PROJECT REPORT

Centre for Cellular and Molecular Biology

Summer Training Program (Teacher) 2007

Study on Tryptophan metabolic pathway with

special emphasis on Serotonin activity
– A Flux Balance Analysis Approach

By

J.Febin Prabhu Dass

Lecturer,
School of Biotechnology,Chemical & Biomedical Engg
Vellore Institute of Technology
Vellore- 632 014, Tamil Nadu, India, India

Under the guidance of

Dr. Ram Rup Sarkar

Mathematical Modelling and Computational Biology Group

Centre for Cellular and Molecular Biology
Uppal Road, Hyderabad- 500 007, India
Acknowledgements

J.Febin Prabhu Dass

Contents
CHAPTER 1 INTRODUCTION

1.1 METABOLIC PATHWAYS 1

1.2 OBJECTIVE 1
CHAPTER 2 METHODOLOGY 2
2.1 INTRODUCTION 2
2.2 FLUX BALANCE ANALYSIS 2
2.2.1 Model Building 3
2.2.2 Dynamic Mass Balance 4
2.2.3 Steady state 5
2.2.4 Elemenatary modes 6
2.2.5 Constraints……………………………………………………………
2.2.6 Solution visualization……………………………………………….

2.3 APPLICATIONS 7
2.4 LIMITATIONS 7

CHAPTER 3 DATABASES, TOOLS & SOFTWARES 9

3.1 KEGG METABOLIC PATHWAY DATABASE 9
3.2 TOOLS & SOFTWARES 10
3.3 TOOLS FOR STATIC MODELING OF BIOCHEMICAL SYSTEMS11
3.4 TOOLS FOR DYNAMIC MODELING OF BIOCHEMICAL SYSTEMS 11
3.5 YANA 12
3.5.1 ALGORITHM 12
3.5.2 File formats & Output files…………… ………………………………
3.5.3 Standardization of Software…………………………………………..
CHAPTER 4 L-TRYPTOPHAN METABOLIC PATHWAY ANALYSIS 13
4.1 INTRODUCTION………………………………………………………….
4.4 SERETONERGIC PATHWAY………………
4.2 PATHWAY REPRESENTATION & FILE FORMAT…………
4.3 KEGG REACTIONS…………………………………………..………

CHAPTER 5 RESULTS 14
5.1 CENTRALLITY ENZYMES………………………….
5.2 COMPLEXITY REDUCTION USING THERSHOLD………
5.3 SUB-NETWORKS…………………………….
5.3.1 TRPH (Tryptophan Hydroxylase)……………………………………
5.3.2 INDO(Indoleamine-pyrrole 2,3 dioxygenase)………… …….
5.3.3 AAAD2 (Aromatic Amino Acid Decarboxylase2)..................................
5.3.4 TrpS(Tryptophanyl t-RNA synthase.......................................................
5.3.5 AAAD(Aromatic amino acid decarboxylase)...........................................
5.3.6 Average activity of Key enzymes………………………………………
5.4 OPTIMIZATION…………………………………………..
5.4.1 Tryptophan Hydroxylase-Activity……………………………………
5.4.2 Indoleamine-pyrrole 2,3 Di oxygenase –Activity………………….
 5.4.3 Aromatic Amino Acid Decarboxylase2-Activity…………………..
5.4.4 Tryptophanyl trna Synthase TrpS-Activity………………………
5.4.5 Aromatic Amino acid Decaroboxylase- activity……………………

CHAPTER 6 CONCLUSION…………………………………………………
6.1 SUMMARY OF THE STUDY…………………………
6.2 IMPORTANCE……………………………………………………………….
6.3 FUTURE DIRECTION………………………………………………………

CHAPTER 7 REFERENCES………………………………………………..
 CHAPTER 1
INTRODUCTION

The genomic era has certainly accelerated the research in all bioscience area. The omics data
are used in constructing metabolic pathway for an organism. This in turn improves functional
annotation and provides platform to visualize and analyze these data’s. We can define a
pathway as a biological network that relates to a known physiological process or phenotype.
Metabolic networks can be described in Classical biochemical pathways, Stoichiometric
modeling and kinetic modeling.

Metabolism is the chemical reaction that takes place in a cell, that produces energy and basic
materials, which enables them to keep living, growing and dividing. Metabolic processes are
usually classified as, (i) Anabolism and (ii) Catabolism.Anabolism. Anabolism includes the
chemical reactions that cause different molecules to combine to from larger, more complex
ones. The net result of anabolism is the creation of new cellular material, such as enzymes,
proteins, cells, cell membranes, and tissues. Anabolism is necessary for growth, maintenance,
and tissue repair. Catabolism includes the chemical reactions that break down complex
molecules into simpler ones for energy production, for recycling of their molecular
components

The complexity of metabolic pathway is high as it involves multiple enzymes, which may
have multiple subunits, alternate forms and alternate specificities. Single enzyme may also
involve in multiple pathways also. Metabolic pathways are stored in public databases for
easy accessibility and represented as diagrams manually created and stored as gif picture file
format. Links are available from the picture to identify the gene of interest and generate
orthologous pathway in other organisms.

Metabolic pathways play a crucial role in any organisms from building to breakdown of
biochemical substances. Analyzing, understanding and manipulation of these gives desired
objective could be achieved using flux balance analysis and finally using metabolic
engineering to check the result invitro and invivo. In this study we have considered
Tryptophan metabolic pathway to identify the key enzymes involved in the entire pathway
and finally how to optimize the desired product (metabolite Serotonine) by manipulating the
target flux activity and finally identifying the enzymes which down regulates serotonin
production and inhibit them by administration of an enzyme inhibitor.

Tryptophan is an essential amino acid in animals including human. Since the human body
cannot synthesize tryptophan,. Iit should be taken along with other nutrients. Tryptophan is
aromatic hydrophobic in nature and has a nitrogen atom in Indole ring. This makes them
buried in the hydrophobic interior of the protein structure. Tryptophan serves as a precursor
to serotonin, an important neuro-transmitter. Abnormal metabolism of L-tryptophan may lead
to the reduction of Kkynuric acid, Xxanthurinate, and qQuinolinate. Quinolinate is a
compound that can antagonize NMDA receptors in the brain. Tryptophan in the extra-cellular
fluid or ?(CSF) in the ?(CNS) is available for cellular uptake by multiple cell types with
unique metabolic demands for tryptophan, including serotonergic neurons, mast cells,
astrocytes, microglia, macrophages and dendritic cells, emphasising the need to understand
the molecular mechanisms underlying cellular uptake of tryptophan by different cell types.
The diversity of metabolites of tryptophan in the CNS, including serotonin, products of the
kynurenine pathway of tryptophan metabolism (including 3-hydroxykynurenine, 3-
hydroxyanthranilic acid, quinolinic acid and kynurenic acid), the neurohormone melatonin,
several neuroactive kynuramine metabolites of melatonin, and the trace amine tryptamine,
points towards a need for integrative approaches to understand the functional consequences
of tryptophan metabolism in the CNS.

