Aim 2: Inference of regulatory networks: Develop a time series expression methodology using multiple data sources to infer regulatory networks. 

Rationale: A principal aim of the biological enterprise is to learn which genes affect which functionalities and how. Whereas it is possible to create models and draw networks based on correlation and other data-driven relationships, the ultimate test of any model is the ability to predict the values of assays (e.g. expression levels) on conditions that were not used in training. If the model is in the form of a causal network, it can be used to suggest gene modifications that may optimize the performance of a species to some useful end. Thus, our goal is to generate networks that show predictive power. Because causality moves forward in time, time series experiments are a particularly promising source of data. In this aim, we describe our methodology in some detail, explain prior results, and explain how we integrate the methodology with other existing information (e.g. validated transcription factor binding sites, structurally based contact-binding sites, and expression data).	

Predicting Arabidopsis regulatory networks using time series data and “State Space” analysis a machine learning approach. The goal of our Arabidopsis time-series work was to uncover gene relationships and consequently to build core regulatory networks involved in Arabidopsis root adaptation to NO3- provision [19]. The experimental approach was to monitor transcriptome responses to NO3- treatment at 0, 3, 6, 9, 12, 15 and 20 min, using ATH1 chips. This high-resolution time course analysis demonstrated that the previously known “primary nitrate response” of 20 min (e.g. genes in the nitrate reduction pathway) [11], is

Fig. 4. State-Space modeling: A machine learning approach to network inference. State-space modeling fueled by regulatory data (transcriptome depicted as heat map) at closely spaced time points seeks to explain the expression of a target gene X, as a function of the expression of one or more other regulatory genes (e.g. transcription factors, TFs) as a fixed relationship (f) between genes. Even though f is fixed, gene expressions can vary in value because their input genes (e.g. TFs) as well as signals to which they respond (e.g. nitrogen signals) can change over time. The function f is "simple" (or "regularized") in the sense that each target gene in the model is forced to depend on no more than three or four input TFs. Function f is computed through a cyclic series of steps of the form guess, compute error, and then refine f, using the time-series expression data [19, 56].

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actually preceded by very fast (within 3 min) regulation of genes including TFs [19]. In order to build a regulatory network that could predict these TF-target interactions, we used a machine learning method, “State-Space” modeling to generate predictions for regulatory networks [56]. The State-Space model synthesizes Bayesian and Markovian approaches (in which each gene’s expression value at a time t is assumed to depend directly only on the state of potentially all the genes at the previous time point and indirectly on values from previous time points) and entails the construction of a “trellis” indicating causalities, as shown in Fig. 5B [42, 56]. In the spirit of the close collaboration between biologists and computer scientists in this project, we depict the state space model from a biologist’s view (Fig. 4) and a computer scientist point of view (Fig. 5).
In the “State Space” model depicted in Fig. 5B, each node represents the values of all gene expressions at a particular time point. Typical values of all gene expressions are depicted as a heat map in Fig. 4. The goal of this approach, is to learn the function that determines the change in expression of a target gene zj, as a linear (or if needed non-linear) combination of the expression of a relatively small number of transcription factors (typically up to three or four) (Fig. 4). As applied to our problem, the set of all genes at time t is modeled by a “latent” (i.e. hidden) variable (denoted Z(t)) from which noisy and sometimes missing observations Y(t) are made. Latent variables are represented by large red circles, and observed variables by large black circles in Fig. 5B. The relationship between latent and observed variables is the identity function h with added Gaussian noise (represented by a black square in Fig. 5B). An unknown function f (represented by a red square in Fig. 5B) relates the values of latent variables Z(t) and Z(t+1) (for all t) corresponding to consecutive time measurements as a Markov chain. The dynamical function f factors in both transcription factors and their target genes (e.g. other TFs or target genes), as shown in Fig. 4. The “State Space” learning algorithm iteratively infers the function of latent values of transcription factors that determines the changes to target genes. Learning the function f corresponds to finding parameters of f that minimize the prediction error while penalizing functions that are excessively complex (i.e. require many transcription factors to determine the change in expression of a target).

