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Bioinformatics and Comparative Genomics
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AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein Coding and Promoter Region Prediction
Pokkuluri Kiran Sree1, Inampudi Ramesh Babu2 and SSSN Usha Devi N3
Research Scholar, Department of CSE, JNTU Hyderabad, India
Professor, Department of CSE, ANU, Guntur, India
3
Assistant Professor, Department of CSE, University College of Engineering, JNTUK, India
1
2

*Corresponding author: Pokkuluri Kiran Sree, Department of CSE, JNTU Hyderabad, India; Email: profkiransree@gmail.com
Received Date: January 20, 2013 Accepted Date: February 10, 2014 Published Date: February 28, 2014
Citation: Pokkuluri Kiran Sree, et al. (2014) AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for
Protein Coding and Promoter Region Prediction. J Bioinfo Comp Genom 1: 1-7.

Abstract
Most of the problems in bioinformatics are now the challenges in computing. This paper aims at building a classifier based
on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic. It is strengthened with an artificial Immune System Technique (AIS), Clonal algorithm for identifying a protein coding and promoter region in a given DNA sequence. The
proposed classifier is named as AIS-INMACA introduces a novel concept to combine CA with artificial immune system to
produce a better classifier which can address major problems in bioinformatics. This will be the first integrated algorithm
which can predict both promoter and protein coding regions. To obtain good fitness rules the basic concept of Clonal selection algorithm was used. The proposed classifier can handle DNA sequences of lengths 54,108,162,252,354. This classifier
gives the exact boundaries of both protein and promoter regions with an average accuracy of 89.6%. This classifier was tested
with 97,000 data components which were taken from Fickett & Toung , MPromDb, and other sequences from a renowned
medical university. This proposed classifier can handle huge data sets and can find protein and promoter regions even in
mixed and overlapped DNA sequences. This work also aims at identifying the logicality between the major problems in
bioinformatics and tries to obtaining a common frame work for addressing major problems in bioinformatics like protein
structure prediction, RNA structure prediction, predicting the splicing pattern of any primary transcript and analysis of
information content in DNA, RNA, protein sequences and structure. This work will attract more researchers towards application of CA as a potential pattern classifier to many important problems in bioinformatics.

Keywords: MACA (Multiple Attractor Cellular Automata); CA(Cellular Automata); AIS( Artificial Immune System);
Clonal Algorithm(CLA);AIS-INMACA (Artificial Immune System-Integrated Multiple Attractor Cellular Automata).

Introduction
Cellular automata
Pattern classification encompasses development of a model
which will be trained to solve a given problem with the help of
some examples; each of them will be characterized by a number of features. The development of such a system is characterized as pattern classification. We use a class of Cellular
Automata (CA)[1,2] to develop the proposed classifier. Cellular automata consist of a grid of cells with a finite number of
states. Cellular Automata (CA) is a computing model which
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provides a good platform for performing complex computations with the available local information.
CA is defined a four tuple <G, Z, N, F>
Where
G -> Grid (Set of cells)
Z -> Set of possible cell states
N -> Set which describe cells neighborhoods
F -> Transition Function (Rules of automata)
As Cellular Automata consists of a number of cells structured
in the form of a grid. The transitions between the cells may
depend on its own state and the states of its neighboring cells.
The equation one sates that if ith cell have to make a transition, it has to depend on own state, left neighbor and right
neighbor also.
qi(t + 1) = f (qi−1(t), qi(t), qi+1(t)) ---- Equation
1
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Problems in Bioinformatics
Bioinformatics can be characterized as a collection of statistical, mathematical and computational methods for dissecting
biological sequences like DNA, RNA and amino acid. It deals
with the design and development of computer based technology that supports biological processing. Bioinformatics tools
are aimed at performing lot of functions like data integration,
collection, analysis, mining, management, simulation, visualization and statistics.
The central dogma shown in the Figure 1 of molecular biology
was initially articulated by Francis Crick [3] in 1958. It deals
with point by point transfer of important sequential data. It
states that data can't be exchanged back from protein to either
protein or nucleic acid. So once data gets into protein, it can't
stream again to nucleic acid. This dogma is a framework for
comprehension about the exchange of sequence data between
sequential data carrying biopolymers in living organisms.
There are 3 significant classes of such biopolymers ie DNA,
RNA and protein.
There are 9 possible immediate exchanges of data that can happen between these. This dogma classifies these nine exchanges
into three transfers (Normal, Special, and Never Happens).
The normal flow of biological information is DNA is copied
to DNA (DNA replication), DNA information is copied into
RNA (Transcription) and protein are synthesized by using the
protein coding region exits in DNA/RNA (Translation).

