eChapters

Abstract
Combined advancement in computer sciences and hardware with uniformly significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. Apart from that, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population.  Although inappropriate pharmacokinetic properties are a major cause of abrasion in the safety issues are recognized as today’s single largest cause of drug candidate failure. It is expected that the right balance of in vivo, in vitro and computational toxicology predictions applied as early as possible in the discovery process will help to reduce the number of safety issues.

ALL MATTER IS IN MOTION

Everything moves:

In the atom electrons rotate around the nucleus

In gases, liquids and even solids molecules move away from other molecules

An accumulation of matter (i.e., a baseball) can move as a unit

The average velocity of movement is the distance travelled divided by the time

Bodies in motion have energy; the faster the velocity the more energy

ALL LIVING ORGANISMS ARE MADE UP OF UNITS CALLED CELLS

All living creatures are made from 1 or more cells

All cells are produced from previously existing cells (no spontaneous generation)

All cells appear to be descended from the first cell which existed about 4 billion years ago

For a species to exist its reproductive cells must be potentially immortal (no aging)

Our bodies start from a single cell and contain about 100,000,000,000,000 (10^14) cells at maturity

Metabolism - sum total of all chemical reactions in living cells

Catabolic reactions - degrade macromolecules and other molecules to release energy

Anabolic reactions - used to synthesize macromolecules for cell growth, repair, and reproduction

Can divide metabolism into 4 groups: carbohydrates, lipids, amino acids, nucleotides.

Abstract
This chapter describes how modern quantum mechanical methods can be applied in a systematic manner to understand pharmacophore and mechanistic aspects of drugs. This chapter also explains why accurate computation of drug-receptor interactions is important for in silico drug designing and testing.

WHY in silico DESIGNING?
Drug designing is the inventive art of finding a new lead compound based on knowledge of its biological target and then its slow conversion to a compound of therapeutic benefit to patient termed as “drug”. This whole process comprises of long sequels of synthesizing compounds followed by their testing and activity evaluation procedures; often a seemingly endless task until desired potency level is achieved.

Introduction and Importance of active site

Proteins are polymers of amino acids coupled by peptide bonds. The constituency of a protein that interacts with a ligand is habitually referred to as the “active site.” Ligands can be proteins, DNA or smaller molecules, such as pharmaceutical compounds [1]. The active site generally lies on the surface of the protein. In some cases, the active site is buried within the protein. Residues with reactive groups (ASP, GLU, SER, CYS, HIS, LYS, ARG) tend to be profuse in protein active sites [2]. The SER-HIS-ASP (sometimes SER-HIS-GLU) named as “catalytic triad” which is a motif, generally found in enzyme active site. Enzymes chip in a significant role in scheming the flow of metabolites within a cell, they catalyze virtually all of the reactions that make and modify the molecules mandatory for biological pathways [3]. The identification of catalytic residues is a key step in recognizing the function of enzymes. The site on the surface of an enzyme molecule that binds and acts on the substrate molecule are called as active site.

Since the dawn of the Industrial Revolution, mankind has been introducing numerous hazardous compounds into the environment at an exponential rate.  Environmental pollutants have become a major global concern. The modern growth of industrialization, urbanization, modern agricultural development, energy generation, have resulted in indiscriminate exploitation of natural resources for fulfilling the human desires and need, which have contributed in disturbing the ecological balance on which the quality of our environment depends.
 
Technological revolution has brought new changes in products and processes in industry. The waste generated from the development of products and processes are of concern to the environmental list. A variety of pollutants such as heavy metals, xenobiotics, polycyclic aromatic hydrocarbons (PAHs), chlorinated and nitro-aromatic compounds are found to be highly toxic, mutagenic and carcinogenic for living organisms (Zhang and Bennett. 2005; Samanta et al., 2002).

Introduction
Machine learning is a branch of artificial intelligence which is concerned with the development of algorithms and techniques that allow computers to learn. These techniques are tolerant of imprecision, uncertainty, partial truth, and approximation. There have emerged many methods based on machine learning techniques, such as Artificial Neural Networks (NNs), Support Vector Machines (SVMs), Decision Tree (DT), Bayesian Networks (BNs), Hidden Markov Models (HMMs), etc. These techniques attempt to study, model, and analyze very complex phenomena  for which more conventional methods have not yielded low cost, analytic, and complete solutions.

Bioinformatics is the application of information technology to the area of molecular biology. The increase in the number and complexity of biological databases has raised the need for powerful data analysis tools and techniques. To fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories (Inza et al., 2010). Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in approximation.

NEW CELLS ARE PRODUCED BY DIVISION OF PREVIOUSLY EXISTING CELLS

All cells come from previously existing cells

To make a new cell identical to itself a cell must perform 4 tasks:

It must grow and make additional copies of all its organelles, enzymes, etc..