An iterative procedure tries to learn the dynamical relationship between latent gene expression variables z(t) while maintaining the latent variables z(t) as close as possible to the observed Affimetrix measures y(t). The algorithm consists in a) minimizing the sum of quadratic errors of the dynamical and the observation models with respect to the latent variables Z by using gradient descent on the latent variables [25] (this is the inference step); and in b) minimizing the sum of quadratic errors of the dynamical model using conjugate gradient, LARS [41] or Elastic Nets [42] optimization on the parameters of F (this is the learning step). During the learning step, sparse gene regulation networks are obtained by penalizing dense solutions using L1-norm regularization, which amounts to adding a λ-weighted penalty to the dynamical error term, as in the LASSO initially described by Tibshirani [43]. Employing regularization on parameters of F also helps avoiding local optima in the solutions. The learning algorithm is run for 100 consecutive epochs over all the replicate sequences (4 replicate sequences here). In order to retain the optimal set of parameters of f, one selects the epoch where the dynamic error on the training dataset is minimal. The cross-validation data are unseen during the optimization procedure. One run of the learning procedure provides with a matrix F of signed (positive: excitatory or negative: inhibitory) interactions between transcription factors and genes. Each element Fi,j represents the action of the jth transcription factor on the ith gene. This algorithm starts with random initial values for both the dynamical model (in other words, matrix F) and for the latent variables Z. We repeat the whole procedure 20 times in order to find the average TF-gene interactions weights. We discuss variations of this approach later.  Suzan: references in this paper are from the Gab paper.

Fig. 5. Prediction and Validation Using a Network Constructed from State-Space Modeling. In the state space model depicted in (A), each node represents the values of all gene expressions at a particular time point. The set of all gene expressions at time t is modeled by a “latent” (i.e., hidden) variable (denoted Z(t)) from which noisy and sometimes missing observations Y(t) are made. An unknown function f (represented by red square) relates the values of latent variables Z(t) and Z(t+1) (for all t) corresponding to consecutive time measurements. In (B) Validation of the predictive modeling is tested by the ability to accurate predicting the direction of change of each gene at 20 min for state space modeling (B) vs. trend-forecast test (C) [19, 56].

To test the ability of the “State Space” approach to generate a predictive regulatory network, we built a regulatory network using the Arabidopsis time-series data up to 15 minutes (training set: 0, 3, 6, 9, 12, 15 min) and used the resulting network to predict the direction of gene change (up regulation or down regulation) from 15 min to 20 min (Fig. 5). Our State Space predictions of gene regulation were correct for 74% of the genes (Fig. 5B). As a basis for comparison, the "naive trend forecast" that predicted the
direction of change from 15 to 20 min to be in the same direction as the movement from 12 to 15 min, was correct for only 52% of the genes, just slightly better than random (Fig. 5C). Thus, our “State-Space” model does significantly better (p value= 0.006) than the “naive trend forecast” test based on a binomial test on a coin that is biased to be correct 51.9% of the time. This “State Space” model can also be

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used to predict the “23most influential TFs” in the network (e.g. the one that is predicted to influences the most genes in the network), and to generate a time-dependent regulatory network model for the control of N-assimilatory pathway genes.
	When compared with other approaches, our method showed a slight improvement in accuracy. Specifically, when compared with the 1) kinetic ODE with LARS optimization, as in Bonneau et al. [31, 32] , 2) 1st-order vector-autoregressive model with Elastic Nets optimization, as in Shimamura et al. [34], and 3) “Brownian motion” ODE, without mRNA degradation, similar in principle to Wang et al. [33], our state space approach tied in high accuracy compared to Bonneau et al. and Shimamura et al. but with better signal to noise ratio. Further we made a great effort to reduce the importance of initial parameters by using random starting point and bootstrapping, thus offering a principled way to deal with uncertainty and avoid over-fitting in micro-array measurements. Further, our method easily allows the addition of “hints” in the form of known transcription factor-binding relationships. When this was done …. Suzan: the references here are from the Gab paper

