PubMed Health⌕ Search

Biomedical subjects

Jeff Shrager

Publications and source records attributed to Jeff Shrager.

9 recordsLinked to original sources

Constructing explanatory process models from biological data and knowledge.

OBJECTIVE: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS: We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. RESULTS: We demonstrate IPM's use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. CONCLUSION: We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

Algorithms↗

Generation of an oligonucleotide array for analysis of gene expression in Chlamydomonas reinhardtii.

The availability of genome sequences makes it possible to develop microarrays that can be used for profiling gene expression over developmental time, as organisms respond to environmental challenges, and for comparison between wild-type and mutant strains under various conditions. The desired characteristics of microarrays (intense signals, hybridization specificity and extensive coverage of the transcriptome) were not fully met by the previous Chlamydomonas reinhardtii microarray: probes derived from cDNA sequences (approximately 300 bp) were prone to some nonspecific cross-hybridization and coverage of the transcriptome was only approximately 20%. The near completion of the C. reinhardtii nuclear genome sequence and the availability of extensive cDNA information have made it feasible to improve upon these aspects. After developing a protocol for selecting a high-quality unigene set representing all known expressed sequences, oligonucleotides were designed and a microarray with approximately 10,000 unique array elements (approximately 70 bp) covering 87% of the known transcriptome was developed. This microarray will enable researchers to generate a global view of gene expression in C. reinhardtii. Furthermore, the detailed description of the protocol for selecting a unigene set and the design of oligonucleotides may be of interest for laboratories interested in developing microarrays for organisms whose genome sequences are not yet completed (but are nearing completion).

Animals↗

BioLingua: a programmable knowledge environment for biologists.

UNLABELLED: BioLingua is an interactive, web-based programming environment that enables biologists to analyze biological systems by combining knowledge and data through direct end-user programming. BioLingua embeds a mature symbolic programming language in a frame-based knowledge environment, integrating genomic and pathway knowledge about a class of similar organisms. The BioLingua language provides interfaces to numerous state-of-the-art bioinformatic tools, making these available as an integrated package through the novel use of web-based programmability and an integrated Wiki-based community code and data store. The pilot instantiation of BioLingua, which has been developed in collaboration with several cyanobacteriologists, integrates knowledge about a subset of cyanobacteria with the Gene Ontology, KEGG and BioCyc knowledge bases. We introduce the BioLingua concept, architecture and language, and give several examples of its use in complex analyses. AVAILABILITY: Extensive documentation is available online at http://nostoc.stanford.edu/Docs/index.html CONTACT: JShrager@Stanford.edu

Artificial Intelligence↗

Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray analysis of gene expression.

Responses of photosynthetic organisms to sulfur starvation include (i) increasing the capacity of the cell for transporting and/or assimilating exogenous sulfate, (ii) restructuring cellular features to conserve sulfur resources, and (iii) modulating metabolic processes and rates of cell growth and division. We used microarray analyses to obtain a genome-level view of changes in mRNA abundances in the green alga Chlamydomonas reinhardtii during sulfur starvation. The work confirms and extends upon previous findings showing that sulfur deprivation elicits changes in levels of transcripts for proteins that help scavenge sulfate and economize on the use of sulfur resources. Changes in levels of transcripts encoding members of the light-harvesting polypeptide family, such as LhcSR2, suggest restructuring of the photosynthetic apparatus during sulfur deprivation. There are also significant changes in levels of transcripts encoding enzymes involved in metabolic processes (e.g., carbon metabolism), intracellular proteolysis, and the amelioration of oxidative damage; a marked and sustained increase in mRNAs for a putative vanadium chloroperoxidase and a peroxiredoxin may help prolong survival of C. reinhardtii during sulfur deprivation. Furthermore, many of the sulfur stress-regulated transcripts (encoding polypeptides associated with sulfate uptake and assimilation, oxidative stress, and photosynthetic function) are not properly regulated in the sac1 mutant of C. reinhardtii, a strain that dies much more rapidly than parental cells during sulfur deprivation. Interestingly, sulfur stress elicits dramatic changes in levels of transcripts encoding putative chloroplast-localized chaperones in the sac1 mutant but not in the parental strain. These results suggest various strategies used by photosynthetic organisms during acclimation to nutrient-limited growth.

Animals↗

Chlamydomonas reinhardtii genome project. A guide to the generation and use of the cDNA information.

The National Science Foundation-funded Chlamydomonas reinhardtii genome project involves (a) construction and sequencing of cDNAs isolated from cells exposed to various environmental conditions, (b) construction of a high-density cDNA microarray, (c) generation of genomic contigs that are nucleated around specific physical and genetic markers, (d) generation of a complete chloroplast genome sequence and analyses of chloroplast gene expression, and (e) the creation of a Web-based resource that allows for easy access of the information in a format that can be readily queried. Phases of the project performed by the groups at the Carnegie Institution and Duke University involve the generation of normalized cDNA libraries, sequencing of cDNAs, analysis and assembly of these sequences to generate contigs and a set of predicted unique genes, and the use of this information to construct a high-density DNA microarray. In this paper, we discuss techniques involved in obtaining cDNA end-sequence information and the ways in which this information is assembled and analyzed. Descriptions of protocols for preparing cDNA libraries, assembling cDNA sequences and annotating the sequence information are provided (the reader is directed to Web sites for more detailed descriptions of these methods). We also discuss preliminary results in which the different cDNA libraries are used to identify genes that are potentially differentially expressed.

Animals↗

Guiding revision of regulatory models with expression data.

BioLingua is a computational system designed to support biologists' efforts to construct models, make predictions, and interpret data. In this paper, we focus on the specific task of revising an initial model of gene regulation based on expression levels from gene microarrays. We describe BioLingua's formalism for representing process models, its method for predicting qualitative correlations from such models, and its use of data to constrain search through the space of revised models. We also report experimental results on revising a model of photosynthetic regulation in Cyanobacteria to better fit expression data for both wild and mutant strains, along with model mutilation studies designed to test our method's robustness. In closing, we discuss related work on representing, discovering, and revising biological models, after which we propose some directions for future research.

Computational Biology↗

Dynamic Plasticity Influences the Emergence of Function in a Simple Cortical Array.

In computational experiments with a simplified cortical array we investigated the factors that give rise to the functional organization of the cerebral cortex during brain development. We show that a dynamical spatial modulation of plasticity in the substrate (i.e., a "wave of plasticity") induces higher functional development in the later-developing parts of the cortical array. This result suggests an account of the role that changes in developmental timing may have in the development of different cortical structures. Copyri

Journal Article↗