39,165 research outputs found
Consistency analysis of metabolic correlation networks
<p>Abstract</p> <p>Background</p> <p>Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network).</p> <p>Results</p> <p>Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network.</p> <p>Conclusion</p> <p>The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles.</p
Evaluation of rate law approximations in bottom-up kinetic models of metabolism.
BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches
Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding
The uniform sampling of convex polytopes is an interesting computational
problem with many applications in inference from linear constraints, but the
performances of sampling algorithms can be affected by ill-conditioning. This
is the case of inferring the feasible steady states in models of metabolic
networks, since they can show heterogeneous time scales . In this work we focus
on rounding procedures based on building an ellipsoid that closely matches the
sampling space, that can be used to define an efficient hit-and-run (HR) Markov
Chain Monte Carlo. In this way the uniformity of the sampling of the convex
space of interest is rigorously guaranteed, at odds with non markovian methods.
We analyze and compare three rounding methods in order to sample the feasible
steady states of metabolic networks of three models of growing size up to
genomic scale. The first is based on principal component analysis (PCA), the
second on linear programming (LP) and finally we employ the lovasz ellipsoid
method (LEM). Our results show that a rounding procedure is mandatory for the
application of the HR in these inference problem and suggest that a combination
of LEM or LP with a subsequent PCA perform the best. We finally compare the
distributions of the HR with that of two heuristics based on the Artificially
Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good
agreement with the results of the HR for the small network, while on genome
scale models present inconsistencies.Comment: Replacement with major revision
Extracting the hierarchical organization of complex systems
Extracting understanding from the growing ``sea'' of biological and
socio-economic data is one of the most pressing scientific challenges facing
us. Here, we introduce and validate an unsupervised method that is able to
accurately extract the hierarchical organization of complex biological, social,
and technological networks. We define an ensemble of hierarchically nested
random graphs, which we use to validate the method. We then apply our method to
real-world networks, including the air-transportation network, an electronic
circuit, an email exchange network, and metabolic networks. We find that our
method enables us to obtain an accurate multi-scale descriptions of a complex
system.Comment: Figures in screen resolution. Version with full resolution figures
available at
http://amaral.chem-eng.northwestern.edu/Publications/Papers/sales-pardo-2007.pd
The relationship between metabolic rate and sociability is altered by food-deprivation
Individuals vary in the extent to which they associate with conspecifics, but little is known about the energetic underpinnings of this variation in sociability. Group-living allows individuals to find food more consistently, but within groups, there can be competition for food items. Individuals with an increased metabolic rate could display decreased sociability to reduce competition. Long-term food deprivation (FD) may alter any links between sociability and metabolic rate by affecting motivation to find food.
We examined these issues in juvenile qingbo carp Spinibarbus sinensis, to understand how FD and metabolic rate affect sociability. Like many aquatic ectotherms, this species experiences seasonal bouts of FD.
Individuals were either: (i) food-deprived for 21 days; or (ii) fed a maintenance ration (control). Fish from each treatment were measured for standard metabolic rate (SMR) and tested for sociability twice: once in the presence of a control stimulus shoal and once with a food-deprived stimulus shoal.
Control individuals ventured further from stimulus shoals over a 30-min trial, while food-deprived fish did not change their distance from stimulus shoals as trials progressed. Control fish with a higher SMR were least sociable. Well-fed controls showed decreased sociability when exposed to food-deprived stimulus shoals, but there was evidence of consistency in relative sociability between exposures to different shoal types.
Results contrast with previous findings that several days of fasting causes individuals to decrease associations with conspecifics. Prolonged FD may cause individuals to highly prioritize food acquisition, and the decreased vigilance that would accompany continuous foraging may heighten the need for the antipredator benefits of shoaling. Conversely, decreased sociability in well-fed fish with a high SMR probably minimizes intraspecific competition, allowing them to satisfy an increased energetic demand while foraging. Together, these results suggest that FD – a challenge common for many ectothermic species – can affect individual sociability as well as the attractiveness of groups towards conspecifics. In addition, the lack of a link between SMR and sociability in food-deprived fish suggests that, in situations where group membership is linked to fitness, the extent of correlated selection on metabolic traits may be context-dependent
On the effects of alternative optima in context-specific metabolic model predictions
Recent methodological developments have facilitated the integration of
high-throughput data into genome-scale models to obtain context-specific
metabolic reconstructions. A unique solution to this data integration problem
often may not be guaranteed, leading to a multitude of context-specific
predictions equally concordant with the integrated data. Yet, little attention
has been paid to the alternative optima resulting from the integration of
context-specific data. Here we present computational approaches to analyze
alternative optima for different context-specific data integration instances.
By using these approaches on metabolic reconstructions for the leaf of
Arabidopsis thaliana and the human liver, we show that the analysis of
alternative optima is key to adequately evaluating the specificity of the
predictions in particular cellular contexts. While we provide several ways to
reduce the ambiguity in the context-specific predictions, our findings indicate
that the existence of alternative optimal solutions warrant caution in detailed
context-specific analyses of metabolism
The specificity and robustness of long-distance connections in weighted, interareal connectomes
Brain areas' functional repertoires are shaped by their incoming and outgoing
structural connections. In empirically measured networks, most connections are
short, reflecting spatial and energetic constraints. Nonetheless, a small
number of connections span long distances, consistent with the notion that the
functionality of these connections must outweigh their cost. While the precise
function of these long-distance connections is not known, the leading
hypothesis is that they act to reduce the topological distance between brain
areas and facilitate efficient interareal communication. However, this
hypothesis implies a non-specificity of long-distance connections that we
contend is unlikely. Instead, we propose that long-distance connections serve
to diversify brain areas' inputs and outputs, thereby promoting complex
dynamics. Through analysis of five interareal network datasets, we show that
long-distance connections play only minor roles in reducing average interareal
topological distance. In contrast, areas' long-distance and short-range
neighbors exhibit marked differences in their connectivity profiles, suggesting
that long-distance connections enhance dissimilarity between regional inputs
and outputs. Next, we show that -- in isolation -- areas' long-distance
connectivity profiles exhibit non-random levels of similarity, suggesting that
the communication pathways formed by long connections exhibit redundancies that
may serve to promote robustness. Finally, we use a linearization of
Wilson-Cowan dynamics to simulate the covariance structure of neural activity
and show that in the absence of long-distance connections, a common measure of
functional diversity decreases. Collectively, our findings suggest that
long-distance connections are necessary for supporting diverse and complex
brain dynamics.Comment: 18 pages, 8 figure
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