Our goal is to help other groups design and analyze such experiments. This entails following a workflow:
1.	find sentinel genes for the given treatment. 
2.	Test them at a variety of times
3.	Decide on the times of global expression experiments
4.	Do enough experiments
5.	Try a leave-out-one test. If resulting network gives good enough predictions, then use it.
Creating a state-space regulatory network for Rice. We will use a similar time series-based approach to examine nitrogen regulatory networks in Rice. For this, we will use similar growth and N-treatments in Rice that we have used in Arabidopsis, and for which we have shown Nitrogen-regulation of gene expression in Rice. Rice seedlings (approximately 14 days old) will be grown in nitrate-free sterile hydroponic media containing 0.5 mM NH4+ succinate. Plants will subsequently be N-deprived for 24 hours, then treated with 1 mM KNO3- and then shoot and root tissue will be harvested at 0, 3,6, 9, 12, 15, 20, 25, 35, 45, 60, 120, 180 and 240 min. Controls are "T0" (harvest time zero, before treatment) and 1 mM KCl at each of these time-points. To select time-points for transcriptome analysis, "sentinel" NO3- regulated genes involved in early steps of the nitrate response (e.g. nitrate uptake or reduction) will be monitored by Q-PCR [11, 43, 44]. Based on these Q-PCR results, an initial set of RNA time points will be analyzed using transcriptomics in biological duplicates. We will prepare mRNA samples for RNA-seq (Illumina) using protocols we have previously used for Arabidopsis. Briefly, polyA mRNA will be sheared and sized to ~50-60 bp and ligated to adaptors at 5’ and 3’ ends for reverse transcription and PCR amplification. 14 cycles will be used for enrichment, to minimize PCR bias, but produce enough material for Illumina sequencing (see McCombie letter, CHSL).
Step 1. Identify the “regulated" genes in the target species (Rice). To find the genes that are N-regulated in Rice in our time-series data, we will use criteria similar to those used for Arabidopsis to minimize the False Discovery Rate (FDR). As we did for Arabidopsis time-series data [19], we will run an ANOVA (aov() function) over the data set where the signal of a probe i is Pi ~ µ + αN +βT+ γT*N + ε where N is the effect of the Nitrate treatment, T is the effect of time, and T*N is the effect of their interaction, µ is the mean signal over the data set, and ε the unexplained variance. We determine for each gene the particular time point that showed the most marked effect of nitrate. We call a probe “regulated” if it has a positive call (FDR <5%) of interaction with nitrate at that particular time point. This will lead us to the identification of a nitrogen-regulated network having regulators such as transcription factors (TFs) and targets of regulation (all genes – both targets and TFs).
Step 2. Find inferred regulatory and homology-supported correlation edges between Arabidopsis and Rice. As a starting point for further analysis, we will next find edges between Rice genes (e.g. R1.1, R2.2) having strong Pearson correlation, where Rice gene R1.1 is a transcription factor, for which binding site over-representation also gives support [7, 8], and such that homologous genes in Arabidopsis (A1, A2) also comprise a putative edge in the transcription factor network generated from the time series data. We seek Pearson correlation between Rice genes having p-value < 0.01 (that is a 0.01 chance that the correlation is due to chance). We will use these inferred TFTargetTarget regulatory edges among Rice genes to “prime the pump” in the “State-Space Modeling” analysis of the next step.
Step 3. Infer the regulatory network using “State Space” analysis. Using the homology-aided correlation edges from Step 2, we set the initial weights of the TFargettarget pairs for the State-Space modeling approach [56]. Specifically, gene pairs that are positively or negatively correlated in the analysis of Step 2, will have large (positive or negative, respectively) weights. As State-Space modeling is an iterative algorithm, those weights will be adjusted in the course of error reduction. Better initial edge weights can improve performance. For example, in our Arabidopsis study, the knowledge that certain genes were transcription factors improved the prediction accuracy of State Space analysis by 5% (from 67% to nearly 71%) (Fig. 5B) [19].
Step 4. Validate the inferred Rice regulatory network and plan new experiments. Validation here will mean prediction of Rice regulatory networks under untested conditions, and validation of these predictions using experimental data. Just as we did for Arabidopsis, we will build a network with the Rice time series transcriptome data, but leaving out certain time points, and then predict the values at those