Serotonin is one among the monoamine has neurotransmitter activity along with Dopamine
and Norephineprine and. Ppossess a single amine (NH2) group. The monoamines can be
further subdivided into an indolamine (Serotonin) and the catecholamines (Dopamine and
Norepinephrine). Most of the serotonin is produced by the brain stem Raphe Nuclei. 5-HT1A
principal target is the principal target for Serotonin but 16 sub-receptors have been identified.
Serotonin mediates antidepressant effects .It may have beneficial effect on reducing
aggressive behavior. It is involved in many functions including mood, appetite, and sensory
perception. In the spinal cord, serotonin is inhibitory in pain pathways.

Although availability of tryptophan in the brain is rate-limiting for the synthesis of serotonin
in serotonergic neurons, the rate-limiting enzyme in the synthesis of serotonin is TPH
(tryptophan hydroxylase; tryptophan 5-monooxygenase; EC 1.14.16.4) (Fig. 4). Until
recently, it was assumed that TPH found in both the periphery and in the CNS was derived
from a single gene. Walther and colleagues discovered a new, brain-specific TPH gene (tph2)
located on the long arm of chromosome 12 region reported to contain susceptibility genes
for bipolar disorder and major depressive disorder. Levels of tph2 mRNA are elevated in the
dorsal and median raphe nuclei of depressed suicide patients and, indeed, subsequent analysis
of polymorphisms in the tph2 gene has supported an association between tph2 genotype and
affective disorders.

TPH1 and TPH2 are tetrameric holoenzymes that catalyse the hydroxylation of tryptophan to
form 5-hydroxytryptophan (5-HTP), which is then readily converted to serotonin (5-HT) by
aromatic amino acid decarboxylase (AAAD)

Our approach in analyzing the tryptophan metabolic pathway is by using a flux balance
approach (FBA). This method uses the stoichiometry of the reaction along with appropriate
constraints on the fluxes to obtain a feasible flux distribution for the reaction pathway using
the principles of optimization. The tool used here creates stoichiometric matrix there by
building a Elementary modes, it gives a simulated enzyme activity in the form of bar charts..
Elementary modes are small set of reactions that cannot be decomposed and they work under
steady state conditions. Since this tool gives the enzyme activity, we try to elucidate the
enzyme activity profile for each key or central enzyme. These key enzymes have also been
also identified by using the tool with various thresholds. This enzyme activity profile can be
achieved by two ways,ways; firstly, we try to break the complex network into small piece of
many sub-networks. By adding each of the sub-network and build them into final complete
network. Here we can easily identify the influence of one enzyme over the other and hence a
complete picture about a particular enzyme of interest can be achieved. Our enzyme of
interest is Aromatic Aamino aAcid Decarboxylase (AAAD), if the activity of this enzyme
increased means it ultimately ends up in high Serotonin activity. Secondly, we forcefully
increase the target flux for particular enzyme or combination enzymes and checking the
associated activity. By this we can conclude the negative and positive regulator of a target
metabolite production,. The target metabolitethat is serotonin. Serotonin has influence in
human behavioral and physiological changes. By doing these type of analysis one can easily
identify and target both kinds of enzymes. If any enzyme has positive regulation, that is the
amount of enzyme activity increases with increase product other than the influencing
enzyme. A enzyme agonist can be used and targeted and a antagonist for negative regulating
enzyme vice versa.
Put a short description of what we are going to do and how?Meaning is not clear for
the last few sentences

Write a elaborately the Introduction

 CHAPTER 2

METHODOLOGY

Metabolic pathways are analyzed by developing number of mathematical and biological

models to predict capabilities based on stoichiometric and kinetic information. With the
predicted outcomes can be tested using metabolic engineering to optimize our objective
functions..

Three major methods are available to study metabolic pathways: Metabolic Control Analysis,
Carbon Flux analysis and Flux Balance Analysis (constraints-based approach).

Metabolic Control Analysis will predict single solutions, but requires lots of information
about the parameters of the enzymes in the organism. This will form a drawback of the
method, because a lot of this kinetic information is not yet available.

Carbon flux analysis is a combined mathematical/biological approach based on modified

carbon molecules, which are fed to the organism. Based on measuring where these molecules
are located, predictions can be made about the fluxes within the model.

Flux Balance Analysis (FBA) method is a constraint-based approach. The basic idea behind
this approach is not to look for the precise solution, but to determine the limitations of a
metabolic system. This method will not create a single prediction, but instead it will create a
solution space based on stoichiometric information. The solution space can then be reduced
by biochemical, environmental constraints, physiochemical laws, regulatory etc.. Based on
an objective function, optimalization can be applied to find the flux distribution, which
optimizes this objective function.

2.2 FLUX BALANCE ANALYSIS

Most of the modeling programs built for metabolic pathways gives single solution after input
of more parameters of kinetic data. This has limitation for these developed models as very
less kinetic data is available. FBA is based on the idea to look at the limitations of a
metabolic network. It narrows the range of possible states that a metabolic network can adopt
based on different constraints, which cells must obey.

2.2.1 Model Building

Flux balance model requires all metabolic reactions and metabolites used in the simple
pathway or entire organism metabolic pathway. The enzymes can be used to describe all
biochemical reaction they catalyze. We consider the metabolic network as a directed graph,
where
the vertices are metabolite/substrate concentrations. The edges are the conversions mediated
by enzymes of one substrate into another.

Fig 1 is shows a imaginary metabolic network with three nodes as metabolites A, B and C.
With in the system boundary, Tthe edges connecting the node metabolites are internal fluxes
v1, v2, v3 and v4 are internal flux, that is with in the boundary. Whereas, b1 (coming inside
the system), b2 and b3 (leaving the system) are external fluxes.
Describe the diagram below and then define v1, b2 etc. (with ref. To the fig1)

Where as v1 and b2 is internal and external flux respectively..

Fig. 1: Schematic representation of an example imaginary metabolic pathway

2.2.2 Dynamic mass balance:

This can be explained by using equations, the change of concentration over time based on the
flux through the metabolic network. The change of concentration is the difference between
the rates at which the metabolite is produced and consumed. The sum of all variables in the
equations equals the external fluxes and fulfills the definition of the conservation of mass
principle (no mass is lost during the experiment).

