Dataset Information

Decroly1982_Enzymatic_Oscillator

ABSTRACT: This is the scaled model described in the article: Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system Olivier Decroly, Albert Goldbeter, Proc Natl Acad Sci USA 1982 79:6917-6921; PMID:6960354; Abstract: We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms. The parameters q1 = 50 and q2 = 0.02 are explicitely included as the stoichiometric coefficients of beta and gamma in the reactions r2 and r3, respectively. Parameter values and initial conditions [ks=1.99/sec, alpha(0)=29.19988, beta(0)=188.8, gamma(0)=0.3367] are for the chaotic regime presented in the upper-curve of Figure 3b.

SUBMITTER: Kieran Smallbone

PROVIDER: BIOMD0000000319 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000319?filename=BIOMD0000000319-biopax2.owl	Owl
	BIOMD0000000319?filename=BIOMD0000000319-biopax3.owl	Owl
	BIOMD0000000319?filename=BIOMD0000000319-matlab.m	Other
	BIOMD0000000319?filename=BIOMD0000000319-octave.m	Other
	BIOMD0000000319?filename=BIOMD0000000319.m	Other

Items per page:

1 - 5 of 15

Publications

Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system.

Decroly O O Goldbeter A A

Proceedings of the National Academy of Sciences of the United States of America 19821101 22

We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable stead ...[more]

PMID: 6960354

Similar Datasets

Project description:Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear, as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures, or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community of prokaryotes and yeasts, realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open metabolic niches for followers. Through metabolomics and large-scale mapping of inter-species interactions, we show how microbes poorly suited for milk survive in — and even dominate — the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence. Linked metabolomics studies:<a href='https://www.ebi.ac.uk/metabolights/MTBLS1823' rel='noopener noreferrer' target='_blank'>MTBLS1823</a> Spent-medium assay, measured untargeted (FIA-qTOF) and targeted (HILIC, LC-MS)<a href='https://www.ebi.ac.uk/metabolights/MTBLS1829' rel='noopener noreferrer' target='_blank'>MTBLS1829</a> Kefir fermentation curve (FIA-MS) Linked cross omic data:Genomes of isolated kefir species are available in the <a href='https://www.ncbi.nlm.nih.gov/' rel='noopener noreferrer' target='_blank'>NCBI database</a> under the accession: PRJNA375758 (bioproject ID: 375758).Metatranscriptomic sequencing reads can be accessed from <a href='https://www.ebi.ac.uk/metabolights/editor/www.ebi.ac.uk/ena' rel='noopener noreferrer' target='_blank'>ENA</a> under the project id PRJEB37001.Genome-scale metabolic models for kefir bacteria can be found at github.com/cdanielmachado/kefir_models.

Project description:Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear, as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures, or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community of prokaryotes and yeasts, realises stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open metabolic niches for followers. Through metabolomics and large-scale mapping of inter-species interactions, we show how microbes poorly suited for milk survive in, and even dominate the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence. Linked metabolomics studies:<a href='https://www.ebi.ac.uk/metabolights/MTBLS1829' rel='noopener noreferrer' target='_blank'>MTBLS1829</a> Kefir fermentation curve (FIA-MS)<a href='https://www.ebi.ac.uk/metabolights/MTBLS1830' rel='noopener noreferrer' target='_blank'>MTBLS1830</a> Interaction between the kefir isolates Lactococcus lactis and Acetobacter fabarum (GC-MS) Linked cross omic data:Genomes of isolated kefir species are available in the <a href='https://www.ncbi.nlm.nih.gov/' rel='noopener noreferrer' target='_blank'>NCBI database</a> under the accession: PRJNA375758 (bioproject ID: 375758).Metatranscriptomic sequencing reads can be accessed from <a href='www.ebi.ac.uk/ena' rel='noopener noreferrer' target='_blank'>ENA</a> under the project id PRJEB37001.Genome-scale metabolic models for kefir bacteria can be found at github.com/cdanielmachado/kefir_models.

Project description:Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear, as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures, or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community of prokaryotes and yeasts, realises stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open metabolic niches for followers. Through metabolomics and large-scale mapping of inter-species interactions, we show how microbes poorly suited for milk survive in, and even dominate the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence. Linked metabolomics studies:<a href='https://www.ebi.ac.uk/metabolights/MTBLS1823' rel='noopener noreferrer' target='_blank'>MTBLS1823</a> Spent-medium assay, measured untargeted (FIA-qTOF) and targeted (HILIC, LC-MS)<a href='https://www.ebi.ac.uk/metabolights/MTBLS1830' rel='noopener noreferrer' target='_blank'>MTBLS1830</a> Interaction between the kefir isolates Lactococcus lactis and Acetobacter fabarum (GC-MS) Linked cross omic data sets:Genomes of isolated kefir species are available in the NCBI database under the accession: PRJNA375758 (bioproject ID: 375758).Metatranscriptomic sequencing data associated with this study are available in the European Nucleotide Archive (ENA): accession number <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB37001' rel='noopener noreferrer' target='_blank'>PRJEB37001</a>.Genome-scale metabolic models for kefir bacteria can be found at github.com/cdanielmachado/kefir_models.

