Mechanistically detailed model of cellular metabolism for glucose-limited growth ofEscherichia coli B/r-A

MPS: An artificially intelligent software system for the analysis and synthesis of metabolic pathways

The concepts of artificial intelligence have been applied for the development of a software system for metabolic pathway synthesis (MPS). An easily expandable data base system for storing enzyme and substance descriptions is used by a search algorithm for the identification of possible ways to interconvert carbon-carrying metabolites. A versatile screening capability permits the user to identify all pathways which contain or exclude any combination of enzymes, substrates, and/or products. Information provided by MPS can be used to predict on a qualitative basis the effects of adding or deleting enzymatic activities to or from the cellular environment, to classify pathways with respect to cellular objectives, and to extract information about metabolic regulation. MPS can be used subsequently to aid the identification of appropriate genotypes or genetic modifications that will redirect metabolism towards amplified production of desirable bioproducts. Two examples illustrating the capabilities of MPS are presented. In the first example, which considers the conversion of glucose 6-phosphate to pyruvate, MPS synthesized the classical catabolic pathways (EMP, pentose phosphate and ED) along with possible variations. A route for the biosynthesis of L-alanine that does not incorporate the enzyme alanine aminotransferase was revealed by MPS during synthesis of alternative pathways which produce L-alanine from pyruvate.

Ribosomal protein limitations in Escherichia coli under conditions of high translational activity

Details of the mechanism for ribosome synthesis have been incorporated in the single-cell Escherichia coli model, which enable us to predict the amount of protein synthesizing machinery under different environmental conditions. The predictions agree quite well with available experimental data. The model predicts that ribosomal protein limitations are important when the translational apparatus is in high demand. Ribosomal RNA synthesis is induced by an increase in translational activity, which, in turn, stimulates ribosomal protein synthesis. However, as the demand increases still more, the ribosomal protein mRNA must compete with the plasmid mRNA for ribosomes, and the efficiency of translation of ribosomal proteins is reduced. (c) 1994 John Wiley & Sons, Inc.

Mathematical modeling of induced foreign protein production by recombinant bacteria

A new generalized mathematical model for recombinant bacteria which includes inducer effects on cell growth and foreign protein production is developed. The model equation set was applied to a host-vector system, Escherichi coli D1210 and plasmid pSD8. Batch experiments were designed and performed in shake flasks to verify the model. A parameter estimation method was developed and proven to be efficient. Although simple, the model can effectively describe the dynamics of the production of foreign protein in recombinant bacteria and can be used for optimization and control studies to maximize foreign protein production.

Rapid responses of ribosomal RNA synthesis to nutrient shifts

A major challenge in systems biology is to integrate our mechanistic understanding of gene regulation to predict quantitatively how cells will respond to environmental changes. Living cells respond rapidly to the availability of nutrients in part by altering production of ribosomal RNA (rRNA), a limiting component in the biosynthesis of ribosomes. Studies of rRNA transcription by the RNA polymerase of Escherichia coli have identified regulatory roles for guanosine tetraphosphate (ppGpp), the initiating nucleotide, and the protein DksA. To what extent findings from in vitro studies can be used to quantitatively predict in vivo responses to changing nutrient environments is unknown. We developed a mechanistic mathematical model for rRNA transcriptional responses to such changes. Our model accounts for binding of RNAP to its rRNA promoter to form a closed complex, isomerization from a closed complex to an open complex, reversible incorporation of the initiating NTP (iNTP), transcript elongation, and clearance of the promoter. Further, the model incorporates interactions between ppGpp and DksA with transcription intermediates, and it includes an empirical correction to account for salt effects. The model biophysical parameters were determined using 33 single- and multi-round transcription experiments spanning 487 in vitro measurements. By incorporating in vivo measurements of ppGpp and ATP, the model correctly predicted rRNA production rates for cellular responses to nutrient upshifts, downshifts, and outgrowth into fresh medium. Inclusion of DksA was essential in all three cases. Our work provides a foundation for using data-driven computational models to predict the kinetics of in vivo transcriptional responses.