Dynamic mass balances can be written around every metabolite in the system taking the form
of the following equation in matrix notation, where ‘x’ denotes the concentration vector of all
the metabolites, S is the stoichiometric matrix and ‘v’ is the flux vector describing the
activity of all the internal and exchange fluxes. The dynamic equations for the model shown
in fig 1 are given below

(1)

If the above equation (1) is represented in terms of matrix format with the stoichiometric
matrix S and flux vector v, then the equation is: x = S*v, and in expanded form it is

(2)

where x = [dA/dt dB/dt dC/dt]T.

2.2.3 Steady State Analysis:

The internal fluxes can be calculated using steady state assumption. This means all
metabolites in the model are constant. Since metabolism is more rapid and extremely fast
results in steady state in a matter of seconds. This assumption is also known as Quasi steady-
state approximation. So the change of concentration over time is approximately zero.

0=S*v (3)

The dynamic mass balance equations are linear equations. The null space of S can be
obtained. The null space is a set of all operands v, which solves the above equation.
Therefore all the possible solutions for the metabolic network works under steady-state
condition.

2.2.4 Elementary Modes

Elementary flux modes can be represented by the null space. There is a unique set of
elementary modes for a given metabolic network available. Each elementary mode consists
of the minimum number of reaction that it needs to exist as a functional unit. If any reaction
in an elementary mode were removed, the whole elementary could not operate as a functional
unit. The elementary modes are the set of all routes through a metabolic network consistent
with functional unit. The elementary mode is the smallest sub- network of metabolic network
that functions in steady state. Elementary flux modes are the set of irreducible pathways
spanning the solution space.

2.2.5 Constraints

After the system is defined in terms of mass balance equations, the constraints can be
imposed in the model. Different kinds of constraints can be used to limit possible solutions.
Constraints examples like environmental, regulatory constraints, thermodynamic constraints.
2.2.6 Solution Visualization

The advantage of FBA approach is the capability of the visualization of the solutions. The
vectors and constraints can be visualized in 3D graph. The graph generates solution in the
cone form. The cone is a graphical representation of all the solution space obtained from the
stoichiometric matrix, means that every point within the cone represents flux distribution that
represent steady-state assumptions. Using linear programming optimal flux distribution can
be calculated with predefined objective function. We can define different Objective functions
like cellular growth, particular biomass overproduction and minimum nutrition to system
etc... It is possible to generate quantitative hypothesis in silico that may be tested
experimentally.

Fig. 2: Flux Cone

2.3 APPLICATIONS

Every model system has its own advantages and disadvantages. If we take metabolic flux
analysis it gives result with all kinetic parameters. A very large systems can be analyzed from
hundreds to thousands of reactions using FBA and it predicts optimal steady-state flux
distribution in the network with different environments and genotypes (pertubations of
network structure) can be simulated the most important is doesn't require any kinetic
parameters.
2.4 LIMITATIONS

Important data like enzyme concentrations and mechanistic details on enzyme regulation (but
gene regulation can be incorporated as further constraints). It is not possible to track the
dynamics of the system to determine the metabolite concentrations.These approaches not
able to resolve bidirectional steps and cannot observe the cyclic flux, as they can only be able
to determine the net flux.
 CHAPTER 3

DATABASES, TOOLS & SOFTWARES

3.1 KEGG METABOLIC PATHWAY DATABASE

Metabolic databases contain a large amount of data about different pathway, reactions and
ligands in particular about similar pathways in different species.

A large amount of data about metabolic processes in different species is accessible from
freely available databases. KEGG is a collection of 4 large databases called PATHWAY,
GENES, LIGAND, and BRITE, plus various software tools. The PATHWAY database is the
most unique and extensive, and contains protein interaction networks, such as metabolic
pathways, genetic information processing, signal transduction, and infectious diseases. The
GENES database provides extensive cover of experimentally and/or computationally
predicted genetic data originally derived from other public resources, as well as microarray
gene expression profiles obtained from Japanese research groups. The LIGAND database is a
source of detailed information on chemical compounds, enzymes, and glycans, plus their
associated reactions. The BRITE database contains the KO (KEGG Orthology), which refers
to the grouping of two genes as orthologs, when they are mapped to the same KEGG
pathway node, and is used to characterize unknown pathways. In addition, KEGG provides a
number of computational services for
sequence analysis, such as BLAST, FAST, MOTIF, and CLUSTALW.

KEGG is a suite of databases and associated software, interlinking data on small compounds,
reactions, enzymes, and genes (40). The graphical pathway maps to which the databases are
linked are an important feature of KEGG. Other databases are shown in Table 1.

aMAZE http://www.amaze.ulb.ac.be/

BioSilico http://biosilico.kaist.ac.kr
BioCyc http://biocyc.org/

BRENDA http://www.brenda.uni-koeln.de/

EcoCyc http://ecocyc.org/

KEGG http://www.kegg.com/

MetaCyc http://metacyc.org/

PANTHER https://panther.appliedbiosystems.com/pathway
Pathways

PathArt http://jubilantbiosys.com/pd.htm

RegulonDB http://www.cifn.unam.mx/Computational_Genomics/regulondb

Table 1: Biochemical pathway databases

The major advantage of KEGG is that it provides a big picture of reaction related to a
specific substrate. Some disadvantage like its inconsistent functions for genes that is
annotation is not equal to assigned reactions, incorrect functional assignments, overestimates
the number of gene pairs.

3.2 TOOL & SOFTWARES

There are many tools available for metabolic pathway study. Computational tools are
available for both static and dynamic simulations. The general aim of simulations of these
metabolic networks is to estimate the time evolution of the concentrations of the substances
in the system (41). In table 2, we have put a listed of few available tools for biochemical
modeling of both static and dynamic.

