1.  Introduction and background
	Overview and significance:  This proposal concerns the development of bioinformatic tools to enable the global study of genes in related biosynthetic pathways.  With the complete genome of an organism, it is possible to identify the genes involved in the biochemical pathways that have been well studied.  The expression of all the genes in an organism, including those in biochemical pathways can be studied using microarray technology.  To my knowledge, nobody has developed a system that allows gene expression data to be observed easily in the context of a whole pathway.  The working example we are using to develop these tools is amino acid biosynthetic pathways in A. thaliana.  The bioinformatic tools we create in this project could be applied to the study of the regulation of any biochemical pathway in any organism.  
Background:  In A. thaliana, it has been shown that the transcription of genes involved in nitrogen assimilation into amino acids are regulated by a complex combination of signals including light, carbon, and nitrogen.  For example, light activates the expression of the GLN2 gene (glutamine synthetase gene) and represses the transcription of the ASN1 gene (asparagine synthetase gene).  This regulatory mechanism allows plants to synthesize glutamine (Gln) when carbon skeletons are abundant, or convert Gln to asparagine (Asn), a more carbon-efficient nitrogen-transport compound, when carbon skeletons are limiting.  This light regulation of gene expression is regulated in part by the phytochrome photoreceptor and in part by light induced changes in carbon metabolites.  Sucrose applied to the media of dark-grown plants can mimic these effects of light; it induces GLN2 expression and represses ASN1 expression in the absence of light.  Amino acids are able to antagonize these effects of sucrose on gene expression.  These studies and others have led to the hypothesis that plants have mechanisms to sense both carbon and nitrogen metabolites and to in turn regulate the expression of genes encoding enzymes in nitrogen and carbon metabolism.  
To explore the interactions of light, carbon, and nitrogen effects on gene expression, the Coruzzi lab has used combinatorial design as a tool to effectively test [TO TEST EACH INPUT’S EFFECT ON GENE REGULATION (you should refer to our paper on the subject] all combinations of inputs on gene regulation.  GLN2 and ASN1 are being used as “sentinel” genes to report on which C:N treatments were effective in gene induction or repression.  Subsequently, selected RNA samples will be used to probe an A. thaliana Affymetrix DNA chip containing ~8,000 genes.  A complete genome chip containing all 25,000 genes is scheduled to be available in early 2002.  The resulting microarray data will be subjected to cluster analysis to determine co-regulated gene sets.  Preliminary clustering analysis of microarray data from nitrate treatments, showed that a cluster of 7 metabolically related genes encoding enzymes involved in nitrogen metabolism or enzymes involved in the biosynthesis of cofactors involved in nitrate reduction and nitrogen metabolism.  This suggests that genes in biochemically related pathways in A. thaliana might be coordinated by metabolic transcriptional control mechanisms.  This is reminiscent of yeast general control mechanism in which amino acid starvation is known to induce genes in all the amino acid biosynthetic pathways.  Herein, we propose to develop bioinformatic tools that will enable us to globally analyze the expression of all the genes involved in amino acid biosynthesis in A. thaliana and to determine whether specific steps in a pathway or in related pathways are subjected to metabolic control of gene transcription.  These experiments allow us to determine how factors such as light, carbon, and nitrogen globally regulate amino acid biosynthetic genes in plants and whether there is a master regulatory system for the biosynthesis of all of the amino acids in plants.  