Sequence-based analysis of metabolic demands for protein synthesis in prokaryotes

Constraints-based models for microbial metabolism can currently be constructed on a genome-scale. These models do not account for RNA and protein synthesis. A scalable formalism to describe translation and transcription that can be integrated with the existing metabolic models is thus needed. Here, we developed such a formalism. The fundamental protein synthesis network described by this formalism was analysed via extreme pathway and flux balance analyses. The protein synthesis network exhibited one extreme pathway per messenger RNA synthesized and one extreme pathway per protein synthesized. The key parameters in this network included promoter strengths, messenger RNA half-lives, and the availability of nucleotide triphosphates, amino acids, RNA polymerase, and active ribosomes. Given these parameters, we were able to calculate a cell's material and energy expenditures for protein synthesis using a flux balance approach. The framework provided herein can subsequently be integrated with genome-scale metabolic models, providing a sequence-based accounting of the metabolic demands resulting from RNA and protein polymerization.

A memorial review of Jay Bailey's contribution in prokaryotic metabolic engineering

When mentioning prokaryotic metabolic engineering, most people will immediately think of Jay Bailey. Jay's contribution to this fast-growing field is evident and familiar to many. Therefore, instead of a detailed technical review, we attempt in this article to summarize his contribution and dissect reasons for his success in this area from a standpoint of one of his former students (VH) and of a colleague in the field (JCL). This short review is by no means complete and provides only a partial view of Jay's contribution to the metabolic engineering of prokaryotes.

Development of bioreaction engineering

In addition to summarizing the early investigations in bioreaction engineering, the present short review covers the development of the field in the last 50 years. A brief overview of the progress of the fundamentals is presented in the first part of this article and the key issues of bioreaction engineering are advanced in its second part.

Investigating autocatalytic gene expression systems through mechanistic modeling

A structured model of gene expression, which incorporates the stochastic behavior of cellular processes, was developed to examine the "all-or-none" phenomenon observed in autocatalytic systems (e.g. the lac operon). Autocatalytic expression systems typically have the genes encoding the inducer transport proteins controlled by internal inducer levels, so that transport of the inducer increases production of the transport protein. The model was able to predict the unique behaviors of autocatalytic expression systems that have been experimentally observed and provided valuable insight into the role of population heterogeneity in these systems. The simulations substantiate the importance of stochastic processes on induction of gene expression in autocatalytic systems. The simulation results show that the all-or-none phenomenon is governed largely by random cellular events, and that population-averaged variations in gene expression are due to changes in the frequency of full gene induction in individual cells rather than to uniform variations in gene expression across the entire population. In addition, the model shows how concentrations of inducer too low to induce expression in uninduced cells can maintain induction in pre-induced cultures. A comparison of induction behaviors from an autocatalytic system and a system having constitutive synthesis of the transport protein showed that transport protein levels must be decoupled from inducer control to achieve homogeneous expression of a gene of interest in all cells of a culture.

Characterization of stress and protein turnover from protein overexpression in fed-batch E. coli cultures

A structured kinetic model that accounts for proteolytic degradation due to recombinant protein overexpression is introduced and its performance evaluated by comparison with previously reported fed-batch experimental data. This mathematical model contains an additional pool for a generic key precursor (in our case phenylalanine), an improved IPTG transport term, a phenylalanine transport term, and a variable protein turnover expression that accounts for proteolytic activity. The model predictions concerning proteolytic activity, glucose level, and cell growth are in very good agreement with an amino acid depletion hypothesis. Cultures exposed to greater stress showed higher and/or longer proteolysis, whereas less overall proteolytic activity was observed when the effect of induction was somewhat ameliorated.

Single-cell models: promise and limitations

This article describes the development of single-cell models, their uses and accomplishments, the barriers to the greater adoption, and a perspective on challenges to the biochemical engineering community where the single-cell model approach may be used advantageously. In particular, it may become an important tool in relating genomic information to cellular regulation and dynamics.

Mathematical models of metabolic pathways

There have been recent advances in metabolic flux analysis. In particular, the marriage of traditional flux balancing with NMR isotopomer distribution analysis holds great promise for the detailed quantification of physiology. Nevertheless, flux analysis yields only static snap-shots of metabolism. To robustly predict the time evolution of metabolic networks, dynamic mathematical models, especially those that contain a description of both gene expression as well as enzyme activity, must be utilized. When mechanistic control and regulatory information is not available, heuristic-based methods, such as the cybernetic framework, can be employed to describe the action of these control mechanisms. In the 'high-information' future, as more biological information becomes available, such heuristic-based approaches can be replaced by mechanistic mass-action representations of physiology that stem directly from genetic sequence.