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missing time points and compare the predicted values with the actual ones. If the network predictions are not statistically significantly better than random based on p-value analysis, we will analyze more time-series experiments. To decide which new time series data points to use, we will make use of the following simple heuristic: perform a new measurement of a sample that is most similar to the most useful measurement just done as judged by prediction accuracy. For example, we can determine which of the existing samples are most valuable by removing them and computing prediction accuracy. In the case of our Arabidopsis time-course study [19], removing two replicate experiments from two different time-points prior to 15 minutes is less harmful to the accuracy of the prediction of the network state at 20 min than removing both replicates from a single time-point prior to 15 min. This suggests that measurements at different time-points are more valuable than replicates.
Expected outcomes of Aim 2: The results of this aim will generate an inferred nitrogen regulatory network for Rice that will identify the “most influential” transcription factors affecting the entire N-response “system”. From this, we can identify a core set of conserved regulatory networks (e.g. conserved between Rice and Arabidopsis). If that core set is large, Arabidopsis could be used as a true reference species to test the role of these TFs in nitrogen-use efficiency (using T-DNA mutants and overexpressors), followed by translational applied studies in Rice. That is, conserved factors shown to affect N-responses in Arabidopsis will set the stage for future translational studies in mutant Rice available from the Oryza Tag Line collection (http://urgi.versailles.inra.fr/OryzaTagLine/).
Timeline:
Year 1: Aim 1. Extend cross species network inference using ground truth protein:protein and metabolic interaction networks for Rice and Arabidopsis to other homology methods. Extend network inference analysis beyond protein-protein interaction to validated regulatory (AGRIS) edges. Aim 2 and 3: Perform the time series experiments in Rice and Medicago to complete the Arabidopsis series and generate the RNA-seq data. Aim 4: Build and/or assemble high-performance network inference software for time series data (e.g. parallelize State-Space modeling) and compare it with others (especially other Bayesian methods [50, 51]). Assemble “ground truth” networks in the 3-5 target crop species beginning with Medicago, Corn, Grape. Select bioinformatic workflow platform on which we will deploy.
Years 2-3: Aim 2 and 3. Apply the State Space analysis to Medicago and Rice and test targeted regulatory genes in N-use pathway. Aim 4. Deploy the first version of the CSNI analysis pipeline for cross species network inference to collaborators (D. Cook, U Davis; R. Gutierrez, Chile).
Years 4-5: Apply the computational pipeline to infer networks in several crop species for example corn and grape. Deploy the full computational CSNI pipeline for cross-species network inference to plant community via CSNI (www.CrossSpecies.org) linked to VirtualPlant, iPlant and a selected workflow platform (e.g. Galaxy).

