International Journal of Computational Biology and Bioinformatics vol 2 issue 1

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! Computer simulation
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! Network biology
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! Synthetic biology
! Systems biology
! Bioinformatics
! Computational genomics and proteomics
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Dr. P Mary Anupama
Department of Biotechnology, ANITS,
Sangivalasa, Visakhapatnam (A.P.), India
Dr. Ir. Sreenivas Reddy Bathula
Department of Biotechnology, Bapuji Institute
of Engineering & Technology, Karnataka, India
Dr. Xiaojun Chen
Institute of Biomedical Manufacturing and Life
Quality Engineering, Shanghai Jiao Tong
University (SJTU), China
Dr. Nayira
College of Medicine,
Imam University, Saudi Arabia
Dr Rahul Raghavan
Department of Biotechnology, University of
Calicut, Kerala, India
Dr. Abid Qureshi
Institute of Microbial Technology,
Bioinformatics Center, Chandigarh, India
Dr. Divya Verma
Department of Botany Kalindi College,
University of Delhi New Delhi, India.
Dr. Aumreetam Dinabandhu
ICAR - NRC on Plant Biotechnology, Pusa
Campus, New Delhi, India
Dr. Sanjay Mahendrakumar Dave
Department of Biotechnology,
Hemchandracharya North Gujarat University,
Gujarat, India
Ashish Runthala
Department of Biological Sciences, Birla
Institute of Technology & Science Rajasthan,
India

From the Editor's Desk
Dear Readers,
We would like to present, with great pleasure, the inaugural volume of a new scholarly
journal, International Journal of Computational Biology and Bioinformatics. This journal
is part of the Applied Sciences, and is devoted to the scope of present Computational
Biology and Bioinformatics issues, from theoretical aspects to application-dependent
studiesand thevalidationofemergingtechnologies.
This new journal was planned and established to represent the growing needs of Computational Biology and
Bioinformatics as an emerging and increasingly vital field, now widely recognized as an integral part of
scientific and technical investigations. Its mission is to become a voice of Computational Biology and
Bioinformatics,addressing researchersandpractitionersinthisarea.
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professional research societies to industry practitioners in a range of topics in Computational Biology and
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papers at excellently, well-timed & successfully. International Journal of Computational Biology and
Bioinformatics focuses on original high-quality research in the realm of Computer simulation,
Mathematical biology, Molecular modeling, Network biology, Structural genomics, Synthetic biology,
Systems biology, Bioinformatics, Computational genomics and proteomics, Biomedical image processing,
Modelling and simulation, Sequence assembly, search, and alignment, Computational biomedical data
engineeringetc.
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thankful to everybody within that community who supported the idea of creating an innovative platform. We
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ComputationalBiologyandBioinformatics.
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editorialstaffofJournalsPub,who supported us ateverystageoftheproject.
It is our hope that this fine collection of articles will be a valuable resource for Computational Biology and
Bioinformatics readers and will stimulate further research into the vibrant area of Computational Biology
and Bioinformatics.
PuneetMehrotra
ManagingDirector

1. Editorial Letter
B.D. Bulchandani 1
2. Molecular Docking of Antipain Inhibitor Using Hex Software
Shridhar Shrimant Bagali, Bychapur Siddaiah Gowrishankar 3
3. Network Biology: Short Article
Neha Purohit 9
4. Molecular Model
Rashmi Malhotra 12
5. Monte Carlo Method
Manish Jain 14
Contents

