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Andrew R Joyce

Publications and source records attributed to Andrew R Joyce.

11 recordsLinked to original sources
PubMed2007

Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach.

The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.

Animals↗
PubMed2006

Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains.

Experimental evolution is now frequently applied to many biological systems to achieve desired objectives. To obtain optimized performance for metabolite production, a successful strategy has been recently developed that couples metabolic engineering techniques with laboratory evolution of microorganisms. Previously, we reported the growth characteristics of three lactate-producing, adaptively evolved Escherichia coli mutant strains designed by the OptKnock computational algorithm. Here, we describe the use of (13)C-labeled experiments and mass distribution measurements to study the evolutionary effects on the fluxome of these differently designed strains. Metabolic flux ratios and intracellular flux distributions as well as physiological data were used to elucidate metabolic responses over the course of adaptive evolution and metabolic differences among strains. The study of 3 unevolved and 12 evolved engineered strains as well as a wild-type strain suggests that evolution resulted in remarkable improvements in both substrate utilization rate and the proportion of glycolytic flux to total glucose utilization flux. Among three strain designs, the most significant increases in the fraction of glucose catabolized through glycolysis (>50%) and the glycolytic fluxes (>twofold) were observed in phosphotransacetylase and phosphofructokinase 1 (PFK1) double deletion (pta- pfkA) strains, which were likely attributed to the dramatic evolutionary increase in gene expression and catalytic activity of the minor PFK encoded by pfkB. These fluxomic studies also revealed the important role of acetate synthetic pathway in anaerobic lactate production. Moreover, flux analysis suggested that independent of genetic background, optimal relative flux distributions in cells could be achieved faster than physiological parameters such as nutrient utilization rate.

Acetate-CoA Ligase↗
PubMed2006

Systems approach to refining genome annotation.

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

Biological Transport↗
PubMed2006

Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale.

We applied whole-genome resequencing of Escherichia coli to monitor the acquisition and fixation of mutations that conveyed a selective growth advantage during adaptation to a glycerol-based growth medium. We identified 13 different de novo mutations in five different E. coli strains and monitored their fixation over a 44-d period of adaptation. We obtained proof that the observed spontaneous mutations were responsible for improved fitness by creating single, double and triple site-directed mutants that had growth rates matching those of the evolved strains. The success of this new genome-scale approach indicates that real-time evolution studies will now be practical in a wide variety of contexts.

Adaptation, Physiological↗
PubMed2006

Experimental and computational assessment of conditionally essential genes in Escherichia coli.

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in approximately 91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

Bacterial Proteins↗
PubMed2006

Matrix formalism to describe functional states of transcriptional regulatory systems.

Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design.

Computational Biology↗
PubMed2006

Long-range periodic patterns in microbial genomes indicate significant multi-scale chromosomal organization.

Genome organization can be studied through analysis of chromosome position-dependent patterns in sequence-derived parameters. A comprehensive analysis of such patterns in prokaryotic sequences and genome-scale functional data has yet to be performed. We detected spatial patterns in sequence-derived parameters for 163 chromosomes occurring in 135 bacterial and 16 archaeal organisms using wavelet analysis. Pattern strength was found to correlate with organism-specific features such as genome size, overall GC content, and the occurrence of known motility and chromosomal binding proteins. Given additional functional data for Escherichia coli, we found significant correlations among chromosome position dependent patterns in numerous properties, some of which are consistent with previously experimentally identified chromosome macrodomains. These results demonstrate that the large-scale organization of most sequenced genomes is significantly nonrandom, and, moreover, that this organization is likely linked to genome size, nucleotide composition, and information transfer processes. Constraints on genome evolution and design are thus not solely dependent upon information content, but also upon an intricate multi-parameter, multi-length-scale organization of the chromosome.

Bacterial Proteins↗
PubMed2006

The model organism as a system: integrating 'omics' data sets.

Various technologies can be used to produce genome-scale, or 'omics', data sets that provide systems-level measurements for virtually all types of cellular components in a model organism. These data yield unprecedented views of the cellular inner workings. However, this abundance of information also presents many hurdles, the main one being the extraction of discernable biological meaning from multiple omics data sets. Nevertheless, researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase our understanding of systems as a whole.

Animals↗
PubMed2005

Modeling the onset of drug dependence: a consideration of the requirement for protein synthesis.

It has been proposed, with some supporting evidence, that development of opiate tolerance and dependence requires protein synthesis. However, a quantitative, biologically based model within which to analyse and support the data has been lacking. Utilizing such a framework or model, we recently compared the time course of onset of opiate dependence in laboratory animals, with the mathematical time course of general changes in protein levels. Not only did the time course of onset of dependence parallel the time course of increasing levels of a protein, but also the half-life of the putative protein required by the model was very similar to those of many brain proteins. In this study, we have more extensively tested the model by producing and examining a much more detailed and surprisingly complex time course of the onset of dependence. Applying the protein synthesis time course model to the data suggested the presence of two distinct components of dependence, an early transient component and a later long-lasting component. These components appear to correspond to acute and chronic dependence, respectively. The protein synthesis hypothesis more readily applies to the chronic dependence portion. Because consideration of the model can generate components that correspond to accepted and well-known components of dependence, both the utility of the model as well as the hypothesis that opiate dependence at least partially requires protein synthesis are supported. It is also possible that individual components of the withdrawal syndrome have individual and unique rate limiting mechanisms. In any case, time course analysis may be helpful in revealing underlying mechanisms of change.

Acute Disease↗
PubMed2005

Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states.

Laboratory evolution can be used to address fundamental questions about adaptation to selection pressures and, ultimately, the process of evolution. In this study, we investigated the reproducibility of growth phenotypes and global gene expression states during adaptive evolution. The results from parallel, replicate adaptive evolution experiments of Escherichia coli K-12 MG1655 grown on either lactate or glycerol minimal media showed that (1) growth phenotypes at the endpoint of evolution are convergent and reproducible; (2) endpoints of evolution have different underlying gene expression states; and (3) the evolutionary gene expression response involves a large number of compensatory expression changes and a smaller number of adaptively beneficial expression changes common across evolution strains. Gene expression changes initially showed a large number of differentially expressed genes in response to an environmental change followed by a return of most genes to a baseline expression level, leaving a relatively small set of differentially expressed genes at the endpoint that varied between evolved populations.

Biological Evolution↗