eChapters

Biology is in the middle of a major paradigm shift driven by computing technology. Due to the impact of information technology, biological sciences have been rapidly becoming much more computational and analytical. Rapid progress in research in the field of genetics and related field combined with the tools provided by modern biotechnology has generated massive volumes of genetic and protein sequence data over last two decades. Compilation, storage, analysis for extraction of information and knowledge from this data becomes a challenging task as usual analytical procedures are not directly applicable to these data sets.
 
Bioinformatics has been defined as a means for analysing, comparing, graphically displaying, modeling, storing, systemising, searching, and ultimately distributing biological information, which includes sequences, structures, functions, and phylogeny. Thus, bioinformatics may be defined as a discipline that generates computational tools, databases, and methods to support genomic and post-genomic research. It comprises the study of DNA structure and function, gene and protein expression, protein production, structure and function, genetic regulatory systems, and clinical applications. Bioinformatics applications need knowledge from computer science, mathematics, statistics, medicine, chemistry and biology.

1.     Concentration

Q.1.    What is split plot experiment ?

    Answer
    
    If one has to study two type of factors say A and B in the same experiment where A requires larger plot size and B requires smaller plot size then the structure of the experiment will be that of plots within blocks are known as split plots and blocks within replication are known as main (whole) plots. Such a layout of the experiment is known as split plot experiment.

Q.1.    Define strip plot design ?

    Answer
    
    There is a variant of the split plot design in which both factors A and B will be applied to the larger strips by dividing the experimental field into as many rows as the number of levels of one factor A and into as many columns as the number of levels of second factor B. Then the two factors will be applied at random to the rows and columns. Such a layout is known as strip plot design.

Introduction

Zymomonas mobilis, a gram negative, anaerobic, micro aerotolerant, ethanologenic bacterium metabolizes glucose, fructose, and sucrose. One of the major factor that limits the usage of Z. mobilis is the narrow substrate range. Moreover, there are no obvious genes reported for using lactose, maltose or cellobiose. Our preliminary sequence analysis of the Zymomonas mobilis ZM4 genome indicated the presence of a putative beta-galactosidase (bga) gene. However, the mechanism of beta-galactosidase (Bga) function is still unknown. Hence, Bioinformatics analysis was made to predict the physicochemical, biochemical and biophysical properties, domains, secondary and tertiary structures of the putative beta-galactosidase. These studies suggested that the putative beta-galactosidase gene was poorly expressed from its native promoter in Z. mobilis. The absence of beta-galactoside transacetylase and permease genes in Z. mobilis led to its inability to metabolize lactose. Hence, function predictions of Bga are performed because of the fact that it has a homologous partner.

r DNA Biotechnology
    
Concept of r DNA
    
DNA Preparation from Tissue
    
Reagents
    
Preparation
    
DNA Buffer (Tris-EDTA)
    
RNase A (20 mg/ml)
    
Toe DNA Preparation for Southern Blots
    
Southern Blot
    
PCR
    
Polymerase Chain Reaction
    
The Cycling Reactions
    
Denaturation at 94°C
    
Annealing at 54°C
    
Extension at 72°C
    
DNA Sequencing
    
The Sequencing Reaction
    
Denaturation at 94°C
    
Annealing at 50°C
    
Extension at 60°C
    
Separation of the Molecules
    
Detection on An Automated Sequencer
    
Assembling of The Sequenced Parts of a Gene
    
Freezing of Buffalo Semen (Jindal, 1995)
    
Tissue Culture
    
Preparation of Media
    
Laboratory Procedure for IVF (Kharche et al., 2008)
    
Source of Oocytes
    
Preparation of Estrous Goat Serum
    
Collection of Oocytes
    
Laparoscopic Ovum Pick-up
    
Oocyte Retrieval from Ovaries Collected form Slaughter 
    
House
    
Slicing Technique
    
Follicle Puncture Technique
    
Follicle Aspiration Technique
    
Follicle Isolation Technique
    
Evaluation of Oocytes
    
In vitro Maturation of Oocytes
    
Preparation of Cells for Co-culture
    
Assessment of Nuclear Maturation After in vitro Maturation of Oocytes
    
Analysis of Nuclear Status is Ascribed to One of the Following
    
Sperm Preparation for in vitro Fertilization of Oocytes
    
In vitro Fertilization of Oocytes
    
Assessment of in vitro Fertilization of Oocytes
    
Assessment of Cleavage
    
In vitro embryo culture

7.1 Gel Electrophoresis
    
7.2 Polymerase Chain Reaction (PCR)
    
7.3 Restriction Fragment Length Polymorphisms (RFLPs)
    
7.4 Random Amplification of Polymorphic DNA (RAPD)
    
7.5 Amplified Restriction Fragment Length Polymorphism (AFLP)

Test for Significance for Population Mean
    
Test for Significance of Population Proportion

2.1     Regulation of the Cell Cycle 
    
2.2     Mitosis
    
2.3     Meiosis

1.1     Prokaryotic 
    
1.2     Eukaryotic
    
1.3     Cell orgenellie

Abstract
This paper presents a lucid and application-oriented delineation of the theory of probability which has proved itself as a basic tool in the theoretical and applied researches in almost all disciplines of science, specially, in the problems encompassing the real-life issues. Bio-informatics has recently emerged as a potential discipline of science and, needless to mention that the application of the probability theory has delved deep to elucidate the laws under its ambit. The inference procedures based on biochemical systems require the formulation of stochastic models (owing to the prevalence of low to moderate levels of stochasticity and noise in their dynamics) which, in turn, are developed using the probability laws in abundance. In Systems Biology, the estimation of parameters poses a prime task in biokinetic models developed on experimental data, and such estimation procedures have been possible only by applying appropriate tools derived using probability. Likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty is an off-shoot of the celebrated, Bayes' theorem of probability.