It must duplicate its DNA

It must accurately separate the DNA into 2 units, so that each cell gets a full set

It must divide into 2 cells (cytokinesis)

A fair number of practical breeding experiments were done in ancient times

People knew there was a connection between sex and reproduction

The idea had developed that “like begets like”

By selecting certain plants and animals and breeding them many domestic varieties of plants and animals had been developed

Example: corn was developed from a wild grass in Mexico and Central America, probably about 5000 years ago

149 of the top 150 plant crops were domesticated by primitive man

MENDEL’S RULES OF GENETICS DO NOT ALWAYS WORK

Mendel’s rules:

Segregation of alleles

Independent assortment of genes for different characters

Hold for characters:

Which have dominant & recessive traits

Determined by single genes

Genes are on separate chromosomes

Different types of chromosomes, or

Different homologues of same type of chromosome

These conditions are not always met, so there are many complications

A gene microarray is a (mxn) matrix, containing n-experimental conditions, also called attributes for m-genes. Gene clustering refers to partitioning a microarray of genes based on the commonality of attributes. Genes belonging to a cluster often do not coexpress or coregulate. This calls for biclustering that segregates the (mxn) matrix into small windows. In other words biclustering refers to simultaneous clustering of rows and columns of the microarray. This chapter reviews some of the well known biclustering algorithms, and proposes an alternative solution to the problem using evolutionary multi-objective optimization technique.

Introduction

Clustering is process to automatically partition a set of p-dimensional data points (p³1) into classes, where the data in each class have similarity with respect to a given distance metric. Each class usually has an ideal member that possesses the necessary characteristics of the class.

Abstract
Proteins are comprised of domains, folds and motifs, which form its basic building blocks. Most efforts to understand protein evolution and its function have focused on domains as these can fold into a stable three-dimensional structure independent of the rest of the protein chain and generally perform a unique function conserved over the evolution. Genetic recombinant techniques allow reorganization of domains, called domain shuffling, resulting in different combinations of domains in different proteins. This along with swapping and insertion of domains results in complex architectures and are responsible for new protein functions in evolution. Protein domains are very useful in analyzing the mechanisms of protein folding and their stability and structural transformations in various conditions. This can be done reliably if a multi-domain target is considered only in terms of its constituent domains. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently.

Introduction

Natural evolution gifted the biological world a treasure of diverse bio-molecules that take part in various cellular activities. Proteins, one of the vital molecules of biological function are wisely folded polymers comprising 20 different aminoacids. Function of each protein is encoded in its aminoacid sequence. Aminoacid residues can indulge in bonded or non-bonded interactions depending on the environment. These inter residue interactions with in a polypeptide drive its folding in to a compact conformation from a linear molecule. Biochemical properties as well as the linear arrangement of the aminoacids determines the three dimensional structure of a protein. These determinants of defined structure directly or indirectly dictates functional fate of the folded protein also.

Modern research on life science is progressing at the nanoscale not only with the objective to reduce time and cost but also to ease the handling and working conditions. Application of nanotechnology in various domains of science has led to the enormous innovations. This field facilitates better understanding as well as treatment of living system, activate biotechnological processes, synthesizing new drugs for targeted delivery and biocompatible materials for sustainable environment. Throughout the world nano science is getting wide propaganda. Its application to medicine is increasing rapidly in order to develop nano-drugs with prolonged availability and efficient delivery through rearrangement of molecules at the nanoscale using molecular interactions for example van der Waals forces, H bonds, electrostatic dipoles and various surface forces.

UNTIL THE 1940S BIOLOGISTS ARGUED ABOUT WHETHER DNA OR PROTEIN WAS THE MOLECULE OF HEREDITY

Nuclei observed to divide when cells reproduced themselves

Large amounts of both DNA & protein in the nucleus

Many though protein was most likely molecule of heredity

Proteins made of 20 different amino acids- could have more variety than DNA (made of only 4 different bases)

Viruses are much simpler than cells

Protein capsid

Genetic information stored on DNA or RNA:

Types of genome:

Double strand DNA: sometimes circular

Single strand DNA: sometimes circular

Double strand RNA

Single strand RNA

Some can be crystallized

BACTERIA HAVE SMALL, COMPACT GENOMES

We now know the complete DNA sequences for about a dozen bacteria

Bacteria seem to need about 500 to 5000 genes to conduct their lives

Because they are small, bacterial genomes are highly organized

Most of the time there are only a few bases between the end of one gene and the beginning of the next

Few introns are found in bacteria

Bacteria contain accessory “chromosomes” called plasmids; each has only a few genes

Restriction enzymes cut only at specific sequences of 4 to 8 bases

A long restriction site is rare. Cutting rare sites will produce a few large pieces of DNA.

Restriction enzymes with long sites → small number of large fragments

Restriction enzymes with short sites → large number of small fragments

Introduction

India is an agriculture based country with 60% of the manpower employed in agriculture (World Bank, 2010). Few of the main environmental issues are soil erosion, water pollution from raw sewage and runoff of agricultural pesticides. There is a need to maintain a sustainable environment and a sustainable agricultural system.  Sustainable Agricultural system is a self sustaining system, is dependent up the ability to bring together and manage simultaneously the soil, water, plant and animal resources with the limitation of climatic and environment in such a way that the agricultural production/profitability can be maintained in the future too. So initiatives need to be taken to achieve a sustainable agricultural system. Tools have been developed to aid agriculturist, extension personnel and policy makers. The World Wide Web has made the tools easy accessible.