Identifying Coevolving Partners from Paralogous Gene Families

Many methods have been developed to detect coevolution from aligned sequences. However, all the existing methods require a one-to-one mapping of candidate coevolving partners (nucleotides, amino acids) a priori. When two families of sequences have distinct duplication and loss histories, finding the one-to-one mapping of coevolving partners can be computationally involved. We propose an algorithm to identify the coevolving partners from two families of sequences with distinct phylogenetic trees. The algorithm maps each gene tree to a reference species tree, and builds a joint state of sequence composition and assignments of coevolving partners for each species tree node. By applying dynamic programming on the joint states, the optimal assignments can be identified. Time complexity is quadratic to the size of the species tree, and space complexity is exponential to the maximum number of gene tree nodes mapped to the same species tree node. Analysis on both simulated data and Pfam protein domain sequences demonstrates that the paralog coevolution algorithm picks up the coevolving partners with 60% 88% accuracy. This algorithm extends phylogeny-based coevolutionary models and make them applicable to a wide range of problems such as predicting protein-protein, protein-DNA and DNA-RNA interactions of two distinct families of sequences

A joint model of regulatory and metabolic networks

BACKGROUND: Gene regulation and metabolic reactions are two primary activities of life. Although many works have been dedicated to study each system, the coupling between them is less well understood. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions. RESULTS: We integrate regulatory and metabolic networks by adding links specifying the feedback control from the substrates of metabolic reactions to enzyme gene expressions. We adopt two alternative approaches to build those links: inferring the links between metabolites and transcription factors to fit the data or explicitly encoding the general hypotheses of feedback control as links between metabolites and enzyme expressions. A perturbation data is explained by paths in the joint network if the predicted response along the paths is consistent with the observed response. The consistency requirement for explaining the perturbation data imposes constraints on the attributes in the network such as the functions of links and the activities of paths. We build a probabilistic graphical model over the attributes to specify these constraints, and apply an inference algorithm to identify the attribute values which optimally explain the data. The inferred models allow us to 1) identify the feedback links between metabolites and regulators and their functions, 2) identify the active paths responsible for relaying perturbation effects, 3) computationally test the general hypotheses pertaining to the feedback control of enzyme expressions, 4) evaluate the advantage of an integrated model over separate systems. CONCLUSION: The modeling results provide insight about the mechanisms of the coupling between the two systems and possible "design rules" pertaining to enzyme gene regulation. The model can be used to investigate the less well-probed systems and generate consistent hypotheses and predictions for further validation

Detecting Coevolution in and among Protein Domains

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level

Implementation of a virtual environment system based on geographical information system and environmental models

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 162-165).by Chen-Hsiang Yeang.M.S

Local governments and information systems in rural Thailand

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1998.Includes bibliographical references (p. 125-128).by Chen-Hsiang Yeang.M.C.P

Distributed GIS for Monitoring and Modeling Urban Air Quality

The progress of technology has made the measurement of air quality and the simulation of complex air pollution models both feasible and cost-effective. However, there is a long way to go in terms of facilitating widespread access to the data and models, and linking the monitoring of trace gases with specific urban activities and land use that might be controllable. As part of a NASA-funded project, we are working with scientists and engineers to design and test a distributed GIS infrastructure for studying such "urban respiration" phenomena. Measurements of trace gases within a metropolitan area (from mobile and fixed instruments) are geo-referenced, time-stamped, and stored in a relational database server (Oracle). GIS services (using ArcInfo and ArcView) are connected to the database so that subsets of the trace gas measurements can be extracted and converted on-the-fly into GIS data layers. These subsets (by location, date, and time-of-day) can be displayed and cross-referenced with other layers such as weather conditions, land use and cover, topography, hydrography, demography, and congestion levels of road networks. A web-based interface (using ArcView Internet Map Server) allows research team members at different locations to query, visualize, and process the cross-referenced data layers in order to generate surface level estimates of initial conditions for use in the air quality models.NAS

Validation and refinement of gene-regulatory pathways on a network of physical interactions

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4