FACILITIES, EQUIPMENT AND OTHER RESOURCES
NEW YORK UNIVERSITY
NYU BIOLOGY CENTER FOR GENOMICS & SYSTEMS BIOLOGY
CORUZZI Laboratory:
The co-PI (Coruzzi, NYU) has approximately 1000 sq. ft. of laboratory space in an open plan lab, with bench and desk space for 15+ researchers within the Center for Genomics and Systems Biology’s 16,000 sq. ft of shared lab and facilities space. Separate areas are provided for a conference room, tissue culture room, cold room, dark room, and an autoclave and dishwashing facility. In addition, the laboratory contains bench model freezers, single-door cold boxes as well as two tissue culture hoods and two fume hoods.
Office: A private office of 150 sq. ft. is provided for the co-PI adjacent to the lab. The office is furnished with a computer, printer, scanner and fax machine.
Equipment: The CORUZZI lab is well equipped to perform the proposed research. Equipment includes a Sorval RC-5B super speed centrifuge, a Beckman L-80M Ultracentrifuge, NanoDrop ND-1000 Spectrophotometer, New Brunswick Scientific Floor Incubator/Shaker, two Revco -80° freezers, Gel Logic 200 Imaging System, Sevant ISS 110 Speedvac Concentrator, Biorad Tetrad 2 thermocycler, Perkin Elmer 9600 thermocycler, Qiagen TissueLyser, water purification system as well as several bench top centrifuges, shakers, hybridization ovens, heat blocks, and other minor equipment. The lab is also fully equipped for Chromatin ImmunoPrecipitation (ChIP) experiments including a Diagenode Sonicator, Invitrogen Magnetic Particle Concentrator, and the necessary electrophoresis system.
Genome Core Facility: The Core Facility includes an Imaging Facility run by Dr. Ignatius Tan that currently includes state of the art microscopes including a Leica Spectral Confocal Microscope and Improvision Spinning Disk Confocal system for high through-put imaging. Both confocal systems have separate computer workstations configured to run Leica and Improvision’s Volocity image analysis software. The DNA Facility includes genome expression equipment including an Affymetrix GeneScan Hybridization and Reader system, Agilent 2100 Bioanalyzer, GenePix Personal 4100A microarray scanner from Axon Instruments, a Light Cycler, and several automated DNA sequencers. The Robotics Facility includes an Acquarius multi-channel pipettor with autoloader by Tecan and a Tecan EVO platform configured with shaking incubator, tilting, cooling and heating carriers, a vacuum system, multichannel pipetting head, bar code reader, and the Infinite M200 microplate reader. As a stand-alone or part of the EVO platform, the Infinite M200 reader is fully loaded incubating, shaking reader with (top/bottom read) fluorescence, luminometer, and excitation and emission monochromator options. The Cellular & Phenotyping Facility consists of the BD FACS Aria a high speed cell sorter with two laser lines (488, 633 nm) capable of automated dispensing into a 96 well format and sterile environment. Bacterial samples can be sorted through a forward scattering PMT.
Other:
Twelve 8x10 ft. walk-in plant growth chambers and five reach in Percival Growth Chambers are located in the basement and are accessed by private elevator.
BIOINFORMATICS: The Coruzzi laboratory is equipped with two Apple G4s, two Apple G5, two Dell PCs with Pentium IV processors and one HP Omnibook 6000 with a Pentium II processor (used to run the Roche LightCycler Quantitative PCR instrument and the NanoDrop spectrophotometer). The VirtualPlant team uses one 4 code and one 8 core Mac Pro, each with 4GB of memory as workstations. The

VirtualPlant web server, database server, and development server are IBM e326 servers with 4 cores and 4GB of RAM each. In addition, a cluster has been purchased from Silicon Mechanics comprised of 8 nodes, each with 8 cores and 32GB of RAM with a 5 Terabyte file system. The purpose of this cluster is to support the computationally intense calculations related to the VirtualPlant project.
GENOME CENTER Bioinformatics Suites and Cluster: The Bioinformatics suites in the Center for Genomics and Systems Biology contain approximately 30 workstations and associated computers. The computer cluster that serves these bioinformatics stations is presently comprised of 26 computational nodes, each with at least 4GB RAM. In addition to many essential software programs, the cluster uses the Sun Grid Engine for load balancing and process distribution among 34 Opteron 242 2.2 GHz processors. Disk storage available is currently over 10 terabytes. The Systems Administrator for the Bioinformatics cluster at NYU's Center for Genomics and Systems Biology is John Bako.
Dennis Shasha of the Courant Institute brings to this project the resources associated with the Computer Science department including a network of several hundred desktop workstations, file servers, multi-processor computational servers and clusters, and state-of-the-art visualization facilities, all supported by a central server infrastructure. The Courant Institute also has access to the Academic Computing Services, a unit of the NYU-wide Information Technology Services offering an additional wide range of computational resources in support of research and instruction. This includes a 4.5 TeraFlops supercomputer, consisting of a cluster of 256 IBM eServer BladeCenter JS20 dual-processor nodes and using Myrinet switch technology. It is the first supercomputer in the United States that is using IPv6, the next generation Internet Protocol.