Metatool, ftp://bmsdarwin.brookes.ac. uk/pub/software/ibmpc/metatool

Bio-SPICE, https://biospice.org/

CellDesigner, http://www.celldesigner.org

COPASI, http://www.copasi.org

E-Cell, http://ecell.sourceforge.net/

FluxAnalyzer, http://www.mpimagdeburg.mpg.de/en/research/projects/1010/1014/
1020/mfaeng/fluxanaly.html

Gepasi, http://www.gepasi.org/

MetaFluxNet, http://mbel.kaist.ac.kr/mfn/

YANA etc http://yana.bioapps.biozentrum.uni-wuerzburg.de

Table 2: Softwares for Mmetabolic pathway simulation and analysis

softwares

3.3 Tools for Static modeling of biochemical systems

Three methods are available under static modeling of biochemical systems,:

Structural Analysis is aimed at elucidating relevant relationships between systems variables

on the basis of network stoichiometry without reference to kinetic properties

Metabolic Control Analysis serves to quantify, in terms of control coefficients, the extent to
which different enzymes limit the flux under particular conditions

Evolutionary Optimization analyzes systems parameters on the basis of evolutionary

optimization principles (eg. maximum reaction rates, minimum intermed. Concentrations and
minimum transient times)

What is this not linked with previous section? Among the three static methods, we use the
first method which uses the stoichiometry . tThe static modeling of biochemical system is
based on steady-state or Elemetary mode analysis (Schuster & Hilgetag (1994)). Some facts
about EFMs isare, any real flux can be represented as a superposition of elementary modes,
in fact it is a linear combination with positive coefficients and any stationary state can be
decomposed with respect to the flux values, in elementary modes which are realizable
stoichiometrically and thermodynamically. Experinments have been done in some
prokaryotes for example in E. coli prediction of gene expression (Stelling et al Nature Nov
14 2002).

3.4 TOOLS FOR DYNAMIC MODELING OF BIOCHEMICAL SYSTEMS

GEPASI (42) is one of the software that allows, covering the construction of biochemical
models using kinetic parameters, optimizing optimization of the models, and simulation ofng
metabolic control analyses and linear stability analyses. Some of the specific features of
GEPASI include the characterization of steady states using a metabolic control analysis and
linear stability analysis, a scan utility for the advanced exploration of a model’s behavior in
multidimensional parameter space, data fitting or parameter estimation with experimental
data, visualization of simulation results in 2D or 3D directly from the program, and the
support of SBML level 1 for data interchange with other systems biology software.

Some of the issues of kinetic models, Kinetic properties (rate constants, etc) are not
completely known, Discrepancies exist between in vitro and in vivo behavior and
Eenzyme activities in vivo are subject to frequent changes due to inhibition or activation

3.5 YANA

In this study we used Yet Another Network Analysis (YANA) software, which comes under
static modeling of biochemical systems. Actually YANA (43) is a platform- independent,
toolbox for metabolic network with a GUI to METATOOL. Using YANA we can visualize,
centralize, and compare elementary flux modes and shows activity pattern of enzymes.
Dissection algorithm, a centralization algorithm, and average diameter for simplify and
analyze complex networks. YANA features a fast evolutionary algorithm (EA) for the
prediction of EM activities with minimum error. For example, Tthe representation of YANA
main screen showing the 47 metabolite, 25 enzymes and 66 elementary modes are shown in
fig. 3.
What is fig 3? Write what is there?

Fig. 3: YANA software with metabolite and reactions

3.5.1 Algorithms

Based on the steady state assumption a number of algorithms are available. But the most
useful algorithm is Elementary Mode Analysis (EMA). This EMA algorithm is implemented
using METATOOL. EMA is an algorithm that systematically enumerates all possibilities
how enzymes can operate together without violating the steady state condition of the system.
Metatool is integrated in other softwares like GEPASI, and CellNetAnalyzer.
YANA is just acting as graphical toolbox and an editor for METATOOL. With In additional
YANA integrates metabolic complexity reduction and centrality calculation algorithm. It has
additional algorithm to calculate flux distribution, even if one enzyme activity or very few
are known. For accurate predictions of enzyme activities experimental data on flux ratios is
helpful.

3.5.2 File formats and Output files

A manual editing for input each metabolite (Internal or External), reactions ( reversible or
irreversible) and enzyme involved are possible of the input files are possible. YANA accepts
both METATOOL format and System Biology Markup Language (SBML)(44) file formats
of version 1. The out put file given as Elementary modes as cascade of enzymes or net
reacitons, calculation of specific flux distribution and visualization in the form of bar charts
named simulated enzyme activity in the gel. A specific EM activity pattern best fitting the
user given flux distribution is also obtained.

3.5.3 Standardization of software

The YANA tool was standardized using the glutathione reductase pathway as mentioned in
the literature of yanaYANA. The human redox metabolism (75 metabolites: (46 internal, 29
external,) and 58 enzymes), around the central enzyme glutathione Glutathione reductase has
been taken for further analysis. This analysis yields the same number of metabolite,
reactions and elementary mode and enzyme activity. this system yields a total of 134 EMs.
From these, 46 include glutathione reductase, 117 involve energy consumption (ATP),
whereas 128 involve redox reactions and further reference from [no1] and [no2]. GR as a
central enzyme of the network has an activity of 399. Besides this, the most active enzymes
are: GAPDH (598), PGM (598), LDH (598), PGK (560), PK (598) and EN (598), as a parts
of glycolysis, and the enzymes G6PD (576); PGLase (576) and GL6PDH (576), as
components of the oxidative part of the pentose phosphate pathway. For the obtained flux
distribution, we have noticed a tight connection between glycolysis and the glutathione
reductase metabolism. The main pathways of glycolysis and PPP supply energy and
reduction equivalents for strong redox protection provided by the glutathione reductase
network. With the obtained results we have cross checked with published data (ref.) and both
the result were found to be same using YANA.

Where is the details of the standardization?done

CHAPTER 4

L-TRYPTOPHAN METABOLIC PATHWAY ANALYSIS

L-Tryptophan biosynthesis is absent in mammals but is a general metabolic capability of

prokaryotes, eukaryotic microorganisms and higher plants. L-Tryptophan is biochemically
the most expensive of the amino acids to synthesize [45]. L-Tryptophan is essential amino
acids for mammals as the biosynthethic pathway of tryptophan is absent in them. Tryptophan
is the only aminoacid to bind with Albumin. The tryptophan metabolic pathway produces
several neuroactive compounds with the central nervous system. Our interest is on the
serotonin metabolite, which is a key neurotransmitter along with Dopamine and
Norepineprine. The central role of serotinergic system in the modulation of physiology and
behavior has been well documented since the first description of serotonergic neuron in the
brain some forty years ago. Tryptophan is the only amino acid that bound to plasma Albumin
and at rest only 10% is available in peripheral blood circulation (46). The entry of tryptophan
to brain through ?(BBB) is the is critical in regulation of brain. The rate of serotonin
synthesis can be either increased or decreased by altering the rate of tryptophan transport
across BBB (47,48).