Continuous culture dynamics for aniline metabolism by Pseudomonas sp. CIT1

Inhibition by toxic substrates enables multiple steady states to arise in biodegradation systems. This phenomenon was investigated for the continuous metabolism of aniline by Pseudomonas sp. CIT1. Differences of various metabolic parameters between the two growth regimes (uninhibited and inhibited) and the transient response to a step-up in dilution rate were determined. Regulatory mechanisms consistent with the experimental evidence are proposed. Aniline is the transcriptional inducer of a metabolic pathway that converts aniline to TCA cycle intermediates. The suite of enzymes is coordinately expressed from a single promoter. We followed the level of the pathway mRNA using a fragment containing the catechol 2,3 dioxygenase gene (andioxB) and monitored the pathway enzyme activity using catechol 2,3 dioxygenase (C23D). The inhibited regime resulted in a 60% lower growth yield, near constant levels of C23D monomer, but a 50% reduction in the specific activity of C23D, increased RNA synthesis rates (total and aniline pathway mRNA), and elevated RNA decay rates. Elucidation of regulatory mechanisms indicates that C23D is noncompetitively inhibited by aniline and subject to feedback inhibition by 2-hydroxymuconic semialdehyde (HMS). During uninhibited growth regime operation, metabolism of HMS is the rate-limiting step; in contrast, conversion of aniline to catechol limits growth in the inhibited regime.

Host-vector interactions in Escherichia coli

Introduction of a DNA vector into E. coli for the purposes of cloned gene expression can perturb native cell functions at many levels. The presence of foreign DNA can alter regulation of chromosomal DNA replication, regulation of transcription of chromosomal genes, ribosome functions and RNA turnover, activities of regulatory proteins, chaperone and protease levels, membrane energetics and protein post-translational processing, as well as energy and intermediary metabolism of the cell. The combined effects of these interactions on the metabolic characteristics of the host-vector system have major implications for yields of cloned biotechnological products and interactions of genetically engineered organisms with their environment.

Transport regulation of recombinant gene expression in E. coli and B. subtilis

Expression kinetics of the lactose (lac) operon in Escherichia coli are reviewed for both wild-type and recombinant cell cultures under chemostatic conditions. A unified model which involves regulation of active inducer (lactose) transport, promoter-operator regulated expression of the lac operon, glucose-mediated inducer exclusion, and catabolite repression is summarized and supporting data is shown to verify its accuracy. The synthesis of alpha-amylase with a recombinant form of Bacillus subtilis is also reviewed to point out generic features in transport regulation, the lac operon model providing a point of departure. While there are many similarities in the influence of transport on both regulating models, there are also important differences. In a chemostat system, the synthesis of alpha-amylase is nongrowth associated, while beta-galactosidase is a growth-associated enzyme. Nevertheless, transport regulation is an important feature in both instances.

Studies of host-plasmid interactions in recombinant microorganisms

Plasmid genes redirect some components of cellular metabolism into synthesis of plasmid gene products and additional plasmids. The stoichiometric and kinetic implications of these host-plasmid interactions have been investigated theoretically and experimentally. Using known pathway energetics, maximum theoretical yield factors based on ATP, glucose, and O2 have been estimated for recombinant Escherichia coli and compared with corresponding estimates for host cells alone, indicating major changes in carbon and energetic stoichiometry in recombinant cells in cases of high cloned gene expression. The influence of the number of plasmids in recombinant E. coli has been experimentally characterized using a series of pMB1 derivatives stably propagated at copy numbers from 12 to 408. Recombinant cell growth rate declines monotonically as plasmid content increases as does efficiency of plasmid gene expression. A detailed metabolically structured single-cell model for E. coli has successfully simulated these trends. Interrelationships among number of plasmids per cell, induction of expression of a plasmid gene, and recombinant population growth rate have been experimentally delineated for Saccharomyces cerevisiae containing plasmid pLGSD5 and derivatives in which the 2-micron origin of replication has been replaced by a cloned ARS1 sequence or its deletion fragments. The CEN4 centromere sequence has been included in some of these plasmids to provide more regular segregation. Specific growth rate of these recombinant yeasts exhibits a maximum as a function of plasmid content, an effect attributed to the interplay between beneficial effects of the plasmid in selective medium and parasitic effects on metabolism at larger plasmid content or with more plasmid gene expression activity. The yeast strains investigated exhibit substantial segregational instability that was characterized using a rapid-flow cytometry measurement based upon single-cell deletion of E. coli beta-galactosidase activity in recombinant cells.