Protein Coding Region Identification
DNA is an important component of a cell and genes will be
found in specific portion of DNA which will contain the information as explicit sequences of bases (A, G, C, T).These explicit sequences of nucleotides will have instructions to build
the proteins. But the region which will have the instructions
which is called as protein coding regions occupies very less
space in a DNA sequence. The identification of protein coding
regions plays a vital role in understanding the gens. We can
extract lot of information like what is the disease causing gene,
whether it is inherited from father or mother, how one cell is
going to control another cell.

Promoter Region Identification
DNA is a very important component in a cell, which is located
in the nucleus. DNA contains lot of information. For DNA sequence to transcript and form RNA which copies the required
information, we need a promoter. So promoter plays a vital
role in DNA transcription. It is defined as “the sequence in the
region of the upstream of the transcriptional start site (TSS)”. If
we identify the promoter region we can extract information regarding gene expression patterns, cell specificity and
development. Some of the genetic diseases which are associated with variations in promoters are asthma, beta thalassemia
and rubinstein-taybi syndrome.

Literature Survey
Eric E. Snyder et al. [4,5] has developed a PC program, Geneparser, which distinguishes and verifies the protein coding
genes in genomic DNA arrangements.
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This program scores all subintervals in a grouping for substance facts characteristic of introns and exons, and for destinations that recognize their limits. Jonathan H. Badger[6]
et al. has proposed a protein coding region identification tool
named CRITICA which uses comparative analysis. In the
comparative segment of the investigation, regions of DNA are
straightened with identified successions from the DNA databases; if the interpretation of the arranged groupings has more
stupendous amino acid than needed for the watched rate nucleotide character; this is translated as proof for coding.
David J. States[7] proposed a PC project called BLASTX was
formerly indicated to be viable in distinguishing and allotting
putative capacity to likely protein coding districts by identifying huge likeness between a theoretically interpreted nucleotide query arrangement and parts of a protein grouping
database.
Steven Salzberg, et al. [8] has used a decision tree algorithm
for locating protein coding region. Genes in eukaryotic DNA
spread hundreds or many base sets, while the locales of the
aforementioned genes that code for proteins may possess just
a little rate of the succession. Eric E.Snyder, et al. [4] has used
dynamic programming with neural networks to address the
protein coding region problem. Dynamic Programming (DP)
is connected to the issue of exactly distinguishing inside exons
and introns in genomic DNA arrangements. Suprakash Datta,
et al. [9] used a DFT based gene prediction for addressing this
problem. Authors provided theoretical concept of three periodicity property observed in protein coding regions in genomic
DNA. They proposed new criteria for classification based on
traditional frequency approaches of coding regions. Jesus P.
Mena-Chalco, et al. [10] has used Modified Gabor-Wavelet
Transform for addressing this issue. In this connection, numerous coding DNA model-free systems dependent upon the
event of particular examples of nucleotides at coding areas
have been proposed. Regardless, these techniques have not
been totally suitable because of their reliance on an observationally predefined window length needed [11] for a nearby
dissection of a DNA locale. Many authors[10,12-15,16] have
applied CA in bioinformatics.
Rakesh Mishra, et al. [17] has worked on search and use of
promoter region. Look for a promoter component by RNA
polymerase from the to a great degree extensive DNA base arrangement is thought to be the slowest and rate-confirming
for the regulation of interpretation process. Few immediate
investigations we portrayed here which have attempted to accompany the robotic suggestions of this promoter look[18].
Christoph Dieterich, et al. [19] made an extensive study on
promoter region. The robotized annotation of promoter districts joins data of two sorts. To begin with, it recognizes crossspecies preservation inside upstream districts of orthologous
genes. Pair wise too a various arrangement examinations are
processed. Vetriselvi Rangnanan, et al. [20] made a dissection
of different anticipated structural lands of promoter [19] districts in prokaryotic and in addition eukaryotic genomes had
prior shown that they have a few normal characteristics, for
example, lower steadiness, higher curve and less bendability,
when contrasted and their neighbouring areas [20]. Jih-Wei
Hung[21] has developed an effective forecast calculation that
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can expand the recognition (power =1 - false negative) of promoter. Authors introduce two strategies that utilize the machine force to ascertain all conceivable examples which are the
conceivable characteristics of promoters. Some of the other
authors [22-26] has worked on promoter identification and
succeeded to some extent.