2. Proposal
Need for a database of biochemical pathways that can be used to query microarray expression datasets:  My goal is to develop bioinformatic tools, which will enable researchers to study the regulation of genes in biochemically related pathways.  As a first step, I propose to construct a database called the "Pathways And Genome Expression Database" (PAGED).  Initially, I will use amino acid biosynthetic pathways as a model to construct this microarray searchable database.  This is the first choice with which to build and test PAGED because the enzymes in these pathways and the connections between them are well studied.  Once developed, PAGED can be expanded to include other biosynthetic pathways including sugar and nucleoside metabolism, as well as others.  I am well aware that pathway databases (KEGG and one through EXPASY) already exist for general metabolic pathways.  However, these are not suitable for our purpose for two reasons: 
1. In some cases, amino acid biosynthesis in A. thaliana is subtlely [CHECK SPELLING]  different than in bacteria and even yeast.  These differences, while subtle, are important and are not contained in those databases. 
2.  More importantly, the existing biochemical pathway databases cannot be used to query microarray expression datasets.
In order to construct PAGED, our input data will be derived from our knowledge of amino acid biosynthesis enzymes and genes in A. thaliana.  The first step is to identify all the steps in each of the 20 amino acid biosynthetic pathways in A. thaliana.  Using published data and the complete sequence of A. thaliana, the data to construct the amino acid biosynthetic pathways of A. thaliana does exist.  However, to my knowledge the genes for all 20 amino acid biosynthetic pathways of A. thaliana have not been gathered in one location.  For some amino acids, like methionine and tryptophan, the pathway has been experimentally determined in A. thaliana.  For others, like phenylalanine and tyrosine, there is very little experimental data.  In such cases, we relied heavily on sequence alignment data to identify putative orthologs in A. thaliana from organisms in which a pathway has been experimentally determined.  In some cases, multiple biosynthetic routes are possible, and I have tried to use sequence alignment data to validate the existence or absence of each biosynthetic route in A. thaliana.  Based on my considerable expertise as a biochemist, and my knowledge of computer-based orthology searches from my Ph.D. studies, I have already begun to identify A. thaliana genes involved in amino acid biosynthesis, and I have reconstructed at least one pathway for the biosynthesis of each amino acid.  It is possible that A. thaliana contains yet unidentified amino acid biosynthetic pathways Therefore, PAGED will be constructed to allow for growth/revisions as more information becomes available. PAGED will also be designed to be queried by a biologist to answer biologically relevant questions, such as “If nutrient starved plants are feed [FED] a carbon source, but not a nitrogen source, how are the genes involved in histidine biosynthesis affected? or What genes for enzymes are affected and in what biochemical pathways are they involved?”

Construction of the Pathway And Genome Expression Database (PAGED):  PAGED construction is being accomplished with PostgreSQL so that queries of microarray expression data like those described above can easily be made with SQL-queries.  Java will be used to create a user interface, so the database will be accessible to any JAVA enabled web browser. 
Overtime, more biosynthetic pathways (e.g. sugar metabolic pathways, which interface with amino acid synthesis) and more microarray expression datasets from more treatment conditions will be added making PAGED more comprehensive.  This will be a first step in creating a "virtual plant", which will allow growth conditions to be tested ahead of time to determine how certain genes will be affected.  To my knowledge no such microarray searchable gene database for biochemical pathways exists for any organism, and PAGED will be a model for others.  Careful design of PAGED is necessary to ensure that it will easily be able to grow and that it will contain information (such as compartmentalization of proteins, equilibrium constants for reactions, and known post-translational regulation of enzymes i.e. inhibitors) that will guarantee that is has uses beyond gene regulation.  To ensure flexibility, we propose to create the pathway database using two different approaches.  In one approach, the "pathway on the fly" approach, we will input all the biochemical reactions and allow the pathways to be created from the database.  In, the second approach we will strictly define each pathway in the database.  Both databases will be constructed and tested for their ability to grow and answer biologically relevant queries.
For the "pathway on the fly" approach, a query asking, “Starting with molecule A what enzymes are needed to make molecule X and how is the transcription of their genes affected under condition Y?” will require transient [THE CONSTRUCTION OF THE TRANSITIVE CLOSURE\ closures to build the pathway.  It will also require that each biochemical reaction be given a unique identifier.  We have considered using Enzyme Commission (E.C.) numbers, however an E.C. number does not take into account how the chemistry occurred just what chemistry occurred.  e.g. There are two known forms of methylmalonyl-CoA decarboxylase, one that uses biotin and one that does not.  The chemistry in each case is different, but they have the same E.C. number (4.1.1.41).  Such information is important to us, because it is known from nitrate treatments of A. thaliana that not only the genes involved in nitrate assimilation were up-regulated, but also the genes involved in making cofactors for those enzymes are up-regulated.  One approach would be to assign each reaction a number, based not only on the overall reaction, but also what cofactors are required for the reaction to occur.  This approach is being investigated and may only require modifying the E.C. number in a few situations.  This approach also requires that naming of molecules be consistent throughout the database.  If multiple names (e.g. -ketoglutarate is also 2-oxoglutarate) are given to the same molecule it will adversely affect the pathway building process. To avoid this problem, each molecule will be given a unique number, such a system is used by DBGET in the Ligand database, and we will investigate using their nomenclature system or one similar to it. 

