop computing, solving key problems throughout the RandD process

Closed source/Trial available

http://accelrys.com/products/materials-studio/

MEDEA

MedeA combines leading experimental databases and major computational programs like the Vienna Ab-Initio Simulation Package (VASP) with sophisticated materials property prediction, analysis, and visualization

Closed source/Not free

link

MCCCS TOWHEE

Originally designed for the prediction of fluid phase equilibria

Free

http://towhee.sourceforge.net/

MDYNAMIX

Parallel MD

Free

http://www.fos.su.se/~sasha/mdynamix/

MOE

Molecular Operating Environment

Commercial

http://www.chemcomp.com/

MOIL

Also includes action-based algorithms (Stochastic Difference Equation in Time and Stochastic Difference Equation in Length) and locally enhanced sampling

Free

http://cbsu.tc.cornell.edu/software/moil/moil.html

MOLECOOLS

Simple Javascript molecular visualization tool

http://blahbleh.com/molecools.php

MOLDY

Parallel, only pair-potentials, Cell lists, modified Beeman's algorithm

Free

http://www.ccp5.ac.uk/moldy/moldy.html

ORAC

A Molecular Dynamics Simulation Program to Explore Free Energy Surfaces in Biomolecular Systems at the Atomistic Level

Free, open source

http://www.chim.unifi.it/orac/

NAB

Generation of Models for "Unusual" DNA and RNA

Free

http://casegroup.rutgers.edu/

PACKMOL

Builds complex initial configurations for Molecular Dynamics

http://www.ime.unicamp.br/~martinez/packmol/

PRIME

Homology modeling, loop and side chain optimization, minimization, OPLS-AA, SGB solvent model, parallalized

http://www.schrodinger.com/

PROTEIN LOCAL OPTIMIZATION PROGRAM

Helix, loop, and side chain optimization. Fast energy minimization

Not free

http://www.jacobsonlab.org/plop_manual/plop_overview.htm

P4VASP

Python-based viewer, structure builder and VASP results browser. Shows band-structure, charge densities and simulates STM images

Free, open source

http://wavemol.org/software/p4vasp

http://cms.mpi.univie.ac.at/odubay/p4vasp_site/news.php

PYMOL

Excellent Python-based viewer, many plugins to other software. Some mutagenisis

Free, open source

http://www.pymol.org/

QMOL

Protein viewer, provided by DNASTAR

Free

http://www.dnastar.com/products/qmol/index.html

RASMOL

Fast viewer

Free

http://www.bernstein-plus-sons.com/ software/rasmol/

RASTER3D

High quality raster images

Free

http://skuld.bmsc.washington.edu/raster3d/raster3d.html

REDMD

Reduced MD. Package for coarse-grained simulations

Free on GNU Licence

http://bionano.icm.edu.pl/software/redmd

STR3DI32

Sophisticated 3-D molecule builder and viewer, advanced structural analytical algorithms, full featured molecular modeling and quantitation of stereo-electronic effects, docking and the handling of complexes

The 200 atom version is free

http://www.exorga.com/

SELVITA PROTEIN MODELING PLATFORM

Protein structure prediction, homology modeling, ab initio modeling, loop modeling, protein threading

Commercial

http://www.selvita.com/selvita-protein-modeling-platform.html

SIMBIOSYS' MODEST (MOLECULAR DESIGN SOFTWARE TOOLKIT)

molecular docking, scoring functions for docking, "ligand-based", "fragment-based", "de-novo"

Not Free

http://www.simbiosys.ca/products/index.html

SPARTAN

Small molecule (< 2000 a.m.u.) MM and QM tools for determining conformation, structure, property, spectra, reactivity, and selectivity

Commercial, Trial Available

http://www.wavefun.com/products/spartan.html

SWISSPARAM

Web server to determine automatically parameters and topologies for small organic molecules, for use with the CHARMM all atoms force field. Files can be used with CHARMM and GROMACS

Free for academic. CHARMm licence required for commercial usage.

http://swissparam.ch/

TINKER

Software tools for molecular design

Free

http://dasher.wustl.edu/tinker/

UCSF CHIMERA

Visually appealing viewer, amino acid rotamers and other building, includes Antechamber and MMTK, Ambertools plugins in development

http://www.cgl.ucsf.edu/chimera/index.html

VLIFEMDS

Complete Molecular Modelling Software, QSAR, Combinetorial Library generation, Pharmacophore, Cheminformatics, docking, etc

Not free

http://www.vlifesciences.com/

VMD + NAMD

Fast, parallel MD

Free

http://www.ks.uiuc.edu/Research/vmd/

WHAT IF

Visualizer for MD. Interface to GROMACS

Not free

http://swift.cmbi.ru.nl/whatif/

XEO

open project management for nanostructures

link

YASARA

Molecular-graphics, -modeling and -simulation program

Not free

http://www.yasara.com/

ZODIAC

Drug design suite

http://www.zeden.org/

In vitro research is better suited than in-vivo research for deducing biological mechanisms of action. With fewer variables and perceptually amplified reactions to subtle causes, results are generally more discernible.

