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C D Maranas

Publications and source records attributed to C D Maranas.

6 recordsLinked to original sources
PubMed2001

Creating multiple-crossover DNA libraries independent of sequence identity.

We have developed, experimentally implemented, and modeled in silico a methodology named SCRATCHY that enables the combinatorial engineering of target proteins, independent of sequence identity. The approach combines two methods for recombining genes: incremental truncation for the creation of hybrid enzymes and DNA shuffling. First, incremental truncation for the creation of hybrid enzymes is used to create a comprehensive set of fusions between fragments of genes in a DNA homology-independent fashion. This artificial family is then subjected to a DNA-shuffling step to augment the number of crossovers. SCRATCHY libraries were created from the glycinamide-ribonucleotide formyltransferase (GART) genes from Escherichia coli (purN) and human (hGART). The developed modeling framework eSCRATCHY provides insight into the effect of sequence identity and fragmentation length on crossover statistics and draws contrast with DNA shuffling. Sequence analysis of the naive shuffled library identified members with up to three crossovers, and modeling predictions are in good agreement with the experimental findings. Subsequent in vivo selection in an auxotrophic E. coli host yielded functional hybrid enzymes containing multiple crossovers.

Algorithms↗
PubMed2001

Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions.

An optimization-based procedure for studying the response of metabolic networks after gene knockouts or additions is introduced and applied to a linear flux balance analysis (FBA) Escherichia coli model. Both the gene addition problem of optimally selecting which foreign genes to recombine into E. coli, as well as the gene deletion problem of removing a given number of existing ones, are formulated as mixed-integer optimization problems using binary 0-1 variables. The developed modeling and optimization framework is tested by investigating the effect of gene deletions on biomass production and addressing the maximum theoretical production of the 20 amino acids for aerobic growth on glucose and acetate substrates. In the gene deletion study, the smallest gene set necessary to achieve maximum biomass production in E. coli is determined for aerobic growth on glucose. The subsequent gene knockout analysis indicates that biomass production decreases monotonically, rendering the metabolic network incapable of growth after only 18 gene deletions. In the gene addition study, the E. coli flux balance model is augmented with 3,400 non-E. coli reactions from the KEGG database to form a multispecies model. This model is referred to as the Universal model. This study reveals that the maximum theoretical production of six amino acids could be improved by the addition of only one or two genes to the native amino acid production pathway of E. coli, even though the model could choose from 3,400 foreign reaction candidates. Specifically, manipulation of the arginine production pathway showed the most promise with 8.75% and 9.05% predicted increases with the addition of genes for growth on glucose and acetate, respectively. The mechanism of all suggested enhancements is either by: 1) improving the energy efficiency and/or 2) increasing the carbon conversion efficiency of the production route.

Aerobiosis↗
PubMed2001

Predicting crossover generation in DNA shuffling.

We introduce a quantitative framework for assessing the generation of crossovers in DNA shuffling experiments. The approach uses free energy calculations and complete sequence information to model the annealing process. Statistics obtained for the annealing events then are combined with a reassembly algorithm to infer crossover allocation in the reassembled sequences. The fraction of reassembled sequences containing zero, one, two, or more crossovers and the probability that a given nucleotide position in a reassembled sequence is the site of a crossover event are estimated. Comparisons of the predictions against experimental data for five example systems demonstrate good agreement despite the fact that no adjustable parameters are used. An in silico case study of a set of 12 subtilases examines the effect of fragmentation length, annealing temperature, sequence identity and number of shuffled sequences on the number, type, and distribution of crossovers. A computational verification of crossover aggregation in regions of near-perfect sequence identity and the presence of synergistic reassembly in family DNA shuffling is obtained.

Algorithms↗
PubMed2000

Modeling DNA mutation and recombination for directed evolution experiments.

Directed evolution experiments rely on the cyclical application of mutagenesis, screening and amplification in a test tube. They have led to the creation of novel proteins for a wide range of applications. However, directed evolution currently requires an uncertain, typically large, number of labor intensive and expensive experimental cycles before proteins with improved function are identified. This paper introduces predictive models for quantifying the outcome of the experiments aiding in the setup of directed evolution for maximizing the chances of obtaining DNA sequences encoding enzymes with improved activities. Two methods of DNA manipulation are analysed: error-prone PCR and DNA recombination. Error-prone PCR is a DNA replication process that intentionally introduces copying errors by imposing mutagenic reaction conditions. The proposed model calculates the probability of producing a specific nucleotide sequence after a number of PCR cycles. DNA recombination methods rely on the mixing and concatenation of genetic material from a number of parent sequences. This paper focuses on modeling a specific DNA recombination protocol, DNA shuffling. Three aspects of the DNA shuffling procedure are modeled: the fragment size distribution after random fragmentation by DNase I, the assembly of DNA fragments, and the probability of assembling specific sequences or combinations of mutations. Results obtained with the proposed models compare favorably with experimental data.

Animals↗
PubMedDate not supplied

Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments.

A computational procedure for identifying the minimal set of metabolic reactions capable of supporting various growth rates on different substrates is introduced and applied to a flux balance model of the Escherichia coli metabolic network. This task is posed mathematically as a generalized network optimization problem. The minimal reaction sets capable of supporting specified growth rates are determined for two different uptake conditions: (i) limiting the uptake of organic material to a single organic component (e.g., glucose or acetate) and (ii) allowing the importation of any metabolite with available cellular transport reactions. We find that minimal reaction network sets are highly dependent on the uptake environment and the growth requirements imposed on the network. Specifically, we predict that the E. coli network, as described by the flux balance model, requires 224 metabolic reactions to support growth on a glucose-only medium and 229 for an acetate-only medium, while only 122 reactions enable growth on a specially engineered growth medium.

Cell Division↗