IJCBI (2016) 1–2 © JournalsPub 2016. All Rights Reserved Page 1
International Journal of Computational Biology & Bioinformatics
Vol. 2: Issue 1
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Editorial Letter
Dr. B.D. Bulchandani
Associate Professor
Department of Biotechnology,
Sobhasaria Group of Institutions, Sikar
Rajasthan Technical University, Kota, India
What Biology has to do with
Computation?Thisis one question that
flashes the mind of every beginner in the
field of Computational Biology. The
answer for the question lies in multifarious
facts and developments that have made a
bridge for biologists to follow
computation. Thanks to modern versions
and miniaturization of computers, which
has made them a house hold term and a
sophisticated automation tool for present
day researchers.Computers have long
been known for their ability to perform
billions of simple calculations per second
and for being able to store billions of bits
of information to be used at random. This
all has been made possible by developing
and using the appropriate and specific
computer programs to face and cater to the
wide range of problems, ever perceived by
modern man in general and scientifically
trained skilled individual in particular, that
would otherwise have remained out of
reach and any solution. Impending
possibilities of computation have spawned
several interdisciplinary activities where
the objective is to utilize the capacities of
computers to gain knowledge from ever
expanding huge amounts of data and
intervene the functional aspects for the
sake of betterment. This very union of
computation and biology, which has been
unexpected in many senses, is attributed
toand propelled by the recently unveiled
fact, that life itself is an amazing array of
information technology.Whereall the
information to make and execute all life
processes lies in the quadruplet of nitrogen
bases (A, G, C and T) present in the DNA
of any organism, that make the genes and
overall genetic constitution. The
physiology of any organism right from
bacteria through plants to complex animals
including human beings is determined by
their constituent genes, these in turn at its
most basic outlay can be viewed as digital
information.
According to a recent report by Anthony
Kervalage of Celera, an average loaded
sequencing laboratory can produce over
100 gigabytes, some even more, of these
genomic data per day with ease and
precision. This incredible pace of
processing has been matched by the drastic
fall in the price of data mining from wide
sources and developments in computer
hard ware as well as sophisticated software
technology, the most important areas
worth mentioning have been disk storage
and speeded internet allowing faster
computation and methods for accessing
and exchange. This fascinating field of
computational biology started with
developing all these required tools to
organize data in an advanced and well-
structured way, analyse and interpret them
on several fronts including homology
search, gene annotation and gene
prediction, has made several leaps with the
development of algorithms that include
neural networks, rule based system,
dynamic programming and hidden Markov
models to facilitate machine based
learning and large scale data
management.In other words, computation

IJCBB (2016) 3–8 © JournalsPub 2016. All Rights Reserved Page 3
International Journal of Computational Biology and Bioinformatics
Vol. 2: Issue 1
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Molecular Docking of Antipain Inhibitor Using Hex Software
Shridhar Shrimant Bagali1*
, Bychapur Siddaiah Gowrishankar2
1
Department of Petrochemical Engineering, KBNCE, Kalaburagi, Karnataka, India
2
Department of Biotechnology, Siddaganga Institute of Technology, Tumkur, Karnataka, India
Abstract
cellular functions through protein-ligand interaction, which leads to significant conformational
Keywords: protein–ligand interactions, Docking, Harmonic Surface and Fourier transform.
INTRODUCTION
Bioinformatics is multidisciplinary science
dealing the connection of molecular
biology and computer science as well as
programming. Recent days, molecular
biology data based software development
is challenging and fascinating in the field
of bioinformatics. Bioinformatics is a new
tool in the growing area of science in the
computational approach to biological
solutions for design of new drug. Design
of drug includes blowing up in molecular
sequence and structural information may
increase in demanding position in study of
fundamental biomedical solution.
Bioinformatics mainly deals gene
discovery, design of molecular modeling,
site-directed mutagenesis and experiments
of many other prospective research had
been correlated with respect to the
structure and function of genes and
proteins.[1]
Proteins are complex molecules made up
of amino acids joined with peptide bonds.
Proteins play a vital role in structural and
functional activities in all living cells.
Many proteins are enzymes or subunits of
enzymes, catalyzing chemical reactions. In
biology, a ligand is an extracellular
substance that binds to receptors called
protein. In chemistry, a ligand is an atom,
ion or functional group that donates its
electrons through a coordinate covalent
bond to one or more central atoms or ions,
usually metals. An array of such ligands
around a center is termed a complex. The
importance of protein ligand binding in
biological systems should not be
underestimated. Any organism must have
a mechanism of interacting with its
environment. Individual cells must be able
to interact with a complex variety of
different molecules, derived from not only
the outside environment but also generated
within the cell itself. Protein-ligand
interaction has an important function in
living organisms and which will be used in
wide verities of molecules. The goal is to
build a complete computer-aided drug
design system. There is tool need to
development, such as Docking. The act of
coupling two or more orbiting objects; the
operation of mechanically connecting
together, or in same manner being
together, orbital payloads are called
docking. Docking is process of fitting
together two molecules in 3d space.
Docking allows the scientists to virtually
screen a database of compounds and
predict the strongest binders based on
various scoring functions.
Macromolecular interactions are essential
for biological life's existence, as each
biological process involves protein protein
interactions; the area of molecular docking
encompasses protein protein interactions
and protein ligand interactions.[2–6]