The massive adoption of low-cost in-vitro molecular biology techniques has caused a shift away from in-vivo research which is more idiosyncratic and expensive in comparison to its molecular counterpart. Currently, in-vitro research is vital and highly productive.

However, the controlled conditions present in the in-vitro system differ significantly from that in-vivo, and may give misleading results. Therefore, in-vitro studies are usually followed by in-vivo studies. Examples include:

· In biochemistry, non-physiological stoichiometric concentration may result in enzymatic active in a reverse direction, for example several enzymes in the Krebs cycle may appear to have incorrect nomenclature.

· DNA may adopt other configurations, such as A-DNA.

· Protein folding may differ as in a cell there is a high density of other protein and there are systems to aid in the folding, while in vitro, conditions are less clustered and not aided.

It should be pointed out that the term is historical, as currently most lab ware is disposable and made out of polypropylene (sterilizable by autoclaving, ex: microcentrifuge tubes) or clear polystyrene (ex: serological pipettes) rather than glass to ease labwork, ensure sterility, and minimize the possibility of cuts from broken glass.

In-vivo vs. Ex-vivo research:

In molecular biology in-vivo is often used to refer to experimentation done in live isolated cells rather than in a whole organism, for example, cultured cells derived from biopsies. In this situation, the more specific term is ex-vivo. Once cells are disrupted and individual parts are tested or analyzed, this is known as in-vitro. in vivo experiment is in living; in-vitro study is in test tube.

Methods of use:

According to Christopher Lipinski and Andrew Hopkins, "Whether the aim is to discover drugs or to gain knowledge of biological systems, the nature and properties of a chemical tool cannot be considered independently of the system it is to be tested in. Compounds that bind to isolated recombinant proteins are one thing; chemical tools that can perturb cell function another; and pharmacological agents that can be tolerated by a live organism and perturb its systems are yet another. If it were simple to ascertain the properties required to develop a lead discovered in-vitro to one that is active in-vivo, drug discovery would be as reliable as drug manufacturing."

In the past, the guinea pig was such a commonly used in-vivo experimental subject that they became part of idiomatic English: to be a guinea pig. However, they have largely been replaced by their smaller, cheaper, and faster-breeding cousins, rats and mice. In-vivo imaging provides a noninvasive method for imaging biological processes in live animals in order to understand metabolic processes, effects of drugs and disease progression. Near-infrared (NIR) fluorescent detection has proven useful for in-vivo imaging in small animals. Low tissue autofluorescence at 800 nm makes it possible to use probes with NIR labels to image tumors and organs. In-vivo imaging is an important tool for any research that uses animal models to study diseases, such as Alzheimer's disease.²⁹

In-papyro: referring to experiments or studies carried out only on paper. For example, the term may be applied to epidemiological studies that do not involve clinical subjects, such as meta-analysis. The term is similar to phrases such as in-vivo, in-vitro, or in-silico. Like the latter, in-papyro (the correct Latin is in-papȳrō) has no actual Latin meaning and was constructed as an analogue to the more popular and longstanding biological sciences terms (vivo and vitro). In-papyro is mutually exclusive from in-vitro and in-vivo, but overlaps with in-silico - that is, a study carried out through computer/abstract simulations can also be considered in-papyro.³⁰

This is a list of computer programs that are predominantly used for molecular mechanics calculations.^31-34

Min - Optimization, MD - Molecular Dynamics, MC - Monte Carlo, REM - Replica exchange method, QM - Quantum mechanics, Imp - Implicit water, HA - Hardware accelerated.

Y – Yes; I - Has interface.

ACKNOWLEDGEMENT:

One of the authors Ravi N. Patel has presented this work at oral presentation session in PHARMAVISION 2020, Organized by Akhil Bharatiya Vidya Parishad at Dharmaj Degree Pharmacy College, Gujarat, 18-19 September 2010. All the authors are thankful to the project guide Prof. Dr. Dhrubo Jyoti Sen of Shri Sarvajanik Pharmacy College, Mehsana, Gujarat to fulfill this project successfully.

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Received on 22.09.2010 Modified on 06.10.2010

Asian J. Research Chem. 4(2): February 2011; Page 167-180

INTRODUCTION:

Figure-2: In-silico technique3

Method:

Ligand-based:

Structure-based:

Computing Infrastructure:

Ligand-based:

Structure-based:

Overview:

Standards:

Cellular model:

Protein folding:

Human biological systems:

Brain model:

Model of the immune system:

Tree model:

Ecological models:

Modeling of infectious disease:

In-vitro:

In-vitro research:

Figure-2: In-silico technique³