eChapters

Principle

The quantification of infectious virus particles is commonly achieved through the utilisation of the Median Tissue Culture Infectious Dose (TCID50) assay. The assay functions through the introduction of a serial dilution of the virus sample into cells in a 96 well plate format. The chosen cell type is specifically intended to exhibit a cytopathic effect (CPE), which refers to observable changes in morphology or cell death resulting from viral infection. Following a period of incubation, the cells are examined for cytopathic effects or cell death, and each well is categorised as either infected or uninfected. The concentration at which 50% of the wells exhibit a cytopathic effect (CPE) is employed to determine the 50% tissue culture infectious dose (TCID50 ) of the viral sample. The end point is the dose or dilution that infects or kills or shows CPE of the cells. The calculation can be performed using various mathematical techniques, such as the Spearman-Karber method or the Reed Muench method. The virus titer is quantified as the number of tissue culture infectious dose 50 (TCID50 ) per millilitre (ml). The non-cytopathic virus can be quantified by employing the fluorescent antibody test, immune-peroxidase assay, or other appropriate methodologies.

Introduction

Biochemistry is branch of life science which deals with the study of chemical reactions occurring in the living cells and organisms. The term biochemistry was first introduced by German chemist Carl Neuberg in 1903. Since biochemistry is associated with the biological reactions and energy changes associated with these reactions or transformations within the living cell, biochemistry may thus be treated as a discipline in which biological phenomenon are analysed in terms of chemistry. The branch of biochemistry for the same reason has been variously named as “Biological Chemistry” or “Chemical Biology”. Modern biochemistry has two branches descriptive and dynamic biochemistry. Descriptive biochemistry deals with qualitative and quantitative characterization of the various chemical components of cell and the dynamic biochemistry deals with the elucidation of the nature and the mechanism of reactions involving these chemical cell components.

Qualitative analysis of biological sample deals with the examination of the organic or inorganic cell associated components in order to determine its composition. It denotes the presence of various biomolecules, elements or groups of elements that are present in the sampling population. It is possible to determine whether a particular component is present or absent in a sample by performing a qualitative analysis of the sample. Qualitative analysis is the identification of elements, species and/or compounds present in a sample.

Quantitative genetics is the study of continuous or quantitative traits and their underlying mechanisms. The main principals of quantitative genetics developed in the 20th century was largely in response to the rediscovery of Mendelian genetics. Mendelian genetics in 1900 centered attention on the inheritance of discrete characters, e.g., smooth vs. wrinkled peas, purple vs. white flowers. This focus was in stark contrast to the branch of genetic analysis by Sir Francis Galton in the 1870’s and 1880’s who focused on characteristics that were continuously variable and thus, not clearly separable into discrete classes. A contentious debate ensued between Mendelians and Biometricians regarding whether discrete characteristics have the same hereditary and evolutionary properties as continuously varying characteristics. I The Mendelians (led by William Bateson) believed that variation in discrete characters drove evolution through mutations with large effects I The Biometricians (led by Karl Pearson and W.F.R. Weldon) viewed evolution to be the result of natural selection acting on continuously distributed characteristics. This eventually led to a fusion of genetics and Charles Darwin’s theory of evolution by natural selection: main principals of quantitative genetics, developed independently by Ronald Fisher (1918) and Sewall Wright (1921), arguable the two most prominent evolutionary biologists.
Interestingly, Galton’s methodological approaches to continuously distributed traits marked the founding of the Biometrical school, which is what many consider to be the birth of modern statistics. Karl Pearson was inspired greatly by Galton, and he went on to develop a number of methods for the analysis of quantitative traits. We will talk more about Galton and Pearson later on in this course. One of the central goals of quantitative genetics is the quantification of the correspondence between phenotypic and genotypic values. It is well accepted that variation in quantitative traits can be attributable to many, possibly interacting, genes whose expression may be sensitive to the environment

Quantitative Traits

• Quantitative traits exhibit a continuum of phenotypic variation that may follow a normal distribution
• Statistical methods are used to evaluate a frequency distribution quantitatively
• Some statistical methods compare two variables with each other

Polygenic Inheritance

Polygenic inheritance and environmental factors create overlaps between genotypes and phenotypes

Polygenic inheritance explains DDT-resistance in Drosophila

Quantitative trait loci (QTLs) are now mapped by linkage to molecular markers

Q1. Fill in the blanks with appropriate answers.

1. India ranks …first…. position in cattle population in the world.

2. India ranks … first …. position in buffalo population in the world.

3. India ranks … first ……. position in total milk production in the world.

4. …Buffalo…….. species produce the highest amount of milk in India.