Project description:Controversy regarding genetically modified (GM) plants and their potential impact on human health contrasts with the tacit acceptance; of other plants that were also modified, but not considered as GM products (e.g., varieties raised through conventional breeding such as mutagenesis). What is beyond the phenotype of these improved plants? Should mutagenized plants be treated differently from transgenics? We have evaluated the extent of transcriptome modification occurring during rice improvement through transgenesis versus mutation breeding. We used oligonucleotide microarrays to analyze gene expression in four different pools of four types of rice plants and respective controls: (i) a; gamma-irradiated stable mutant, (ii) the M1 generation of a 100-Gy gamma-irradiated plant, (iii) a stable transgenic plant obtained for production of an anticancer antibody, and (iv) the T1 generation of a transgenic plant produced aiming for abiotic stress improvement, and all of the unmodified original genotypes as controls. We found that the improvement of a plant variety through the acquisition of a new desired trait, using either mutagenesis or transgenesis, may cause stress and thus lead to an altered expression of untargeted genes. In all of the cases studied, the observed alteration was more extensive in mutagenized than in transgenic plants. We propose that the safety assessment of improved plant varieties should be carried out on a case-by-case basis and not simply restricted to foods obtained through genetic engineering. Experiment Overall Design: Plant Materials Experiment Overall Design: Two genetically stable Oryza sativa L. ssp. japonica lines: a gamma-irradiated ricemutant(cv. EstrelaA)and a well characterized transgenic rice line (cv. Bengal) were used as well as controls. The stable mutant was obtained in 1988 by gamma-irradiation, had already gone over 10 generations of self-pollination, and had a mature average height about 45 cm lower than the wild type. The stable transgenic line, which was already in the third generation of self-pollination after transformation, expresses a ScFV antibody (ScFvT84.66) against carcinoembryonic antigen, a well characterized tumor-associated marker antigen. Experiment Overall Design: We have also used two genetically unstable rice lines: the M1 generation of a 100-Gy gamma-irradiated line (98% survival after mutagenesis) and the T1 generation of an Agrobacterium-transformed transgenic line (both cv. Nipponbare) containing one copy of the BCBF1 gene driven by the AtRD29A promoter from Arabidopsis and one copy of the hpt II gene. We used seeds from the same self-pollinated panicle for control and irradiation/transgenesis. The unstable mutant line chosen for this experiment was the one showing a phenotype more similar to that of the nonirradiated control. Experiment Overall Design: In the case of the transgenic lines, stability was based on the stable Experiment Overall Design: inheritance of the introduced transgenes in the homozygous progeny. Regarding the mutagenized plants we have defined as genetically stable plants those that, after mutagenesis, had already gone through several cycles of self-pollination while maintaining the desirable traits. Experiment Overall Design: RNA Extraction and Microarrays Experiment Overall Design: Two pools of six whole seedlings were prepared for each condition under test, and RNA was isolated with the RNeasy Plant Mini Kit (Qiagen) following the manufacturerâ??s instructions. Total RNA was kept at -80Â°C and sent to the Affymetrix core facility (Instituto Gulbenkian de CiÃªncia, Experiment Overall Design: Oeiras, Portugal), where quality-control analysis was carried out before cDNA synthesis from themRNA[with appropriate oligo(dT) primers], labeling (through synthesis of cRNA with incorporation of biotinylated ribonucleotide analogs), and hybridization to the GeneChip Rice Genome Array (Affymetrix). This array contains probes to query 51,279 transcripts representing two rice subspecies (48,564 japonica transcripts and 1,260 transcripts of indica subspecies). Experiment Overall Design: Data Analysis Experiment Overall Design: Microarrays data analysis was performed with Partek Genomics Suite software. Affymetrix CEL files were imported by using the Robust Multichip Average method, which involves four steps: background correction of the perfect match values, quintile normalization across all of the chips in the experiment, Log2 transformation, and median polish summarization. The logged data were used for hierarchical cluster analysis and statistical analysis. Experiment Overall Design: Hierarchical cluster analysis was performed by using Pearsonâ??s dissimilarity product moment correlation coefficient and Wardâ??s algorithm. Experiment Overall Design: For the identification of differentially expressed genes we used ANOVA Experiment Overall Design: and a false discovery rate with a 0.05 threshold.