Table 1:  Definition of biochemical reactions used in PAGED.  The database will be designed to handle cases where a reaction has multiple reactants and products, [THAT’S SUCH A COMMON CASE, I THINK THE UNCAREFUL REVIEWER WIWLL THINK THIS IS JUST A MISTAKE; I WOULD BE MORE COMPLETE IF I WERE YOU] which is not shown in the example below.  From the information below, several pathways could be built by PAGED with several ending and starting points.  So, if a query asked about the biosynthesis of molecule number 80 starting from molecule number 10, a pathway would be built by PAGED by combining reactions 1, 2, and 6 sequentially.
reaction ID	reactants	stoichiometry	products	stoichiometry	
1	10	1	20	1	
2	20	1	50	1	
3	30	1	40	1	
4	10	1	60	1	
5	40	1	70	1	
6	50	1	80	1	
7	40	1	60	1	
 
In order for PAGED to be searchable by enzyme, reactant, and product names, it will require tables to convert the enzyme and molecule names to numbers, which then can be used to search other tables.  Even, our initial design of PAGED will require several tables:
1.  molecule ID, molecule name
2.  reaction ID, reactants (a molecule ID), stoichiometry, 
3.  reaction ID, products (a molecule ID), stochiometry
4.  reaction ID, enzyme name, free energy
5.  gene, enzyme name
6.  Affymetrix ID, gene
7.  experiment, treatments, conditions
8.  experiment, treatment, replica, Affymetrix ID, microarray value
The first five tables are needed to contain the reactions, enzymes, genes and related information to build the pathways.  The sixth table will be needed to join the information in the first four tables to the expression data and the experimental methods, which will be in the seventh and eighth table.
While internally PAGED will use numbers to represent reactions and molecules (Table 1), those numbers will not be part of the output.  Using the tables discussed above, those numbers can be transformed back to molecule and enzyme names.  See Table 2 for a hypothetical output for PAGED.  

Table 2: A hypothetical output for PAGED from a query involving cysteine metabolism.  Some of the information contained in the table is not real, but just used as an example.  The user will be able to control exactly what information is contained in the output.
gene name	enzyme name	reactants	products	reaction free energy (kJ/mol)	fold change	
SIR1	sulfite reductase	sulfite	sulfide	-1.2	+2.1	
SAT1	serine acetyltransferase	serine
acetyl-CoA	O-acetylserine
CoA	-57.8	+8.3	
SAT2	serine acetyltransferase	serine
acetyl-CoA	O-acetylserine
CoA	-57.8	-1.4	
OSL1	O-acetylserine lyase (thiol)	O-acetylserine
sulfide	cysteine
acetate	-23.2	+5.7	