4.2 PATHWAY REPRESENTATION & FILE FORMAT

Metabolic pathway of tryptophan is represented in KEGG. KEGG is featured with the entire
organism with their known genome. It also has a reference pathway for tryptophan, which
has all the enzymes involved in the metabolism with the map number 00380. A map of
human specific tryptophan metabolic pathway was obtained from the KEGG with the map
number hsa00380 (homo sapiens) (Fig. 4).
 hsa00380

Fig, 4: Graphical representation of tryptophan metabolic pathway

The graph contains the entire product, reactants and enzymes involved, where all these
enzymes are represented by Enzyme Commission (E.C numbers). Then each metabolite is
identified and the associated Enzymes and coenzymes were are identified. By following the
link of the E.C number we can actually get the individual reactions.

KEGG file format apart from the normal Graphical representation can be obtained from
KEGG. The KEGG Markup Language (KGML) is an exchange format of the KEGG graph
objects, especially the KEGG pathway maps that are manually drawn and updated. KGML
enables automatic drawing of KEGG pathways and provides facilities for computational
analysis and modeling of protein networks and chemical networks. The KGML files for
KEGG metabolic pathways contain two types of graphical objects, how enzymes boxes
(boxeenzymes) are linked by a relation and how compounds circles (circles compounds) are
linked by a reaction in the KEGG pathway diagrams. In contrast, the KGML files for KEGG
regulatory pathways contain only the aspect of how proteins boxes (boxes proteins) are
linked by a relation. Note that boxes are identified by KO (KEGG Orthology) identifiers in
the current KEGG system, but for historical reasons boxes in the metabolic pathways are
marked with EC numbers in the actual pathway diagrams.

4.3 KEGG REACTIONS

By following the enzyme number link 1.14.16.4 (tryptophan hydroxylase) KEGG gives the
reaction. This will link to a reaction number R07213 on following the reaction number for
this individual enzyme. We still get clear picture as shown in fig 5.

Fig. 5: Representation of Reaction in KEGG

4.4 SEROTONERGIC PATHWAY

Tryptophan (Trp) in the extracellular fluid is transported into the serotonergic neuron by a
high-affinity neuronal tryptophan transporter. In the serotonergic neuronal cell body, the rate-
limiting enzyme tryptophan hydroxylase 2 (TPH2) is located in the cytoplasm and, using
molecular oxygen (O2) and the cofactor tetrahydrobiopterin (BH4) catalyses the
hydroxylation of tryptophan to 5-hydroxytryptophan (5-HTP), which is rapidly converted to
5-hydroxytryptamine (5-HT, serotonin) by aromatic l-amino acid decarboxylase, using
pyridoxal-5′-phosphate (P5P) as cofactor. TPH2 activity is increased by action-potential- and
Ca2+-dependent phosphorylation, and the phosphorylated form of TPH2 is stabilised by
binding to 14-3-3 protein. Serotonin is then either: (a) metabolised by flavoprotein
monoamine oxidase B (located on the mitochondrial membrane) to 5-hydroxyindole
acetaldehyde (5-HIA) and then by aldehyde dehydrogenase to the acid metabolite 5-
hydroxyindoleacetic acid (5-HIAA), which is excreted from the cell by an energy-dependent
clearance mechanism; or (b) packaged in synaptic vesicles, via the action of vesicular
monoamine transporter 2 (VMAT2), bound by the carrier protein serotonin-binding protein
(SBP), and then released into the extracellular fluid by calcium-dependent exocytosis. (c)
Released serotonin can be recycled within the cell following reuptake by the serotonin
transporter (SERT) or can be metabolised to 5-HIAA by postsynaptic cells containing
monoamine oxidase for chemical structures of key metabolites (48.1). The two steps reaction
for serotonin production is represented in fig. 6.
Fig. 6: Serotonin Reactions from Tryptophan
 CHAPTER 5

RESULTS

5.1 CENTRALITY ENZYMES

YANA has two types of threshold, Centrality and Cutting. Using the centrality threshold, all
metabolites with high connectivity are value below the threshold as external. In this way,
only connections between the core nodes of a metabolic system are included, neglecting
those on the outskirts. The resulting pathway set still holds the most important EMs,
shortened and focused on the central hub metabolites (49). Various cut-off is given and the
central enzymes are shown in Table 3.

Threshold AAAD2 INDO TRPH TrpS AAAD EM DIAM

(Central)
0 20(30%) 36 20(30%) 11 11 66 5.56
(54.54%) (16.6 %) (16.6%)
3 20(30%) 36 20(30%) 11 11 66 4.39
(54.54%) (16.6 %) (16.6 %)
4 4(25%) 4(25%) 4(25%) 4(25%) 0 16 2.0

5 4(40%) 4(40%) 4(40%) 4(40%) 0 10 2.0

EM = Elementary mode; DIAM = Diameter

Table 3: Enzyme Centrality

Change the style of presenting the result

From the Table 3, we infer that different cutt-off or Threshold given for metabolites for
identifying the central enzymes. Four cutt-off values are used for this purpose, each time
there is different activity or availability of enzymes. at At zero threshold of centrality the
complete activity of all enzymes can be observed and at theresholds one and two, also shows
the same activity as zero threshold. Using the threshold three we could observe some changes
in enzyme activitys, at least some enzymes are not showing activity in the bar chart and still
the number of elementary modes are same at 66 but there is a change in diameter from 5.56
to 4.39. Using threshold four there is sudden decrease of elementary modes from 66 to 16
with the diameter of 2.0 and most of the enzymes has null activity. Using the threshold five
there changes the elementary mode finally to 10 with same diameter as for cutt-off four and
their finally Five enzymes emerge ( fig.7) as the central enzyme or key enzyme according to
the threshold given in YANA. These five enzymes are only tabulated in table 3 and their
change in Percentage activity along with different threshold. The correlation between
enzyme connectivity and centrality in Tryptophan metabolic pathway havecorrelation
between enzyme connectivity and centrality in Tryptophan metabolic pathway has been
done from the output picture of YANA with centrality threshold in (fig 7).
Using thise threshold simplification of the Tryptophan metabolic system by we concentration
concentrate on highly connected metabolites (centralization) for further analysis.
From this the key enzymes, like AAAD2, INDO, TRPH, TrpS are responsible for various
major pathways like serotonergic, kynureninergic and tryptaminergic, which is clearly visible
in the pathway. Since our enzyme of interest is AAAD (this enzyme activity yields the
Serotonin), we are taking these this enzymes into account for further analysis. as this enzyme
activity yields the Serotonin.

After identifying these key enzymes, we focus only on these enzymes along with the enzyme
of our interests.
 Fig. 7: Key enzymes at centralization (at Threshold 5)

5.2 COMPLEXITY REDUCTION USING THRESHOLD

Using the cutting threshold one can reduce network complexity. Both change systematically
the internal / external status of the metabolites using their connectivity values as the basic
criterion. Using the cutting threshold, the network is divided by automatically setting
metabolites with a connectivity value above a certain user-defined threshold as "external"
(50).