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(A-1) Sharing of Results and Management of Intellectual Property
Results Sharing
1.	Data Distribution: In this NSF Plant Genome project, we will generate deep-transcriptomic (Illumina) time-series data for N-treatment of two crop species, Rice and Medicago. This project proposes to generate short read sequences for transcriptome analysis. A typical sequencer run will generate several hundred gigabytes of data. Once NCBI fully implements the equivalent of a trace archive for short read sequences, we will deposit our sequence data into that database. Once we have finished aligning the reads to the respective genome sequence, the alignment information will be stored in a database and made available to the public using the Gbrowse (www.gbrowse.org) interface. Normalized expression values from these experiments will be made available as supplemental data in the journal where it is accepted.
2.	Informatic Resources & Software: The informatic analysis pipelines for Cross Species Network Inference (CSNI), discussed in Aim 4 will be made available to the community free of charge. We will implement CSNI as a general-purpose tool to be used by plant scientists. We plan to deploy CSNI as a website (www.crossspecies.org) linked to several additional platforms, first to VirtualPlant website (www.virtualplant.org), and second to iPlant (see S. Goff letter). We will also deploy CSNI on one of the widely-used bioinformatic workflow engines: Taverna [2], Kepler [3] or Galaxy [4]. Implementing the CSNI pipeline on top of one (or more) of these bioinformatic workflow engines is important because they provide increasingly popular platforms for developing computational genetic analyses, and provide generic support for reproducible bioinformatic analyses.
Publications: The results of our analysis of the data we generate will be made available through peer- reviewed literature as it is the most appropriate way to make this information available.
Intellectual Property
1.	Invention Disclosure and Patent Management:
Invention Disclosures Invention disclosures will be reported by the inventors to the office responsible for patenting and licensing at their institutions. At NYU, this is the Office of Industrial Liaison. Invention disclosure forms are available on the Office of Industrial Liaison website (www.nyu.edu/oil) under “New Invention Disclosure Form.” The principal investigator at each institution will be responsible for ensuring that inventions are disclosed to their institution’s patenting and licensing office sufficiently prior to public disclosure to allow the office to evaluate patentability and commercial potential, and to have a patent application filed if appropriate. The offices at institutions other than NYU will notify the NYU Office of Industrial Liaison of any inventions made at their institutions.
Patenting The institution at which the inventor is employed will be responsible for evaluating whether to retain title to inventions, filing U.S. and/or foreign patent applications, and notifying NSF. Inventions will be disclosed to the funding agency promptly upon receipt. Decisions on whether to file a patent application will be based upon an evaluation of the commercial potential of the invention by the institutional patenting and licensing office, and an evaluation of the patentability of the invention (including a search of prior art) in conjunction with an outside patent law firm. It is expected that the first patent filing will typically be a provisional filing. Decisions on whether to file non-provisional U.S. and or PCT applications will be made in the 10-11 month timeframe following the first (provisional) filing.
Patent Reporting NYU participates in the Interagency Edison System. NYU will notify the funding agency and grant the required non-exclusive license for government purposes for inventions for which NYU elects title. A final invention statement will be sent to NSF upon completion of the grant.
2.	Licensing and Commercialization: The Office of Industrial Liaison at NYU shall be responsible for

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seeking to make technology developed under the grant at NYU commercially available. Technology developed at the other participating institutions will be licensed by the technology licensing offices of those institutions. Research materials developed under the grant will be made available to researchers at not-for-profit institutions under a material transfer agreement with intellectual property terms consistent with NIH guidelines. The Office will also seek out appropriate industry partners to commercially develop technology. Potential arrangements will include exclusive or non-exclusive licenses. Any licenses will contain provisions requiring the licensing company to diligently develop the technology. Options may also be granted to companies considering licensing for limited time periods (e.g., 3-6 months) to allow them to conduct due diligence and evaluate their interest in a license. It is expected that technology will be licensed to existing companies, but if the technology is of sufficient breadth to justify the creation of a new start-up company, this will be considered.
3. Inter-Institutional Agreement: As described above, inventions made solely by one institution shall be managed by that institution. For inventions made by more than one institution, an inter-institutional agreement shall be agreed upon, under which the parties shall agree on which institution shall take the lead in managing patenting and licensing activities, and on sharing patent expenses and license revenues based on relative contributions. License agreements shall require the approval of all institutions with an ownership interest in the licensed technology.