The other approach to build PAGED is to strictly define each [I THINK THE TWO APPROACHES ARE SIMPLY CONFUSING. PRESENT THE ON-THE-FLY APPROACH ON ITS OWN] biochemical pathway and to enter them as a performed set into the database.  This approach would require that a table be built in the database for each pathway.  Using this approach, an enzyme that catalyzes the same reaction in different pathways will be entered into the database multiple times.  For the biosynthetic pathways for the twenty amino acids this is not difficult, but as the database grows it may become more cumbersome.  This method however does avoid transient [TRANSITIVE] closures, and the need for the database to build the pathway.  These aspects, ensure pathways that are known to be biologically relevant.  The tables required for this more rigid set database can be constructed by the "pathway on the fly" database, which will also be a test for its accuracy.
Applications of PAGED to analyze microarray expression sets:  PAGED can help uncover whether amino acid biosynthetic pathways are co-regulated at the transcriptional level or if subsets of amino acids (e.g. aromatic amino acids) are co-regulated under certain conditions.  While it is interesting to know how different amino acid biosynthetic genes are regulated under certain conditions, it is more interesting to know why that regulation occurs.  In some cases, the final product will be the amino acid itself‑ GLN2 is induced to produce Gln, which is used to store and transport organic nitrogen.  In other cases, the final product will be downstream of the amino acid e.g. methionine biosynthesis maybe induced under certain growth treatments so that ethylene production can increase.  PAGED can easily be expanded to include the downstream pathways into which the amino acids feed, which will help determine which other pathways are affected.  To date, several different techniques exist to identify co-regulated genes.  Genes can be clustered for those that are expressed similarly under many different growth conditions, or the promoter regions of genes that are expressed similarly under one condition can be searched for patterns that may constitute a cis-acting element.  PAGED will cooperate well with both techniques because it will be designed to handle expression data from many different conditions, and identify biochemical relationships between genes based on biochemical pathways.
PAGED can also help answer other questions. One question of interest is, what genes must be regulated in order to control flux through a pathway?  Historically, it was believed that organisms control flux through a pathway by regulating a single step in the pathway.  However, metabolic control analysis studies have indicated that changing the activity or amount of a single enzyme would be insufficient to change the overall flux through a multistep pathway.  Since, experimental evidence has been found supporting both views.  While at this stage our analysis does not require the measurements of metabolite concentrations (which is required to strictly determine flux through a pathway), knowing how organisms regulate the genes of proteins involved in a pathway under a variety of conditions will certainly suggest how organisms control flux through a pathway.  In addition, the database is being designed to contain information that will allow it to help study regulation beyond gene control.
Conclusions:
The information and technology exist to construct PAGED, the Pathways And Genome Expression Database.  As expression data for A. thaliana increases, it will be necessary that its storage and analysis be organized in some rational manner. PAGED will allow it to be organized so that it will be easily searchable by biologist to answer biologically relevant questions related to the regulation of pathways.  To my knowledge, no database like this exists for any organism, and PAGED will be a first step in creating a virtual plant.  Such a database will encourage collaborations with other groups interested in metabolic pathways and control of gene expression.
3.  Training Objectives
The fields of computer sciences, biology, and biochemistry are becoming more and more intertwined.  The computer sciences allow the analysis of large amounts data like that created by genomic and microarray studies.  The use of computers to analyze data from microarray and genomic studies allows one to determine what biochemical pathways are in an organism and how they are affected under different conditions.  The knowledge of the regulation of genes will allow the determination of affected pathways, and the final products of the pathways can be determined based on classical biochemical studies.  Such knowledge will drive biology as researchers attempt to understand the effects of the final product of the biochemical pathways in the organism.  It is therefore necessary to be well trained in all three fields to be involved in this process at all the steps.  I already have training in biochemistry, and during this grant, I will receive new training in A. thaliana molecular biology, genomics, and genetics in the Coruzzi lab.  I will also receive new training in computer sciences from the NYU Courant bioinformatics group [AND THROUGH COURSES SELECTED BY MY ADVISORS].  While this training will center around the analysis of gene expression data and database creation, management, and querying, it will introduce me to new ways to use computer sciences to solve problems in the biological sciences.  In my doctoral work, I was struck by the ability of computers to predict the function of proteins based on sequence alignment data.  I think predictions drive science, and therefore making predictions is necessary to be a good scientist.  Computers aid in the process by allowing one to sort through large amounts of data, determine what is useful, and then make a prediction based on that data.  The use of computers to make predictions in the biological sciences is growing and that trend will continue.  I would like to be part of that trend, and this proposal puts me in a position to receive training from three experts (Gloria Coruzzi, Dennis Shasha, and Bud Mishra) in the fields of genomics and bioinformatics.

4.  Choice of Sponsor and Institution
This proposal is the result of a collaboration between the Coruzzi lab in the New York University Biology Department and the Mishra and Shasha group of the Courant Institute at New York University.  The Coruzzi lab is experienced in studying the transcription of genes involved in nitrogen metabolism in plants.  In order to study this problem, they use a wide variety of techniques including reverse and forward genetics and microarray analysis. Dr. Mishra's group at the Courant Institute is a leader in the bioinformatics field and is involved in diverse bioinformatic projects including microarray expression clustering and database management.  Dr. Shasha has been involved in analysis of expression data sets and cis-element analysis.  Several members of both groups are receiving training in biology and computer sciences, which creates a rich environment for me to receive my new bioinformatic training.