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Bhattacharya, Subhamoy; Chakrabarti, Subhra; Nayak, Amiya; Bhattacharya, Sanjoy K.

doi:10.1186/1475-2859-2-3

Cited by 8 publications

(4 citation statements)

References 57 publications

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“…However, in recent years, renewed interest is obvious in not only phage therapy [ 5 - 8 ], but also in detection and diagnostics [ 9 - 11 ], bacterial control [ 12 - 17 ] and recombinant protein production [ 18 - 20 ]. Moreover, bacteriophages have now been identified as important tools in many aspects of nano-medicine - such as phage display for treatment or drug discovery, gene or drug delivery or even in direct cancer treatment [ 21 - 30 ]. These developments have led to the re-evaluation of the potential uses of bacteriophages and to attempts to improve the methods of production.…”

Section: Introductionmentioning

confidence: 99%

Two-stage, self-cycling process for the production of bacteriophages

Sauvageau

Cooper

2010

Microb Cell Fact

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BackgroundA two-stage, self-cycling process for the production of bacteriophages was developed. The first stage, containing only the uninfected host bacterium, was operated under self-cycling fermentation (SCF) conditions. This automated method, using the derivative of the carbon dioxide evolution rate (CER) as the control parameter, led to the synchronization of the host bacterium. The second stage, containing both the host and the phage, was operated using self-cycling infection (SCI) with CER and CER-derived data as the control parameters. When each infection cycle was terminated, phages were harvested and a new infection cycle was initiated by adding host cells from the SCF (first stage). This was augmented with fresh medium and the small amount of phages left from the previous cycle initiated the next infection cycle. Both stages were operated independently, except for this short period of time when the SCF harvest was added to the SCI to initiate the next cycle.ResultsIt was demonstrated that this mode of operation resulted in stable infection cycles if the growth of the host cells in the SCF was synchronized. The final phage titers obtained were reproducible among cycles and were as good as those obtained in batch productions performed under the same conditions (medium, temperature, initial multiplicity of infection, etc.). Moreover, phages obtained in different cycles showed no important difference in infectivity. Finally, it was shown that cell synchronization of the host cells in the first stage (SCF) not only maintained the volumetric productivity (phages per volume) but also led to higher specific productivity (phage per cell per hour) in the second stage (SCI).ConclusionsProduction of bacteriophage T4 in the semi-continuous, automated SCF/SCI system was efficient and reproducible from cycle to cycle. Synchronization of the host in the first stage prior to infection led to improvements in the specific productivity of phages in the second stage while maintaining the volumetric productivity. These results demonstrate the significant potential of this approach for both upstream and downstream process optimization.

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Section: Introductionmentioning

confidence: 99%

Two-stage, self-cycling process for the production of bacteriophages

Sauvageau

Cooper

2010

Microb Cell Fact

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“…This type of subnet is radically different from the regional-based subnets. It is, however, a frequent scenario in the analysis of technological and biological networks: most studies of molecular networks, such as protein-protein interaction [4,5], gene-regulation [6] and metabolic networks [7], test for connections between a subset of the known molecular entities (proteins, genes and enzymes/metabolites, respectively). The process by which these entities (or corresponding probes) are chosen may reflect the bias of the experimenter or merely chance, and this will in turn influence the extent to which * Electronic address: m.stumpf@imperial.ac.uk; URL: http://www.bio.ic.ac.uk/research/stumpf † Electronic address: wiuf@birc.au.dk; URL: http://www.birc.au.dk/~wiuf In the text we show that sometimes, however, degree distributions in both networks can be related under random sampling of nodes.…”

Section: Introductionmentioning

confidence: 99%

Sampling properties of random graphs: The degree distribution

Stumpf

Wiuf

2005

Phys. Rev. E

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We discuss two sampling schemes for selecting random subnets from a network: Random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distribution of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is fulfilled by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real E.coli protein interaction network data.

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“…(c) The "hub proteins", i.e., those with dense links in the network, are likely to be more important than those with sparse links [55]. (d) The PPI network tends to be the shortest in the length of its paths [56].…”

Section: The Characteristics Of the Ppi (Protein-protein Interaction)mentioning

confidence: 98%

Computational Methods for Protein-Protein Interaction and their Application

Shi

Li²,

Cai³

et al. 2005

CPPS

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Protein-protein interactions play a central role in numerous processes in cell and are one of the main research fields in current functional proteomics. The increase of finished genomic sequences has greatly stimulated the progress for detecting the functions of the genes and their encoded proteins. As complementary ways to the high through-put experimental methods, various methods of bioinformatics have been developed for the study of the protein-protein interaction. These methods range from the sequence homology-based to the genomic-context based. Recently, it tends to integrate the data from different methods to build the protein-protein interaction network, and to predict the protein function from the analysis of the network structure. Efforts are ongoing to improve these methods and to search for novel aspects in genomes that could be exploited for function prediction. This review highlights the recent advances of the bioinformatics methods in protein-protein interaction researches. In the end, the application of the protein-protein interaction has also been discussed.

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Cited by 8 publications

References 57 publications

Two-stage, self-cycling process for the production of bacteriophages

Two-stage, self-cycling process for the production of bacteriophages

Sampling properties of random graphs: The degree distribution

Computational Methods for Protein-Protein Interaction and their Application

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