5. Frieswal cattle is a cross of …Holstein Friesian….and ……Sahiwal…

6. Mehsana buffalo is a cross of …Murrah….and ……Surti……

7. India ranks … first …. position in goat population in the world.

Reagents

Restriction enzymes of choice, such as BamH1 and EcoRI

Restriction enzyme reaction buffer,

70 % Ethanol

100 % Ethanol

3 M Sodium acetate (pH 5.2)

Distilled water

Plasmid DNA isolated from bacteria, such as pGFP(R) and pBC(R)

R packages include functions, codes and sample data. The functions used in R are contained in packages. A package in R contains several functions which are related to some particular type of job. A package conatins at least one function and there is no maximum limit to the no. of functions in a package. The set of packages in R which are set by default in the software is called base package. It contains a lot of commonly used functions. The packages in R are stored under a directory called “library” in the R environment. The packages get installed by default while installing R in the computer. Also more packages can be added later on after installation, when required for some specific purposes. The base packages are available by default when we start using R. But the other packages which are already installed while installing R have to be loaded while using R. These packages can be viewed in the package window by clicking on package. To install the package we have to click on install menu in the package window and then write the name of the package.

The packages can also be installed by using the function install.packages(“abc”) where abc is the name of the package to be installed.

Once a package is installed, it remains in the hard disk of the PC/laptop. But the package will have to be called for use in the current R session. The package once installed can be loaded for use in the current R session by checking the box near the name of the package displayed after clicking on the install menu in the Files/ Plots/Packages/Help window. The package can also be loaded by using library() function. The command will be library(“abc”) where abc is the name of the package. The loaded package remains in the memory for the current R session. Once we quit the R session, the installed package has to loaded again if needed.

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

In a historic landmark, Genentech, a young Californian Company pioneering in gene splicing (which includes recDNA technology, genetic engineering, gene transplantation, gene cloning), saw its stock price skyrocket form $35 to a staggering $89 within just 20 minutes of the New York Stock Exchange’s opening bell on October 15, 1980. This was marked as the fastest stock price growth ever witnessed on that exchange. The company’s success was attributed to the ground-breaking work it had been involved in producing human insulin through genetic engineering that offered a revolutionary alternative for diabetics allergic to the then traditional insulin of porcine origin. Genentech’s pioneering work in gene splicing has its roots in the early 1970s. In 1972, Stanley Cohen and Herbert Boyer were credited with creating the first recombinant DNA molecule. Their experiments demonstrated the ability to create recombinant DNA molecules that could be introduced into bacteria like E? coli, offering a foundation for future advancements in genetic engineering.

Recombinant DNA Technology

A transgenic organism contains foreign DNA.

Biotechnology is the industry dedicated to using altered cells or molecules for various applications.

Recombinant DNA technology (i.e. gene cloning) was the first of the modern biotechnologies.

Gene targeting uses precision techniques to “knockout” a gene or substitute a gene for one on a chromosome.

Bioethics questions, gene patenting, and potential uses and risks of biotechnology pose treat questions for society.

Sister Chromatid Exchange and Homologous Recombination

The staining of harlequin chromosomes can reveal recombination between sister chromatids

The holliday model describes a molecular mechanism for the recombination process

More recent models have refined the molecular steps of homologous recombination

Various proteins are necessary to facilitate homologous recombination

Gene conversion may result from DNA gap repair synthesis or DNA mismatch repair

Isolated tissue preparations are widely used in physiological or pharmaco logical research to delineate the mechanism of action of drugs or any active biomolecules. Traditionally it is used as a tool for bioassay of drugs. The tissue used for such pharmacodynamics studies may be isolated from different animal models viz. frog, rodents, rabbits, sheep, goat, buffalo, chicken etc. Evaluation of drug-receptor interaction and underlying mechanism of action is then performed using isolated organ bath or myograph where the isolated tissues are mounted under optimum resting tension and changes in the tension following exposure of drug is recorded with the help of recording device. With the advancement of science and technology, the recording devices are also gradually upgraded from smoked drum to student’s physiograph to the most advanced Data Acquisition System (DAS) based polyphysiograph.

The isolated tissue preparations (uterine strips, arterial rings, tracheal chains etc.) need essential and appropriate nutrients and environment during experimental which is generally provided by using suitable salt solutions (Physiological Salt Solutions) which provide ionic balance, energy supply, maintain pH and tissue tonicity. The composition of physiological salt solutions is such that it provides an artificial media simulating with the inorganic composition of blood plasma together with a buffer mechanism to maintain an optimum pH of 7.0 to 7.4 and glucose to facilitate tissue metabolism. Commonly used PSS are Frog ringers, tyrode, Kreb’s, de Jalon, Ringer-Locke, modified Kreb’s Henseleit solution etc.

7.1 Learning Objectives
After comprehending this unit, we will be capable to:
i) Understand the conception of regression;
ii) Appreciate the significance of regression;
iii) Understand the difference between regression and correlation;
iv) Know about the regression lines;
v) Anticipate the difference between simple regression and multiple regression.