AIS-INMACA(Artificial Immune SystemIntegrated Multiple Attractor Cellular Automata)

Multiple Attractor Cellular Automata which is used in this report is a special class of fuzzy cellular automata which was introduced thirty years ago. It uses fuzzy logic [27,28] to handle
real value attributes. The development process / implementation of Multiple Attractor Cellular Automata is administered
by AIS technique, a Clonal Algorithm with the underlying
theory of survival of the fittest gene.
Artificial Immune System is a novel computational intelligence
technique with features like distributed computing, fault /error tolerance, dynamic learning, adaption to the frame work,
self monitoring, non uniformity and several features of natural
immune systems. AIS take its motivation from the standard
immune system of the body to propose novel computing tools
for addressing many problems in wide domain areas. Some
features of AIS which can be mapped with bioinformatics
framework are chosen and used in the thesis to strengthen the
proposed CA classifier.
This paper introduces the integration of AIS with CA which is
first for its kind to produce a better classifier which can address
major problems in bioinformatics. This proposed classifier
named artificial immune system based multiple attractor cellular automata classifier (AIS-INMACA) uses the basic frame
work of Cellular Automata (CA) and features of AIS like self
monitoring and non uniformity which is potential, versatile
and robust. This is the basic motivation of the entire research.
The objectives of AIS based evolution of MACA [13-14, 28] is
1. To improve the conception of the ways CA performs calculations.
2. To Figure out how CA may be advanced to perform a particular computational job.
3. To follow how advancement makes complex global behavior into locally interconnected cells on a grid.
4. To extract innate classification potential in CA and use
Clonal algorithm for producing better rules with fitness.

Design of AIS-INMACA

The proposed AIS based CA classifier uses fuzzy logic can
address major problems inbioinformatics like protein coding

Figure 1: Central Dogma

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Figure 2: Design of AIS-INMACA
Neighbourhood

111

110

101

100

011

010

001

000

Rule

Next State
( Rule 51)

0

0

1

1

0

0

1

1

51

Next State
(Rule 254)

1

1

1

1

1

1

1

0

254

Table 1: Transition Function
SNO

Rule Number

General Representation

1

1

q i-1 +qi + qi+1

2

3

q i-1 +qi

3

17

qi + qi+1

4

5

q i-1 + qi+1

5

51

qi

6

15

q i-1

7

85

qi+1

8

255

1

Table 2: Complemented Rules
SNO

Rule Number

General Representation

1

254

q i-1 +qi + qi+1

2

252

q i-1 +qi

3

238

qi + qi+1

4

250

q i-1 + qi+1

5

204

qi

6

240

q i-1

7

170

qi+1

8

0

0

Table: 3: Non complemented Rules

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region identification and promoter region prediction .Even
though some scientists have proposed different algorithms, all
of these are specific to the problem. None of them have worked
towards proposing a common classifier whose frame work can
be useful for addressing many problems in bioinformatics.
The general design of AIS-INMACA is indicated in the Figure.
2 Input to AIS-INMACA algorithm and its variations will be
DNA sequence and Amino Acid sequences. Input processing
unit will process sequences three at a time as three neighborhood cellular automata is considered for processing DNA sequences. The rule generator will transform the complemented
(Table 2) and non complemented rules (Table 3) in the form
of matrix, so that we can apply the rules to the corresponding sequence positions very easily. AIS-INMACA basins are
calculated as per the instructions of proposed algorithm and
an inverter tree named as AIS multiple attractor cellular automata is formed which can predict the class of the input after
all iterations.
Algorithm 3.1 is used for creating of AIS-INMACA tree .This
tree will dissipate the DNA sequence into respective leaves of
the tree. If the sequence is falling into two or more class labels, the algorithm wills recursively partition [29] in such a
way that all the sequences will fit into one of the leaves. Every
leaf will have a class .Algorithm 2 will be used for getting the
class as well as the required transition function. The best fitness rules with a score more than .5 is considered. Algorithm
3.3 [7] uses, algorithm 3.1, 3.2 for predicting the protein and
promoter coding regions.