Threshold AAAD2 INDO TRPH TrpS AAAD EM DIAM

(cutting)
>5 20(30%) 36 20(30%) 11 11 66 5.56
(54.54%) (16.6 %) (16.6%)
4 2(7.4%) 6(22.2%) 2(7.4%) 0 2(7.4%) 27 4.0

3 2(13.3%) 2(13.3%) 2(13.3%) 0 2(13.3%) 15 3.27

2 0 0 0 0 1(20%) 5 2.4

EM = Elementary mode ; DIAM = Diameter

Table 4: Simplification of complex metabolic pathway

…………………………………….
After the identification of central or key enzyme now we try to simplify the tryptophan
metabolic pathway using another threshold parameter given in YANA. Here inIn table 4 we
concerntrate only on these enzymes in for simplification of the metabolic pathway. Since the
total number of enzyme in this pathway is 25 its is can be easily simplified with four cutt-off
values.

Using a threshold of 5 (metabolites participating in more than five reactions are considered
external). There is no change in complexity of enzyme activity as all the enzymes are present
are active or present as usual.

At Threshold 4 the number of elementary mode reduces from the total 66 into a total of 27
elementary modes with reduction or absence of some enzymes with the diameter of 4.0.

At Threshold 3 splits these sub-networks further into a total of 15 elementary modes with a
certain reduction in complexity or simplification of network. At cutt-off 2 only 12 enzymes
are active as shown in (fig 8) or connected with the metabolites and remaining 13 were
absent . this is a simple techniquc available in YANA and could be used efficiently in for
higher complex pathways particularly if complete organismal flux balances is are needed to
be analyzed.

Fig. 8: Reducing network complexity Cutting ( at Cutting Threshold 3)

5.3 SUB-NETWORKS

The entire metabolic network has been split up into small sub-networks without violating the
steady state. The tryptophan metabolic network has been divided into a total of 11 sub-
networks. The purpose of this study was to identify the behavior of key enzymes, when they
are in minimum number of networks to the complete activity. How the addition of each small
sun-network affects the overall activity of the key enzyme identified and the enzyme of
interest.

The Few examples of differentdivided sub-networks obtained from the original network is
are shown below as pictorial representation in fig. 9.
 Fig 9: Example of
different sub-networks

The total tryptophan metabolic network was separated to product specific sub-networks.
From Serotonin as the sub-network one, it goes like Tryp t-rna synthase as number two.
This entire network was totally subdivided into eleven product based simple reaction
 network. The purpose of subdividing is to check the enzyme profile or rather the enzyme
activity behaves with the response to addition of simple sub-networks.

Using these this type of analysis we can identify the activity of key enzymes that plays a
active role and infer how the percentage changes from one network to another when the
number of reactions increased with enzymes.

5.3.1 TRPH (Tryptophan Hydroxylase)

(Enzyme involved in the conversion of Tryptophan to 5 Hydroxy Tryptophan)

The activity of the important enzyme: , Tryptophan Hydroxylase (TRPH). acts as a rate
rate-limiting enzyme of serotonin production. Tryptophan Hydroxylase (TRPH). We see
the activity of TRPH is very high and at the maximum level, even aAt the beginning in
network 1 and 2 from the graph, there was were few enzymes, that is only network 1 and
2 from the graph, we see the activity of TRPH is very high and at the maximum. But
when we keep on adding the other sub-network together it shows a wavy like activity.
The activity of the enzyme from single to total network from the graph as shown in fig.
10, we can infer the activity of this enzyme and each addition of sub-networks and
enzyme affects the activity of TRPH.

Tryptophan Hydroxylase(TRPH)
100
%Activity

80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11
SUB-NETWORKS

Fig 10: TRPH Activity

5.3.2 INDO(Indoleamine-pyrrole 2,3 dioxygenase)

Enzyme involved in the activity of converting Tryptophan to N-formyl Kynurenine.

Indoleamine-pyrrole 2,3 dioxygenase(INDO)

100
80
%Activity

60
40
20
0
1 2 3 4 5 6 7 8 9 10 11
SUB-NETWORKS

Fig. 11: INDO Activity

This network also behaves like TRPH as shown in fig. 11 but the difference is the overall
Percentage activity is increased compared to TRPH. This too forms a wavy graph and it’s
sensitive to addition of different sub-networks and enzymes.

5.3.3 AAAD2 (Aromatic Amino Acid Decarboxylase2)

(Enzyme involved in the conversion of Tryptophan to Tryptamine.)

Aromatic Amino acid Decarboxylase(AAAD2)

100
% Activity

80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11
SUB-NETWORKS
 Fig 12: AAAD2 Activity

This Tryptamine is another important metabolic product of Tryptophan metabolism. The sub-
network involeved only at the reaction number six and its shows a constant activity and high
fluctuations and overall activity of the enzyme is high comparitivly with other enzymes.

5.3.4 TrpS(Tryptophanyl t-RNA synthase)

(Enzyme involved in conversion of Tryptophan to Tryptophan t-RNA)

Tryptophan trna synthase (TrpS)

100
80
%Activity

60
40
20
0
1 2 3 4 5 6 7 8 9 10 11
SUB-NETWORKS

Fig 13:TrpS Activity

By addition of other sub-networks, the enzyme activity of TrpS declines and steadily as
shown in fig 13. From this we infer that influx of tryptophan is metabolized through various
pathway and the quantity available for formation of Tryptophan t-RNA, as this is a useful
metabolite for protein synthesis in the neuronal cells.
What are the results (differences) you are observing from your study for each Enzymes—say
in details? It is not clear from the way you have written.Written down

5.3.5 AAAD (Aromatic amino acid decarboxylase)

(Enzyme involved in conversion of 5-Hydroxy Tryptophan to Serotonin)

AromaticAminoAcid Decarboxylase(AAAD)
100
80
%Activity

60
40
20
0
1 2 3 4 5 6 7 8 9 10 11
SUB-NETWORKS

Fig. 14: AAAD Activity

This is an important enzyme, which actually converts the 5HT to Serotonin. Looking at
this enzyme activity in fig 14 there is a steady decay of activity as the number of sub
networks added. of is no difference with TrpS except the average percentage is high. Since
its not a direct product from Tryptophan a previous step of TRPH plays a major role in
serotonin production.

