9,349 research outputs found
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
Automation on the generation of genome scale metabolic models
Background: Nowadays, the reconstruction of genome scale metabolic models is
a non-automatized and interactive process based on decision taking. This
lengthy process usually requires a full year of one person's work in order to
satisfactory collect, analyze and validate the list of all metabolic reactions
present in a specific organism. In order to write this list, one manually has
to go through a huge amount of genomic, metabolomic and physiological
information. Currently, there is no optimal algorithm that allows one to
automatically go through all this information and generate the models taking
into account probabilistic criteria of unicity and completeness that a
biologist would consider. Results: This work presents the automation of a
methodology for the reconstruction of genome scale metabolic models for any
organism. The methodology that follows is the automatized version of the steps
implemented manually for the reconstruction of the genome scale metabolic model
of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for
the reconstruction are implemented in a computational platform (COPABI) that
generates the models from the probabilistic algorithms that have been
developed. Conclusions: For validation of the developed algorithm robustness,
the metabolic models of several organisms generated by the platform have been
studied together with published models that have been manually curated. Network
properties of the models like connectivity and average shortest mean path of
the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table
MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models
Metabolomic data sets provide a direct read-out of cellular phenotypes and
are increasingly generated to study biological questions. Our previous work
revealed the potential of analyzing extracellular metabolomic data in the
context of the metabolic model using constraint-based modeling. Through this
work, which consists of a protocol, a toolbox, and tutorials of two use cases,
we make our methods available to the broader scientific community. The protocol
describes, in a step-wise manner, the workflow of data integration and
computational analysis. The MetaboTools comprise the Matlab code required to
complete the workflow described in the protocol. Tutorials explain the
computational steps for integration of two different data sets and demonstrate
a comprehensive set of methods for the computational analysis of metabolic
models and stratification thereof into different phenotypes. The presented
workflow supports integrative analysis of multiple omics data sets.
Importantly, all analysis tools can be applied to metabolic models without
performing the entire workflow. Taken together, this protocol constitutes a
comprehensive guide to the intra-model analysis of extracellular metabolomic
data and a resource offering a broad set of computational analysis tools for a
wide biomedical and non-biomedical research community
Conditions for duality between fluxes and concentrations in biochemical networks
Mathematical and computational modelling of biochemical networks is often
done in terms of either the concentrations of molecular species or the fluxes
of biochemical reactions. When is mathematical modelling from either
perspective equivalent to the other? Mathematical duality translates concepts,
theorems or mathematical structures into other concepts, theorems or
structures, in a one-to-one manner. We present a novel stoichiometric condition
that is necessary and sufficient for duality between unidirectional fluxes and
concentrations. Our numerical experiments, with computational models derived
from a range of genome-scale biochemical networks, suggest that this
flux-concentration duality is a pervasive property of biochemical networks. We
also provide a combinatorial characterisation that is sufficient to ensure
flux-concentration duality. That is, for every two disjoint sets of molecular
species, there is at least one reaction complex that involves species from only
one of the two sets. When unidirectional fluxes and molecular species
concentrations are dual vectors, this implies that the behaviour of the
corresponding biochemical network can be described entirely in terms of either
concentrations or unidirectional fluxes
Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling
We propose a method for finding metabolic parameters of cells, organs and
whole organisms, which is based on the earlier discovered general growth law.
Based on the obtained results and analysis of available biological models, we
propose a general framework for modeling biological phenomena and discuss how
it can be used in Virtual Liver Network project. The foundational idea of the
study is that growth of cells, organs, systems and whole organisms, besides
biomolecular machinery, is influenced by biophysical mechanisms acting at
different scale levels. In particular, the general growth law uniquely defines
distribution of nutritional resources between maintenance needs and biomass
synthesis at each phase of growth and at each scale level. We exemplify the
approach considering metabolic properties of growing human and dog livers and
liver transplants. A procedure for verification of obtained results has been
introduced too. We found that two examined dogs have high metabolic rates
consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per
day, and verified this using the proposed verification procedure. We also
evaluated consumption rate of nutrients in human livers, determining it to be
about 0.088 gram of nutrients per cubic centimeter of liver per day for males,
and about 0.098 for females. This noticeable difference can be explained by
evolutionary development, which required females to have greater liver
processing capacity to support pregnancy. We also found how much nutrients go
to biomass synthesis and maintenance at each phase of liver and liver
transplant growth. Obtained results demonstrate that the proposed approach can
be used for finding metabolic characteristics of cells, organs, and whole
organisms, which can further serve as important inputs for many applications in
biology (protein expression), biotechnology (synthesis of substances), and
medicine.Comment: 20 pages, 6 figures, 4 table
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