Biomedical subjects

Scott C Chapman

Publications and source records attributed to Scott C Chapman.

4 recordsLinked to original sources

PubMed2004

Identification of differentially expressed transcripts from maturing stem of sugarcane by in silico analysis of stem expressed sequence tags and gene expression profiling.

Sugarcane accumulates high concentrations of sucrose in the mature stem and a number of physiological processes on-going in maturing stem tissue both directly and indirectly allow this process. To identify transcripts that are associated with stem maturation, we compared patterns of gene expression in maturing and immature stem tissue by expression profiling and bioinformatic analysis of sets of stem ESTs. This study complements a previous study of gene expression associated directly with sugar metabolism in sugarcane. A survey of sequences derived from stem tissue identified an abundance of several classes of sequence that are associated with fibre biosynthesis in the maturing stem. A combination of EST analyses and microarray hybridization revealed that genes encoding homologues of the dirigent protein, a protein that assists in the stereospecificity of lignin assembly, were the most abundant and most strongly differentially expressed transcripts in maturing stem tissue. There was also evidence of coordinated expression of other categories of fibre biosynthesis and putative defence- and stress-related transcripts in the maturing stem. This study has demonstrated the utility of genomic approaches using large-scale EST acquisition and microarray hybridization techniques to highlight the very significant transcriptional investment the maturing stem of sugarcane has placed in fibre biosynthesis and stress tolerance, in addition to its already well-documented role in sugar accumulation.

Amino Acid Sequence↗

PubMed2004

On systems thinking, systems biology, and the in silico plant.

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

Computer Simulation↗

PubMed2002

Expression profile analysis of the low-oxygen response in Arabidopsis root cultures.

We used DNA microarray technology to identify genes involved in the low-oxygen response of Arabidopsis root cultures. A microarray containing 3500 cDNA clones was screened with cDNA samples taken at various times (0.5, 2, 4, and 20 h) after transfer to low-oxygen conditions. A package of statistical tools identified 210 differentially expressed genes over the four time points. Principal component analysis showed the 0.5-h response to contain a substantially different set of genes from those regulated differentially at the other three time points. The differentially expressed genes included the known anaerobic proteins as well as transcription factors, signal transduction components, and genes that encode enzymes of pathways not known previously to be involved in low-oxygen metabolism. We found that the regulatory regions of genes with a similar expression profile contained similar sequence motifs, suggesting the coordinated transcriptional control of groups of genes by common sets of regulatory factors.

Arabidopsis↗

PubMed2002

The GP problem: quantifying gene-to-phenotype relationships.

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Computer Simulation↗