Rules of AIS-INMACA
The decimal equivalent of the next state function, as defined
as the rule number of the CA cell introduced by Wolfram [2],
is. In a 2-state 3-neighborhood CA, there are 256 distinct next
state functions, among 256 rules, rule 51 and rule 254 are represented in the following equations.
Rule 51 : qi(t + 1)
Rule 254 : qi(t + 1)

=
qi(t)		
= qi-1(t)+qi(t) + qi+1(t)

(1)
(2)

Input: Training set S = {S1, S2, • • • , SK}, Maximum Generation (Gmax).
Output: Dependency matrix T, F, and class information.
begin
Step 1: Generate 200 new chromosomes for IP.
Step 2: Initialize generation counter GC=zero; Present Population (PP) ← IP.
Step 3: Compute fitness Ft for each chromosome of PP according to Equation
Step 4: Store T, F, and corresponding class information for
which the fitness value Ft>0.5
Step 5: If number of chromosomes with fitness more than 0.5
are 50 then go to 12.
Step 6: Rank chromosomes in order of fitness.
Step 6a: Clone the chromosome
Step 7: Increment generation counter (GC).
Step 8: If GC > Gmax then go to Step 11.
Step 9: Form NP by selection, cloning and mutation.
Step 10: PP← NP; go to Step 3.
Step 11: Store T, F, and corresponding class information for
which the fitness value is maximum.
Step 11a: Output class, T,F

Algorithm 3.3:
1. Uses the AIS-INMACA Tree construction Algorithm 3.1
2. Uses the AIS-INMACA Evolution Algorithm 3. 2
3. Trace the corresponding attractor
4. Travel back form attractor to the starting node
5. Identify the start codon
6. Identify the stop codon
7. Report the boundaries of protein coding region.
8. From fist codon of the sequence to start codon Search for
TAATAA.
9. Report the promoter boundary located at upstream

The transition function Table 1 was shown for equations 1,2.
The tables 2, 3 show the complemented and non complemented rules.

Algorithm 3. 1:
Input: Training Set S= {S1, S2,…………….., Sx) with P classes
Output: AIS-INMACA tree
Partition(S, P)
1. Generate a AIS-INMACA with x attractor basins (Two
Neighborhood CA)
2. Distribute training set in x attractor basins(Nodes)
3. Evaluate the patterns distributed in each attractor basin.
4. If all the patters say S’ which are covered by the attractor
basin belong to only one class, then label the attractor basin (
Leaf Node) as the class
5. If S’ of an attractor basin belong to more than one class
partition (S’,P’)
6. Stop

Algorithm 3.2 (Partial CLA Algorithm)
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Figure 3: Interface

Experimental Results & Discussions
Experiments were conducted by using Fickett and Toung
data [30] for predicting the protein coding regions. All the 21
measures reported in [30] were considered for developing the
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classifier. Promoters are tested and trained with MpromDb
data sets[31]. Figure 3 shows the interface developed. Figure.
4 is the training interface with rules, sequence and real values.
Figure 5 shows the testing interface. Table. 4 show the execution time for predicting both protein and promoter regions
which is very promising. Table. 5 shows the number of datasets handled by AIS-INMACA. Figure. 6 and Figure. 7 shows
the accuracy of prediction separately which is the important
output of our work. Figure. 8 gives the prediction of extons

Table 5: Number of Data Sets Used

Figure 6: Predictive Accuracy for Protein Coding Regions

Figure 4: Training Interface

Figure 7: Predictive Accuracy for Promoter Regions

Figure 5: Testing Interface
Size of Data Set

Prediction Time of Integrated
Algorithm

5000

1064

6000

1389

10000

2002

20000

2545

Table 4: Execution Time for prediction of both protein and promoter regions.

Figure 8: Exons Prediction

Figure 9: Exons Boundaries Reporting

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Figure 10: Predictive Accuracy for Promoter Regions

from the given input graphically. Figure 9. gives the boundary
of the location of extons. Figure 10. gives the promoter region
prediction and boundary reporting.

Conclusion
We have successfully developed a logical classifier designed
with MACA and strengthened with AIS technique. The accuracy of the AIS-INMACA classifier is considerably more when
compared with the existing algorithms which are 84% for
protein coding and 90% for promoter region prediction. The
proposed classifier can handle large data sets and sequences of
various lengths. This is the first integrated algorithm to process
DNA sequences of length 252,354. This novel classifier frame
work can be used to address many problems in like protein
structure prediction, RNA structure prediction, predicting the
splicing pattern of any primary transcript and analysis of information content in DNA, RNA, protein sequences and structure and many more. We have successfully developed a frame
work with this classifier which will lay future intuition towards
application of CA in number of bioinformatics applications.

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