7.2 Introduction

Correlation is followed by linear regression. Regression is applied for forecasting the value of an unknown variable with the help of a known variable. The forecasted variable is called the dependent variable. On the contrary, an independent variable is used for forecasting the value of a dependent variable. If you desire to know, for instance, if exam achievement can be anticipated based on review time, whether cigarette consumption can be predicted based on smoking length, and so on, you may use linear regression. You must utilize multiple regressions if we deal with more than one independent variable rather than simply one.

I. CHALLENGES TO CYBERSPACE

The problems and prospects of copyright and information technology need to be deliberated in the context of present day jurisprudence. The subject is referable to the extensive use of information technology all over the world which has resulted in enhanced efficiency, effectiveness and optimum use of resources. The copyright law is concerned with the negative right of preventing copying of the physical material in the field of literature and arts. Trade and Merchandise Marks Act protects the patent, trade mark and design giving the registered proprietor an exclusive right to the registered material. The prohibition contained in rights in patent and copyright extend to the unauthorised manufacture or reproduction, so long as the exclusive right rests. Copyright has been held to be not the mere creature of the statute but a natural and civil right. The moral basis of the copyright is that the man is entitled to the fruits of its mental faculties as much as he is entitled to the fruits of his mechanical labour. The law rests on the English Commandment - thou shall not steal. It does not permit one man to make a profit and appropriate himself what has been produced by the labour, skill and capital of another. Blackstone in his commentaries Vol.II at page p.406 has stated:

There is still another aspect of property and this is the right which an author may be supposed to have in his own original literary compositions: so that no other person without his leave may publish or make profit of the copies. When a man by the exertion of his rational powers has produced an original work, he seems to have clearly a right to dispose of that identical work as he pleases and any attempt to vary the disposition he has made to it appears to be an invasion of that right.

When Allele Frequencies Stay Constant

The Importance of Knowing Allele Frequencies

Population genetics concerns the study of allele frequencies in a population.

Genes in a population comprise the gene pool.

Gene flow is movement of alleles between populations.

Microevolution reflects changes in gene frequencies in populations, which can be traced using the Hardy-Weinberg equation.

Allele frequencies are affected by mutation, migration, genetic drift, selection, and non-random mating.

I. INTRODUCTION

Procedural remedies for the infringement of one’s right are not enough by themselves unless they result in some reliefs of substantive nature whereby it can reasonably be said that these reliefs, temporary or permanent, as the case may be, are enough in the circumstance to make good or, say, compensate the genuine loss of the aggrieved party to an infringement suit. The substantive remedies available to a successful plaintiff at any stage in any suit for patent infringement could, however, be derived from the express as well as by necessary implication of the provisions of the Patents Act, 1970 dealing with the “Reliefs in suit for infringement”. Its Sec. 108 declares in terms quoted below:

There are two assumptions necessary involved in the regression of repeatability. These are:
• The variances of the different measurements are equal and have their components in the same proportions, and
• The different measurements reflect genetically the same character. If these assumptions are not valid the repeatability concept becomes vague without precise meaning in relation to the variance components.
The repeatability differs very much depending on the following:
• The nature of the trait.
• The genetic properties of the population.
• Environmental conditions under which the individuals are kept or raised.
Repeatability indicates the proportion of observed differences in performance between animals caused by differences in real producing ability. That is say, repeatability of butterfat yield is 0.40, and if a cow has an age corrected butter fat yield of 30 kilograms above the herd average, her near producing ability will be equal to 30x 0.40 = 12 kg. above the herd average. This can also be explained in a different way, i.e., if two cows differ in one lactation by 30 kg in butter fat yield. They will have difference of 12 kg of butter fat in the next lactation.
Repeatability has a value 0 to 1 and may also be expressed in percentage. Repeatability of a trait is not a constant, and measurements error or generally, varying environmental conditions tend to increase VEt and thus to decrease repeatability, shrode et al (1960) have shown the effect of a change in environmental conditions on repeatability. They obtained repeatabilities of milk yield, butter fat yield and fat percentage as 0.37, 0.32 and 0.7 respectively and after improving the management removed some of the environmental variations that had marked the cows real producing abilities.

In many experimental situations, it becomes necessary to repeat an experiment over time, (for a number of seasons or years). This repetition (or replication) of the experiment broadens the scope of the experiment in the sense that our recommendations will be applicable for a number of seasons. In the case of agricultural experiments, for example, there may be present treatment x season interaction. So the results of a series of experiment, performed for different seasons with the same set of treatments, will have wider applicability.

We shall consider the simplest case of a repetition of experiments of identical structure for a number of seasons. Let us consider randomized block experiments with t treatments in r blocks and conducted in p seasons. The analysis of the experiment in a season is based on the linear model. Before attempting a combined analysis for the p experiments, it is necessary to perform the analysis for p experiments separately and interpret the results separately. It may be of interest to find out whether differences among the treatments are the same in the different seasons so that ‘ best ‘ treatment may be recommended for all seasons or whether different treatments are to be recommended for different seasons.

We next consider the analysis of the combined experiments considering the seasons as a random sample from the population of seasons. Analysis of Variance of series of experiments