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(A-2) Management Plan
Overview: Our proposal is a result of a highly successful collaboration between computer scientists at NYU Courant Institute of Mathematical Sciences and biologists at NYU Biology’s Center for Genomics and Systems Biology. Our close proximity and ongoing successful working relationships during the “VitrualPlant” software development NSF project (DBI DBI-0445666) to enable Systems Biology analysis in Arabidopsis encourages us to further develop and expand the project to crop species. In this proposed NSF Plant Genome Grant, we will generate the data (transcriptomic) for several crop species (Rice and Medicago) as proof-of-principle, as well as informatic tools and resources and pipeline of informatic analysis tools to develop cross species network inference and machine learning approaches to discover genes associated with important economic traits. The diverse background of the participants in this project provides an opportunity to combine the expertise of investigators with backgrounds in plant biology, genomics, bioinformatics and computer science.
A successful management plan is already in place and this plan is described below: To coordinate and facilitate interactions between individuals, Dennis Shasha (NYU Courant) and Gloria Coruzzi (NYU Biology) will serve as Project Managers for computational and experimental aspects respectively. The role of the Project Managers is to oversee the daily operations of the project and insure that the needs and concerns of the participants are addressed on a day-to-day basis between the participants involved. This includes monitoring and facilitating the technical operations both experimental and computational ranging from plant growth and tissue collection, RNA purification and sequencing to software development, testing and implementation. The project manager will also facilitate communication between PIs, post-docs, graduate students and laboratory technicians by scheduling weekly meetings of all participants to manage immediate issues regarding research needs. We will also schedule day-long meetings twice a semester with collaborators on the experimental end (Doug Cook, UC Davis) and on the computational end (Rodrigo Gutierrez, Chile), to do evaluation of work status and long term planning.
Bioinformatics manager: Dr. Manpreet Katari (NYU Biology) will be in charge of the bioinformatics analysis aspects including RNA-seq analysis, databases, and multinetwork generation. He will also serve as a liaison to researchers and technicians at CSHL where Illumina sequence generation of deep-seq libraries and bioinformatic analysis occurs (see letter from McCombie, CSHL). Dr. Katari will facilitate the processing of this data into contigs and expression data for Machine Learning, State Space analysis. To enable efficient information exchange of raw and processed data, a file server has been set up at the NYU to store and distribute data and its analysis among users at NYU Biology and NYU Courant. A secondary server is also established to create daily backups of the primary server in order to insure and protect data. Relevant information is also accessible in a web-based interface for authorized users. Dr. Katari will maintain the web server, database server, and the multinetwork database.
Software development manager: Dr. Arthur Goldberg (NYU Courant) will manage the development of new software analysis tools and pipelines to enable Cross Species Network Inference (CSNI) which will support the different species and inference, and also new pipelines for cross species analysis, especially as they relate to crop species.
Website: We have set up a web site to house the development of Cross Species Network Inference tools and pipelines, which is accessible at: www.CrossSpecies.org
Education & outreach: MS and PhD students involved in this project will be co-mentored by a Biology and Courant faculty advisors, Coruzzi (NYU Biology) and Shasha (NYU Courant). The students will present their work at weekly joint lab meetings. PhD students will also present their work annually in the weekly PhD seminar series hosted by the Biology Department. Computational students will be involved

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in constructing the pipeline and making it perform through the use of parallelization. Such students will also help to develop and test optimization and machine learning algorithms for network inference. Biological students will engage in experimental work including preparation of RNA for Illumina sequencing, as well as the analysis of data using the VirtualPlant platform. Students will be exposed to Genomics and Systems Biology also through a series of courses offered by faculty at NYU’s Center for Genomics and Systems Biology including: G23.1128 Systems Biology; G23.1130 Applied Genomics: Introduction to Bioinformatics & Network Modeling; G23.1127 Bioinformatics & Genomes.
Principal Investigators: The three principal investigators will each commit a minimum of a half-day per week to this project. This will include supervision of personnel, organizational meeting attendance, and intellectual developments and contributions.
Role of senior participants and timeline:
Name	Institution	Role	Aim	
Dennis Shasha
PI	NYU Courant	Project Leader	Oversee Aims 1, 2, 3, 4	
Gloria Coruzzi,
Co-PI	NYU Biology	Co-leader: Experimental	Oversee Aims 2 & 3	
Manpreet Katari
Co-PI	NYU Biology	Bioinformatics Manager	Aims 1 & 4	
Arthur Goldberg
Senior Personnel	NYU Courant	Software developer	Aims 1 &4	
AmyMarshall-Colon
Post-Doc	NYU Biology	Experimental Treatments
RNA processing for Seq	Aim 2 Rice experiments	
Doug Cook
Collaborator	UC Davis	Experimental support
Plant treatments	Aim 3
Medicago Experiments
Aims 1 & 4	
Rodrigo Gutierrez
Consultant	U Catolica,
Chile	Assembling “ground
truth” networks for crop		