IJCBB (2016) 9–11© JournalsPub 2016. All Rights Reserved Page 9
Vol. 2: Issue 1
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Network Biology: Short Article
Neha Purohit*
Department of Biotechnology, Amity University, Uttar Pradesh, India
Abstract
Network representation of intracellular biological systems regularly considers molecular
segments inside a cell as hubs and their immediate or circuitous communications as
connections or links. Distinctive sorts of intracellular molecular biological systems can be
spoken to by various sorts of numerical structures called graph. Rapid advances in network
biology indicate that cellular networks are governed by universal laws and offer a new
conceptual framework that could potentially revolutionize our view of biology and disease
pathologies in the twenty-first century.
Keywords: biological systems, network biology
INTRODUCTION
A biological system is any system that
applies to biological frameworks. A
system is any framework with sub-units
that are connected into an entire, for
example, animal types units connected
into an entire sustenance web. Biological
systems give a numerical investigation of
associations found in environmental,
transformative, and physiological
concentrates, for example, neural
networks.[1]
The examination of biological
systems concerning human ailments has
prompted the field of system prescription.
Complex biological networks might be
spoken to and broke down as processable
systems. For instance, biological systems
or ecosystems can be displayed as systems
of collaborating species or a protein can be
demonstrated as a system of amino acids.
Separating a protein more distant, amino
acids can be spoken to as a system of
associated molecules, for example, carbon,
nitrogen, and oxygen.
Nodes and edges are the fundamental
segments of a system. Nodes speak to
units in the system, while edges speak to
the collaborations between the units.
Nodes can speak to a wide-exhibit of
natural units, from individual life forms to
individual neurons in the mind. Two
essential properties of a system are degree
and centrality. Degree (or availability, an
unmistakable use from that utilized as a
part of chart hypothesis) is the quantity of
edges that interface a hub, while
betweenness is a measure of how focal a
hub is in a network.[2]
Nodes with high
betweenness basically serve as extensions
between various segments of the system
(i.e. communications must go through this
hub to achieve different bits of the
system). In informal communities, hubs
with high degree or high betweenness may
assume essential parts in the general
structure of a system.
Bioinformatics has increasingly shifted its
focus from individual genes, proteins, and
search algorithms to large-scale networks
often denoted as -omes such as biome,
interactome, genome and proteome. Such
theoretical studies have revealed that