Preliminaries and Test Based on N. P. Lemma
    
More on Testing of Hypothesis

10.1 Plasmids
    
10.2 Retrovirus

Mesenchymal Stem Cells

Recent advances in the field of regenerative medicine have opened up newer therapeutic modalities using cell-based and cell-free therapies, particularly stem cells, as potential candidates. As per the definition, stem cells are the undifferentiated progenitor cells that can regenerate and can also differentiate into various specialized cells (Hoang et al., 2022). They can be classified as embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs) and postnatal adult stem cells (Locke et al., 2009). Among postnatal adult stem cells, mesenchymal stem cells (MSCs) have emerged as a popular choice of candidate for therapeutics due to the relatively easy availability, simple isolation techniques, low risk of teratogenicity, minimal ethical and legal concerns unlike embryonic stem cells, hence, are easy to apply in clinical research and practice (Voga et al., 2020). Among MSCs of different origin, bone marrow and particularly adipose tissue-derived stem cells (ADMSCs) received more attention and are one of the most common and promising stem cell types. These cells can be simply obtained from subcutaneous and omental fat or liposuction aspirates for the treatment of diverse clinical applications (Kim &Heo, 2014). These cells can also be expanded vigorously in vitro and differentiated into specific cell lineages such as adipocytes, osteoblasts, chondrocytes, and neurocytes and are even genetically stable in long-term cultures (Miana& González, 2018). Mesenchymal stem cells (MSCs) are multipotent progenitor cells possessing self-renewal ability (limited in vitro) and differentiation potential into mesenchymal lineages, according to the (ISCT) International Society for Cell and Gene Therapy (Dominici et al., 2006).

Acetylcholinesterase activity in brain is measured by the method described by Ellman’s et al. (1961).

Reagents

1. Phosphate buffer (0.1M) (pH 8.0) Solution A: 5.22 g of K2 HPO4 and 4.68 g of NaH2 PO4 are dissolved in 150 ml of distilled water. Solution B: 6.2 g of NaOH is dissolved in 150 ml of distilled water. Solution B is added to solution A to get desire pH 8.0 and then finally the volume is made up to 300 ml with distilled water.

2. DTNB reagent 39.6 mg of DTNB with 15 mg of NaHCO3 is dissolved in 10 ml of 0.1 M phosphate buffer.

3. Acetylthiocholine (ATC) 21.67 mg of Acetylthiocholine is dissolved in 1 ml of distilled water.

Procedure

The brain tissue is weighed and 10% homogenate is made in 0.1M Phosphate buffer (pH 8.0). Take 0.4 ml aliquot of homogenate in a cuvette containing 2.6 ml phosphate buffer (0.1M) and 100 µl DTNB. The contents of the cuvette are mixed thoroughly by bubbling air and absorbance is measured at 412 nm in a spectrophotometer. When absorbance reached a stable value, it is recorded as the basal reading .To this 20 µl of acetylthiocholine is added and change in absorbance is recorded for a period of 10 min at intervals of 2 min. Change in the absorbance per minute determined.

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.

The success of a TTO relies on a systematic approach encircling factors like assessment, valuation, marketing, negotiation, and post-transfer management (fi gure 8). This chapter covers the activities that are essential for the effective functioning of a TTO. Other activies Technology Assessement /Valuation Financial Managemnt Major Activities of technology transfer office Commercialization IP Management Fostering Innovation
Fig 8: Various activities of TTOs.
3.1 Technology assessment - A pillar of innovation commercialization
The process of transitioning an innovative technology to commercial reality requires a stringent and formal process known as technology assessment. This early stage is crucial as it determines the viability of an invention by analysing practicability of the invention as well as its uniqueness and the likelihood of it being adopted in the target markets. In the absence of this preliminary examination, the chance of technology’s potential being mismatched to the market can arise.
a) Scientific merit and technical feasibility
A key benchmark in assessing technologies involves the identification of
scientific validity and technical performance. An innovation’s viability depends on the fact that it complies with good scientific practice or the innovative application of a new paradigm that has empirically confirmable validity. It is important to determine whether the invention’s underlying mechanism can be reliably proven through established scientific practice. A working prototype allows for empirical testing in different conditions and serves as proof of practical application. In addition to that, scalability has to be tested critically. It often happens that innovations perform well in a controlled laboratory setting but lose efficiency or cost-effectiveness in the case of mass production. The viability of the implementation on a grand scale becomes a core concern in this phase of the evaluation.