Computer model for glucose-limited growth of a single cell ofEscherichia coli B/r-A

Domach, Michael M.; Leung, S. K.; Cahn, R. E.; Cocks, George G.; Shuler, Michael L.

doi:10.1002/(sici)1097-0290(20000320)67:6<827::aid-bit18>3.0.co;2-n

Cited by 16 publications

(14 citation statements)

References 36 publications

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“…However, such modeling approaches commonly involve either kinetic analysis [11] which requires detailed enzyme kinetic information that is still mostly unknown, or Metabolic Control Analysis [12] that requires experiment-based measurements of flux control coefficients that are also mostly unavailable. An alternative modeling approach, called constraint-based modeling (CBM), analyzing the function of genome-scale metabolic networks through relying solely on simple physical-chemical constraints[13], [14].…”

Section: Introductionmentioning

confidence: 99%

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Tepper

Shlomi

2011

PLoS ONE

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Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).

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Section: Introductionmentioning

confidence: 99%

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Tepper

Shlomi

2011

PLoS ONE

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“…The base model is the Cornell E. coli model (26) that accounts for control of chromosome replication, cell division, cell shape, and volume changes. The prototype version of the Cornell E. coli model was reported in 1979 (27) and modified in 1983 and 1984 (26,28) to simulate the growth of an individual cell using 18 components. The general structure of the model is sketched in Fig.…”

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confidence: 99%

“…The model includes: (i) pseudochemical reactions summarizing the stoichiometry relationships, (ii) kinetic relationships reflecting general dependencies of major metabolic pathways, (iii) metabolic control using the concentration of chemical components as signals, and (iv) evaluation of kinetic and stoichiometric parameters from independent experiments on cells growing exponentially in complex media. The Cornell E. coli model has been used to study glucose- (26) and ammonia-limited growth (28) and to simulate a population of cells (29,30). Further work (31) expanded the model to include the uptake and incorporation of amino acids.…”

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confidence: 99%

A modular minimal cell model: Purine and pyrimidine transport and metabolism

Castellanos

Wilson²,

Shuler³

2004

Proc. Natl. Acad. Sci. U.S.A.

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A more complete understanding of the relationship of cell physiology to genomic structure is desirable. Because of the intrinsic complexity of biological organisms, only the simplest cells will allow complete definition of all components and their interactions. The theoretical and experimental construction of a minimal cell has been suggested as a tool to develop such an understanding. Our ultimate goal is to convert a ''coarse-grain'' lumped parameter computer model of Escherichia coli into a genetically and chemically detailed model of a ''minimal cell.'' The base E. coli model has been converted into a generalized model of a heterotrophic bacterium. This coarse-grain minimal cell model is functionally complete, with growth rate, composition, division, and changes in cell morphology as natural outputs from dynamic simulations where only the initial composition of the cell and of the medium are specified. A coarse-grain model uses pseudochemical species (or modules) that are aggregates of distinct chemical species that share similar chemistry and metabolic dynamics. This model provides a framework in which these modules can be ''delumped'' into chemical and genetic descriptions while maintaining connectivity to all other functional elements. Here we demonstrate that a detailed description of nucleotide precursors transport and metabolism is successfully integrated into the whole-cell model. This nucleotide submodel requires fewer (12) genes than other theoretical predictions in minimal cells. The demonstration of modularity suggests the possibility of developing modules in parallel and recombining them into a fully functional chemically and genetically detailed model of a prokaryote cell. T he basic design rules relating the regulation of cellular function to genomic structure is of broad interest. Bioinformatics emerged as an approach to convert static linear sequence genomic data into an understanding of the dynamic nonlinear function of living organisms. Initial efforts have focused on identifying the proteins encoded in the genome and, subsequently, identifying protein function and regulatory elements in the genome. These efforts are able to address specific questions but cannot translate genomic data broadly into an understanding of cell function. We propose a reverse approach. We ask how we would design a cell to achieve expected functions and, from that design, how we would write the genomic instructions. This approach follows the typical engineering design approach where desired performance dictates functional design, which is then translated into blueprints. To accomplish this goal, we are constructing a chemically and genetically minimal cell computer model. By modeling the essential regulatory structure and functions to maintain a living cell, we expect to better understand the relationship of genomic instructions to cell function and regulation.A "minimal cell" is a hypothetical cell possessing the minimum functions required for sustained growth and reproduction in a maximally supportive culture env...

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“…Traditional modeling techniques are based on mathematical approaches that require detailed and accurate information regarding reaction kinetics as well as enzyme and metabolite concentrations [1,2]. The lack of sufficient data limits the current applicability of such methods to small-scale systems.…”

Section: Introductionmentioning

confidence: 99%