Comparing the sub-network results, gives a good idea of how each of the key enzymes
behave when there is additional enzyme activity is added or not available for some time. So
here we trying to explore the non availability of some enzyme in their own sub pathway or
other associated pathways. This type of analysis could be correlated to metabolic
perturbation, gene deletion or mutation, by which we can study the change of flux or fluxes
consequently forces the metabolic system to a new state and affects the attainment of its
steady state flux. Using this type of arbitrary removal and addition of certain enzmes(sub-
networks) to study how the key or central ezymes are getting affected due to this type of
perturbation.

From the result of fig 10, fig 11 and fig 12 , we could obviously see the metabolic system is
quite unstable particulary in enzymes viz TRPH, AAAD2 and INDO . A lot of fluctuation
that happens in the system is clearly visible on addition or deletion of some metabolic
subgroups. This states that the system could not take this type of pertubations where a
number of enzymes have been deleted or added at a single point of time at least for these
three enxymes mentioned above. The wavy nature of graph figures show each of this three
enzymes are too sensitive for these kind of deletion or addition of enzyme analysis. From this
we observe that the metabolic system is not quite stable under perturbation of these kinds.
On the other hand fig 13 and fig 14 implies that they can accept the perturbation in a good
way, the enzymes involved are AAAD and TrpS. AAAD is the taget enzyme for producing
Serotonin and we observe was its quite stable for the perturbation though its associated
enzyme(TRPH) fluctuates there has been a steadiness in AAAD which means that Serotonin
production is always there even some of associated enzymes in tryptophan got mutation or
lost its activity. Finally the TrpS enzyme activity is also stable like AAAD which gives a
clear implication that even complete system of tryptophan metabolic pathway is pertubated
its not affecting the activity of TrpS as this enzyme steady activity is a must for the protein
synthesis.

5.3.6 Average Activity of key enzymes

The average activity of the key enzymes were calculated and its been found that the activity
of INDO is highly active and least active is TrpS. The enzyme responsible for serotonin
production is third highly active.
Enzymes AAAD TRPH INDO TrpS AAAD2
AVG-act 43.82 53.32 57.77 27.14 39.03

Table 5: (Average activity of key enzymes)

There was one more observation from key enzymes activity is that in addition of network
number 10. a sudden increase in all the enzyme activity. The enzyme is 2 Amino 3-carboxy
muconate semialdehyde decarboxylase (2ACMSD). This enzyme has a good influence in
increasing the overall enzyme activity including serotonin production.

5.3 OPTIMIZATION

The objective function is to identify or tune the metabolic system such that there will be
increased activity of particular enzyme that finally give rise to the desired product. In order to
achieve that, we altered the target flux of key enzymes and enzyme of interest. Out of all the
participating enzymes that are available in tryptophan metabolic pathway, if we could ??
know target flux for some enzyme it would be easy to identify the associated enzyme
activity.

These kinds of studies may help to reveal the relative importance of a specific enzyme and to
predict the impact on the flux distribution if the enzyme is not active, e.g. due to a mutation
or due to the administration of an enzyme inhibitor.

5.4.1 Tryptophan Hydroxylase-Activity

Keeping the Tryptophan amount of influx unknown and setting the activity of TRPH and
AAAD the following results are obtained.

2ACMSD
Fig. 15: Up-regulation ofTRPH-Act
3HAD
TRPH using target flux distribution
AAAD
AAAD1
As we see from the fig 15, the unknown flux for trypin (Trptophan trasporter)
AAAD2 is higher than
5 AAAT
all other enzyme activiy. This also reveals that after upregulation of TRPH enzyme, the
AAAT1
AF
enzymes4 like AAAD, sero and TrpS are also higgly active. AF1
AF2
ALDH2
%Activity

3 CAT
In addition to these, though making the target flux zero for other enzyme
IMMTstill some enzymes,
2 INDO
INDO ,KA, KA1, AF,AF1 and AAAT are showing up some activity. INDO1
KA
1 us that formation of Kynurenine and other associated product
This tells KA1 are formed even
KA2
when TRPH is active in the system. KMO
0 MOA
MOA1
1 Sero
TRPH
Enzymes TrpS
trypin
5.4.2 Indoleamine-pyrrole 2,3 Di oxygenase –Activity

2ACMSD
INDO_ACT 3HAD
Fig 16 Up-regulation of INDO using target flux distribution AAAD
AAAD1
AAAD2
5 AAAT
AAAT1
AF
4 AF1
Upregulation of INDO enzyme as shown in the fig 16 and tryptophan AF2and TRPH influx
%Activity

unknown 3 and Tryptamine flux distribution to Zero.In this scenario itsALDH2

very clear only the
CAT
IMMT
INDO and INDO upregulation of
2 their associated enzymes are highly active. It automatically shows
INDO1
KA
their associated enzymes like KA,CAT, AAAT1 and AF etc. KA1
1
KA2
As usual the influx to tryptophan is very high and with activity of TrpS enzyme
KMO is obsereved.
0 MOA
But there is null activity of TRPH and AAAD, this means the serotonin MOA1
production is nil.
1 Sero
TRPH
Enzymes TrpS
trypin

5.4.3 Aromatic Amino Acid Decarboxylase2-Activity

Setting the activity of AAAD2 high flux and making INDO zero activity. Making
Tryptophan influx and TRPH unknown, we obtained the following activity,

2ACMSD
AAAD2_ACT
Fig 17: Up-regulation of AAAD2 using target flux distribution3HAD
AAAD
AAAD1
AAAD2
5 activity of AAAD2 high as shown in fig 17 certainlyAAAT
Tuning the give high yield of
AAAT1
AF
Tryptamine
4 and N-Methyltryptamine with their respective enzymes highlyAF1 active AAAD2
AF2
% Activity

and IIMT. ALDH2

3 CAT
IMMT
INDO
Here too2the influx of Tryptophan is too high and TrpS activity is as similar to AAAD2
INDO1
KA
Tryptaminergic
1 activity. As the above two metabolite are important in brain
KA1 functions.
KA2
There is some minor quantity of kynurenine enzymes active and nullKMO activity of Serotonin
0 MOA
and TRPH enzymes. MOA1
1 Sero
TRPH
Enzymes TrpS
trypin

What are the results (differences) you are observing from your study for each Enzymes—say
in details? It is not clear from the way you have written. Writtern below
 5.4.4 Tryptophanyl t-rna Synthase TrpS-Activity

2ACMSD
TrpS-Act 3HAD
AAAD
AAAD1
AAAD2
5 AAAT
AAAT1
4 AF
AF1
AF2
%Activity

3 ALDH2
CAT
2 IMMT
INDO
INDO1
1 KA
KA1
0 KA2
KMO
1 MOA
MOA1
Enzymes Sero
TRPH
TrpS
trypin
Fig 18: Up-regulation of TrpS using target flux distribution

The upregulated condition of TrpS as shown in fig 18, the desired product of Serotonin and
its associated enzymes TRPH, Sero and AAAD are active.
There is null activity of Tryptaminergic and Kynureninergic enzymes.
Actually TrpS has lesser activity that the other active enzymes
5.4.5 Aromatic Amino acid Decaroboxylase- activity

Here we did not show the result of upregulated activity of AAAD which is important in
conversion of Tryptophan to serotonin.This give the similare activity as upregulated TRPH
activity.