Timeline:
Year 1: Aim 1. Extend cross species network inference by 1) using ground truth protein:protein and metabolic interaction networks for Rice and Arabidopsis from several different sources, 2) using other tools, methods and databases for defining homology, and 3) incorporating validated regulatory (AGRIS) edges. Aim 2 & 3: Perform the time series experiments in Rice and Medicago and complete the Arabidopsis series by generating RNA-seq data. Aim 4: Build and/or assemble high-performance network inference software for time series data (e.g. parallelize State-Space modeling) and compare it with others (especially other Bayesian methods). Assemble “ground truth” networks in the 3-5 target crop species beginning with Medicago, Corn, Grape. Evaluate different bioinformatic workflow platforms and decide which we will deploy.
Years 2-3: Aim 2 & 3. Apply the state space analysis to Medicago and Rice and test targeted regulatory genes in N-use pathway. Aim 4. Deploy the first version of the analysis pipeline for cross species network inference to collaborators (D. Cook, U Davis; R. Gutierrez, Chile).
Years 4-5: Apply the computational pipeline to infer networks in several crop species for example corn and grape. Deploy the full computational CSNI pipeline for cross-species network inference to plant community via CSNI (www.CrossSpecies.org) linked to VirtualPlant, iPlant and a selected workflow platform (e.g. Galaxy).

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(A-3) Coordination with Outside Groups
Please see attached letters of collaboration:
Doug Cook (UC Davis) Dr. Cook, an expert in the field of Medicago genomics will perform the N-treatment time series and supply tissues for RNA-seq analysis (Aim 3), to fuel the regulatory network inference studies in Medicago.
Rodrigo Gutierrez (U Catolica, Chile) Dr. Gutierrez, the creator of the Arabidopsis multinetwork (Gutierrez et al 2007) will assist in the assembly of multinetworks for crop species including Vitis, Corn and Medicago.
iPlant (see letter from iPlant Project Director, Steve Goff) We will coordinate with iPlant to make our Cross species network inference platform (CSNI) modular, independent and accessible with and compatible with iPlant, and accessible using other annotation analysis platforms such as Galaxy and Taverna. We will also make our currently developed VirtualPlant tools accessible to iPlant, as per letter by (S. Goff).
Richard McCombie (CSHL) Dr. McCombie, will process our time-series RNA seq data using CSHL Genome Sequencing facility using an Illumina sequencing platform.

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(A-5) Postdoctoral Mentoring Plan
Co-mentorship across disciplines and institutions: Post-Docs and students will receive novel cross-disciplinary training across biology & genomics (NYU Biology, Center for Genomics & Systems Biology) and informatics and systems Biology (NYU Courant). The PIs with expertise spanning these disciplines will co-mentor students and post docs individually and also at the weekly meetings where postdocs & students present their results.
Training as Educators: In this project, Post-Docs are paired up with graduate students, undergraduate students, and technicians in the laboratory/at the computer to practice mentoring skills in a research context. Post-docs are also afforded the opportunity to teach in NYU Biology courses where they are mentored by faculty advisors. For example, Dr. Katari, is currently co-teaching an undergraduate course “Introduction to Genomics & Bioinformatics” with a faculty mentor (Kris Gunsalus).
Career Development: Post-Docs receive counseling from their co-mentors and practice presentation skills during regular group-lab meetings, through a special NYU Biology Post-Doc seminar series, and at annual poster sessions at the NYU Biology retreat. Funds are provided for students and Post-Docs to attend at least one meeting each year and are expected to widely disseminate their work.