Vol. 2: Issue 1
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Molecular Model
Rashmi Malhotra*
Department of Computational Biology, Sharda University, Uttar Pradesh, India
Abstract
Molecular modeling is an accumulation of (PC based) methods for inferring, representing
and controlling the structures and responses of particles, and those properties that are
subject to these three dimensional structures. This is a standard tool used for searching for,
rationalizing and predicting structure and reactivity of organic, bio-organic and
organometallic molecules.
Keywords: molecular graphics, molecular modeling
INTRODUCTION
Molecular modelling includes every single
hypothetical strategy and computational
procedures used to model or copy the
conduct of particles. The procedures are
utilized as a part of the fields of
computational science, drug design,
computational science and materials
science for contemplating sub-atomic
frameworks going from little substance
frameworks to expansive organic particles
and material gatherings.[1]
The most
straightforward counts can be performed
by hand, however definitely PCs are
required to perform sub-atomic
demonstrating of any sensibly estimated
framework. The normal element of sub-
molecular displaying strategies is the
molecular level portrayal of the sub-
atomic frameworks. This may incorporate
regarding particles as the littlest individual
unit (the Molecular mechanics approach),
or expressly displaying electrons of every
iota (the quantum science approach).[2]
Molecular mechanics is one aspect of
molecular modelling, as it refers to the use
of classical mechanics/Newtonian
mechanics to describe the physical basis
behind the models. Molecular models
typically describe atoms (nucleus and
electrons collectively) as point charges
with an associated mass. The interactions
between neighbouring atoms are described
by spring-like interactions (representing
chemical bonds) and van der Waals forces.
The Lennard-Jones potential is commonly
used to describe van der Waals forces.[3]
The electrostatic interactions are
computed based on Coulomb's law. Atoms
are assigned coordinates in Cartesian
space or in internal coordinates, and can
also be assigned velocities in dynamical
simulations. The atomic velocities are
related to the temperature of the system, a
macroscopic quantity. The collective
mathematical expression is known as a
potential function and is related to the
system internal energy (U), a
thermodynamic quantity equal to the sum
of potential and kinetic energies. Methods
which minimize the potential energy are
known as energy minimization techniques
(e.g., steepest descent and conjugate
gradient), while methods that model the
behaviour of the system with propagation
of time are known as molecular dynamics.

Vol. 2: Issue 1
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Monte Carlo Method
Manish Jain*
Department of Bioinformatics, SRM University, Chennai, India
Abstract
Monte Carlo simulations are utilized to display the likelihood of various results in a
procedure that cannot without much of a stretch be anticipated because of the mediation of
arbitrary variables. Monte Carlo simulation outfits the leader with a scope of conceivable
results and the probabilities they will happen for any decision of activity. It demonstrates the
compelling conceivable outcomes—the results of pulling out all the stops and for the most
traditionalist choice—alongside every single conceivable outcome for widely appealing
choices.
Keywords: computational algorithm, Monte Carlo, simulation
INTRODUCTION
Monte Carlo methods (or Monte Carlo
trials) are a wide class of computational
calculations that depend on rehashed
arbitrary testing to get numerical results.
Their crucial thought is utilizing
haphazardness to take care of issues that
may be deterministic on a basic level.
They are regularly utilized as a part of
physical and numerical issues and are
most helpful when it is troublesome or
difficult to utilize different methodologies.
Monte Carlo techniques are mostly
utilized as a part of three particular issue
classes:[1]
improvement, numerical
combination, and producing draws from a
probability circulation.
In physical science related issues, Monte
Carlo methods are entirely helpful for
reproducing frameworks with numerous
coupled degrees of flexibility, for
example, liquids, cluttered materials,
emphatically coupled solids, and cell
structures (see cell Potts model,
communicating molecule frameworks,
McKean-Vlasov forms, active models of
gasses). Different cases incorporate
demonstrating marvels with critical
instability in inputs, for example, the
computation of danger in business and, in
math, assessment of multidimensional
positive integrals with confused limit
conditions.[2,3]
In application to space and
oil investigation issues, Monte Carlo–
based expectations of disappointment, cost
invades and plan overwhelms are routinely
superior to anything human instinct or
option "delicate" strategies.
On a basic level, Monte Carlo strategies
can be utilized to take care of any issue
having a probabilistic elucidation. By the
law of vast numbers, integrals portrayed
by the normal estimation of some irregular
variable can be approximated by taking
the experimental mean (a.k.a. the
specimen mean) of autonomous examples
of the variable.[4]
At the point when the
likelihood circulation of the variable is
parametrized, mathematicians frequently
utilize a Markov Chain Monte Carlo
(MCMC) sampler. The focal thought is to
outline a reasonable Markov chain model

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