Since increased activity of AAAD produces a same graph result as TRPH activity from this
we infer that for serotonin production TRPH is very important and it’s the rate limiting
enzyme for Serotonin production. So if TRPH activity is high which automatically results in
high activity of AAAD, which in turn produce Serontonin metabolite and vice versa.

A comparative analysis of the optimization results, gives us clear picture of the network
vulnerabities , which could be exploited for drug target identification. Our enzyme of interest
as mentioned along with the key enzymes. As mentioned in literature that TRPH enzyme is
the rate limiting enzyme in the production of Serotonin. If we see the combined result of each
key enzyme optimization for all the five enzymes, TRPH activity is highly active when there
is upregulation of their pathway enzymes for example if there is increase in the activity of
AAAD or TrpS. TRPH is the initiation enzyme from tryptophan to serotonin and
kynuramine. So further studies could be done on these three enzyme alone to see which of
TRPH associated enzyme activity could be increased to increase the TRPH activity itself
there by increasing the desired metabolite serotonin. TrpS is active all the time irrelevant of
Serotonin related enzymes active or inactive. So increasing the TrpS activity above ceratin
level wont bring benefit in serotonin production. AAAD as such increases the production of
Serotonin, this enzyme is highly available only there is availability of Typtophan influx and
TRPH activity. By doing these analysis we come close to find the enzymes, which enhances
the activity of Serotonin. Considering the non serotonergic pathways from tryprophan
metabolic pathway like Kynurenine (INDO) and Tryptamine (AAAD2) , these enzyme has
rather much less influence in Serotonin production. In the other way up-regulation of these
enzyme produced null production of Serotonin, this means that these enzyme plays a
negative role in AAAD and TRPH active in enhancing the productivity of Serotonin. As we
can observe from fig. 15, the enzyme activity of upregulated TRPH also gives rise to activity
of Kynurenine and some of the metabolites involved AF, KN and F1. This clearly tell that
this INDO pathway have some role to play in TRPH and AAAD activity and this could be
further studied to see the effect of INDO on TRPH and AAAD as they involved in producing
Serotonin. INDO and AAAD2 has very prominent negative role in production of serotonin as
we see in fig. 16 and fig. 17, there is absolutely no activity of serotonin producing enzyme
activity. On analyzing the central enzyme activity in tryptophan metabolic pathway with
reference to production of Serotonin, the enzymes AAAD, TRPH and TrpS has have
enhancing activity with respect for the production of Serotonin. INDO and AAAD2 has have
negative regulation in the activity of Serotonin producing enzymes. This is how we explored
the vulnerabiiliteisvulnerabilities in tryptophan metabolic pathway, which could lead us in
choosing a potential target enzyme in biological perspective.
 CHAPTER 6

CONCLUSION

Totally there are four different study has been done with this Tryptophan metabolic
pathway using YANA. The first one is the indentifyingt the central of key enzymes in this
metabolic pathway and we have identified five key enzymes along with enzyme of
interests. Secondly, we did simplification of the tryptophan complex pathway into simple
one by using the threshold values. Thirdly, Tthe subnetwork division and joining was
done to identify the enzyme profile with respect to the central or key enzymes. And finally
optimization was done, that is upregulation or increasing the target flux for the key
enzyme and checking the activity of other associated enzymes.

From these analysis on Tryptophan metabolic pathway one can completely analyze the
pathway with metabolite production and decomposition with respect to the active
enzymes. We can also infer how one enzyme activity affects the other at steady state and
desired metabolite important for the system existence.

The usage of flux balance analysis is much useful in pharmaceutical industry in the way of
analyzing the complete set of biochemical network and identifying a potential target
enzyme. This drug target identification is possible by analyzing the biochemical network
vulnerabilities. After analyzing the tryptophan metabolic pathway we could further go in
detail of identifying the particular enyme, which suppresses the activity or because of its
overexpression it inhibits the production of certain metabolite, that is nessecearynecessary
for normal physiological functions. In this way serotonin metabolite as a product in the
tryptophan metabolic pathway could be enhanced by enhancing the enzyme activity which
produce serotonin or suppressing the enzyme activity which inhibits the production. FBA
can be used in understanding the range of metabolic pathways in a network, like all the
elementary modes obtained in this work. The optimization is mostly done to increase the
amount of desired product which we have checked in silico could be taken to next level
for Engineering them invitro and invivo this could be applied in field using Metabolic
engineering.
Write a little bit elaborately
 CHAPTER 7

FUTURE DIRECTIONS

With the up-regulated activity of key enzymes, its very clear that high influx of
TRYPTOPHAN Amino acid is needed for the system to produce the metabolite and other
product that are needed for the brain in this case. We could also conclude that activity of
Tryptophanyl tRNA Synthase is always active no matter which enzyme is upregulated or
downregulated, this clearly defines the importance of Tryptophan t-RNA is need for cell for
protein synthesis and other functions to be performed.

During the addition of sub network to identify the individual key enzyme activity,
the enzymes INDO have high average activity along with most of the key enzymes, like
TRPH. So this could be studied further on INDO with TRPH activity in the future.
This also defines that specific target enzyme could be identified from INDO sub-pathway
which could have negative effect in the production of Serotonin. As already available drug
products like Mono Amine Oxidase inhibitor and Serotonin reuptake inhibitors available in
the market have more adverse effects. The identification of good target to treat depression or
in other words make good availability of serotonin for neurotransmission can be possible. A
much fine target enzyme could be identified by using the flux balance approach of modeling
metabolic pathways.
 REFERENCES

Start from 1
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Over All: Check the figure and table nos. and refer them in proper place where you are
describing those. Result part has to be re-written properly with implications.

Conclusion should contain the overall implication of the results with proper references.

In introduction and other part, where you are giving any definition or description put
reference?

If possible give a table of all the reactions involved in the Tryp. Met. Network after the figure
of the network.

Abreviations List

Certificate

Prepare this today and copy it in my desktop, so that tomorrow morning onwards I can start
correcting the details.

And please…..check the formatting