Recipient Organization
MISSISSIPPI STATE UNIV
(N/A)
MISSISSIPPI STATE,MS 39762
Performing Department
LSBI / IGBB
Non Technical Summary
Understanding protein interactions involved in cellular signaling is one of the challenges of the post-genomic era. Much current research targets the complex structure and dynamics of signaling networks, in which diverse signals activate the same cascade, or where multiple, parallel cascades are activated by the same stimulus. The identification and characterization of signaling pathways and the integration of publicly available data sets across multiple species would greatly improve our knowledge and lead to the formulation of new, testable research hypotheses. Given the rapid accumulation of new genomic data, new methods that translate pathway structure, signaling dynamics and molecular mechanisms in plants would have a tremendous impact in the field. Among these novel methods, systems modeling is an essential investigative tool for studying cellular mechanisms. Here we propose the use of systems biology methods to investigate structure and dynamics of signaling networks. We plan to develop systems models of biological networks that capture functional as well as structural molecular information. The proposed research focuses on modeling network dynamics and on creating new inference methods for the identification of signaling networks. We will use stochastic simulation to identify the structure and functionality of signaling networks, study signaling network regulators and perform functional analysis of network regulatory motifs. Modeling the evolution process of signaling networks will further help us understand their structure and dynamics and provide the basis for functional analysis of signaling pathways. The overall goal is to generate one or more comprehensive models of the structure and dynamics of signaling networks.
Animal Health Component
33%
Research Effort Categories
Basic
33%
Applied
33%
Developmental
34%
Goals / Objectives
The identification of signaling pathways and the integration of transcriptomics and proteomics data sets across multiple plant species could greatly improve our knowledge of cellular function and dynamics. In this context, our proposed research focuses on new approaches to study signaling networks in order to understand their structure and evolution at the molecular and cellular levels. The specific aims of our proposal are to:1) integrate transcriptome and proteome analyses in order to study stress responses circuits;2) construct systems models for stress signaling;3) perform system analysis of the stress response models; and4) analyze the evolution of plant signaling networks.We will identify signaling networks and characterize their dynamics in cultivars and under various types of stresses. The resulting data will create a knowledgebase for molecular breeding and genetic engineering of crops, which will be the basis for creating new cultivars with enhanced stress tolerance.
Project Methods
Systems biology analyses:- construct systems models for signal transduction in plant stress responses.- analyze systems biology models of stress response:1) a detailed stress response systems biology model (SBM) that will include genes, mRNA transcripts, proteins, peptides, metabolites, as well as all identified interaction pathways;2) a stress response functional model (SRFM) that will summarize the stress response logic and the key components and pathways;3) a stress response interactive model (SRIM) that can be used to visualize the stress response and to assess the effects of systems changes.- construct models to simulate the systemic acquired resistance of plants in response to stress.- analyze systemic acquired resistance models in plants.Computational biology analyses:- develop methods to analyze stress responses circuits in plants- merge and analyze transcriptomics and proteomics datasets to infer stress response pathways.- develop methods to analyze transcriptome-proteome relationships in stress responses of plants.- mine functionally relevant redox signaling pathways and components in SAR responses.- develop methods to analyze of the evolution of plant signaling networks- compare and characterize signaling network alignment in multiple plant speciesBioinformatics tools development:- develop bioinformatics tools to infer stress response pathways from transcriptomics and proteomics datasets.- develop bioinformatics tools to simulate and analyze of the stress response models- develop bioinformatics tools to analyze of the evolution of plant signaling networks- design relational databases for signaling networks annotations.The algorithms will be developed in R and Python and software packages will be made available in the CRAN and Github developing network repositories. We will perform all simulations and analyses using the bioinformatics tools available at IGBB/HPC2 (new analysis packages will be installed as needed).