329 research outputs found

    A Characterization of Scale Invariant Responses in Enzymatic Networks

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    An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

    Challenges in experimental data integration within genome-scale metabolic models

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    A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincar\'e, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.Comment: 5 page

    Modeling in Systems Biology

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    The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

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    Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment

    The Epistemology of Causal Selection: Insights from Systems Biology

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    Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. However, I argue that this is only part of the epistemology of causal selection. Recent work on portable causes has implicitly assumed them to be portable within the same causal system at a later time. As a result, it has appeared that the objective content of causal selection includes only facts about the causal structure of that single system. In contrast, I present a case study from systems biology in which scientists are searching for causal factors that are portable across rather than within causal systems. By paying careful attention to how these biologists find portable causes, I show that the objective content of causal selection can extend beyond the immediate systems of interest. In particular, knowledge of the evolutionary history of gene networks is necessary for correctly identifying causal patterns in these networks that explain cellular behavior in a portable way

    Production of Single Cell Protein and Astaxanthin Using Methanol as Carbon Source

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    Singlecell protein (SCP) is the biomass of unicellular organisms, such as bacteria or yeast, which is used commonly as a food source for animals. With a high protein content, a broad amino acid profile, and the ability to produce essential organic compounds and vitamins, SCP is a promising alternative to other classical sources of animal feed. Several processes have been developed to manufacture SCP for use in feedstocks for the sustainable farming of fish and other aquatic life, or aquaculture, which is one of the fastest growing food markets in the world. Here, a process is presented for the production of 8,800 MT of SCP per year using methanotrophic bacteria with methanol as the carbon source. To increase process profitability, the cells will be genetically engineered to produce astaxanthin, a carotenoid pigment found naturally in aquatic algae. When used as a feed supplement for farmed salmon, these SCP will serve as a nutritional additive and ensure that the salmon possess the pink pigmentation consumers expect. The final product is SCP with 0.3% by weight astaxanthin sold for 16,500/MT.Thehighmarketpriceofastaxanthinsignificantlyimprovestheprofitabilityoftheprocess.Accordingtoa10yearprofitabilityanalysis,thepredictedIRRis41.916,500/MT. The high market price of astaxanthin significantly improves the profitability of the process. According to a 10year profitability analysis, the predicted IRR is 41.9%. In 2020, the Net Present Value of the project will be 129,000,000. In the third production year, the ROI will be 56.0%

    Molecular development of a thermostable β-glucosidase for modification of natural products

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    The gene encoding a β-glucosidase originating from the extreme thermophile Thermotoga neopolitana has been cloned and expressed in Escherichia coli. The aim was to produce a thermostable enzyme that could be used to remove sugar residues from glucosylated natural products classified as flavonoids by applying a method combining extraction in hot pressurized water with enzymatic hydrolysis. The β-glucosidase (termed TnBgl1A in this thesis) is a member of family 1 of glycoside hydrolase (GH1). The enzyme has an apparent unfolding temperature of 101.9 °C and a molecular weight of 52.6 kDa. The activity of TnBgl1A was first analysed using the model substrate para-nitrophenyl-β-D-glucopyranoside (pNPGlc), demonstrating that a single glucosyl residue (typical for β-glucosidases) is released by this enzyme. Hydrolysis of glucosylated forms of the natural product quercetin (the major flavonoid in yellow onion) was found to be dependent on the position of the glucosylation (see below). Expression in E. coli resulted in a relatively large fraction of insoluble target protein. To improve the folding of TnBgl1A during production, different strategies were then applied: I) the gene was constructed synthetically and codons were optimized to match codon usage in E.coli, II) the gene was cloned in frame with a signal peptide to translocate the protein to the periplasmic space, and III) the gene was co-expressed with genes encoding molecular chaperones. Among these strategies the co-expression of the gene with chaperones worked best, and the improved folding resulted in an increased fraction of soluble, active enzyme. The TnBgl1A was tested for the hydrolysis of quercetin-glucosides, which are antioxidants classified as flavonoids present in onion, and composed of a polyphenolic backbone glucosylated at two different positions (Q3, Q4´, and the diglucoside Q3,4´). The aglycone form of quercetin is a more potent antioxidant than the glucosylated forms. The activity of TnBgl1A for Q3 was lower compared to its activity on quercetin-4´-glucoside (Q4´). To improve hydrolysis of Q3, mutations were introduced in the enzyme, based on a structure model of TnBgl1A. The mutant N221S/P342L showed increased efficiency towards Q3 as well as for Q4´ compared to wild-type. This showed that the position and nature of this residue in the active site is important for substrate specificity and that by careful selection the specificity can be changed for different substrates. Therefore, the mutation studies were extended and the active site region was targeted for further mutagenesis. Among the changes introduced, the mutagenesis of the neighbouring residue, N220S, was also found to influence activity, and this variant had a higher specific activity for quercetin-glucosides. TnBgl1A and one of the best performance mutants (N221S/P342L) were immobilized on acrylic support to allow recycling of the enzyme in experiments coupling hot water extraction of quercetin-glucosides with enzymatic hydrolysis. The activity of the immobilised enzyme was analysed in batch experiments using pNPGlc as model substrate and showed not only that the enzyme remained active after immobilisation, but that the thermal stability of the enzyme improved slightly. The effect of additives on immobilisation was studied and, as glucose is an activator for the enzyme, the addition of glucose during immobilisation resulted in a slight increase in the specific activity. In the case of bovine serum albumin (BSA), the original activity was recovered after three months of storage by incubation with BSA for 24 hours. In conclusion, the work in the present thesis shows that TnBgl1A is an effective biocatalyst for the conversion of quercetin-glucosides to quercetin. It was also found that, in the production step, more soluble enzymes can be obtained if they are co-expressed with chaperones. The specificity of the enzyme can be changed by changing a single amino acid in the active site, and the improved hydrolysis of Q3 found for two single mutants (N220S and N221S) is caused by indirect changes in interactions, which may lead to a better fit of the quercetin backbone in the active site. As the modification of amino acids in the active site requires a deep understanding of the structure, it is hoped that the results reported here can contribute to the creation of, new mutants with better activity guided by predictions based on the current results. In this research, we have shown that both free and immobilised enzyme can be coupled to the hot water extraction process and that the immobilised enzyme can be used in an on-line process for hydrolysis of flavonoid glucosides. Further improvements can also be made in such combined processes, both concerning the conditions for the extraction and hydrolysis

    Methods in Computational Biology

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    Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen
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