16,988 research outputs found
Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis
The genetic regulatory network (GRN) plays a key role in controlling the
response of the cell to changes in the environment. Although the structure of
GRNs has been the subject of many studies, their large scale structure in the
light of feedbacks from the metabolic network (MN) has received relatively
little attention. Here we study the causal structure of the GRNs, namely the
chain of influence of one component on the other, taking into account feedback
from the MN. First we consider the GRNs of E. coli and B. subtilis without
feedback from MN and illustrate their causal structure. Next we augment the
GRNs with feedback from their respective MNs by including (a) links from genes
coding for enzymes to metabolites produced or consumed in reactions catalyzed
by those enzymes and (b) links from metabolites to genes coding for
transcription factors whose transcriptional activity the metabolites alter by
binding to them. We find that the inclusion of feedback from MN into GRN
significantly affects its causal structure, in particular the number of levels
and relative positions of nodes in the hierarchy, and the number and size of
the strongly connected components (SCCs). We then study the functional
significance of the SCCs. For this we identify condition specific feedbacks
from the MN into the GRN by retaining only those enzymes that are essential for
growth in specific environmental conditions simulated via the technique of flux
balance analysis (FBA). We find that the SCCs of the GRN augmented by these
feedbacks can be ascribed specific functional roles in the organism. Our
algorithmic approach thus reveals relatively autonomous subsystems with
specific functionality, or regulatory modules in the organism. This automated
approach could be useful in identifying biologically relevant modules in other
organisms for which network data is available, but whose biology is less well
studied.Comment: 15 figure
Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis
We unravel how functional plasticity and redundancy are essential mechanisms
underlying the ability to survive of metabolic networks. We perform an
exhaustive computational screening of synthetic lethal reaction pairs in
Escherichia coli in a minimal medium and we find that synthetic lethal pairs
divide in two different groups depending on whether the synthetic lethal
interaction works as a backup or as a parallel use mechanism, the first
corresponding to essential plasticity and the second to essential redundancy.
In E. coli, the analysis of pathways entanglement through essential redundancy
supports the view that synthetic lethality affects preferentially a single
function or pathway. In contrast, essential plasticity, the dominant class,
tends to be inter-pathway but strongly localized and unveils Cell Envelope
Biosynthesis as an essential backup for Membrane Lipid Metabolism. When
comparing E. coli and Mycoplasma pneumoniae, we find that the metabolic
networks of the two organisms exhibit a large difference in the relative
importance of plasticity and redundancy which is consistent with the conjecture
that plasticity is a sophisticated mechanism that requires a complex
organization. Finally, coessential reaction pairs are explored in different
environmental conditions to uncover the interplay between the two mechanisms.
We find that synthetic lethal interactions and their classification in
plasticity and redundancy are basically insensitive to medium composition, and
are highly conserved even when the environment is enriched with nonessential
compounds or overconstrained to decrease maximum biomass formation.Comment: 22 pages, 4 figure
Estimating the size of the solution space of metabolic networks
In this work we propose a novel algorithmic strategy that allows for an
efficient characterization of the whole set of stable fluxes compatible with
the metabolic constraints. The algorithm, based on the well-known Bethe
approximation, allows the computation in polynomial time of the volume of a non
full-dimensional convex polytope in high dimensions. The result of our
algorithm match closely the prediction of Monte Carlo based estimations of the
flux distributions of the Red Blood Cell metabolic network but in incomparably
shorter time. We also analyze the statistical properties of the average fluxes
of the reactions in the E-Coli metabolic network and finally to test the effect
of gene knock-outs on the size of the solution space of the E-Coli central
metabolism.Comment: 8 pages, 7 pdf figure
Organising metabolic networks: cycles in flux distributions
Metabolic networks are among the most widely studied biological systems. The topology and interconnections of metabolic reactions have been well described for many species, but are not sufficient to understand how their activity is regulated in living organisms. The principles directing the dynamic organisation of reaction fluxes remain poorly understood. Cyclic structures are thought to play a central role in the homeostasis of biological systems and in their resilience to a changing environment. In this work, we investigate the role of fluxes of matter cycling in metabolic networks. First, we introduce a methodology for the computation of cyclic and acyclic fluxes in metabolic networks, adapted from an algorithm initially developed to study cyclic fluxes in trophic networks. Subsequently, we apply this methodology to the analysis of three metabolic systems, including the central metabolism of wild type and a deletion mutant of Escherichia coli, erythrocyte metabolism and the central metabolism of the bacterium Methylobacterium extorquens. The role of cycles in driving and maintaining the performance of metabolic functions upon perturbations is unveiled through these examples. This methodology may be used to further investigate the role of cycles in living organisms, their pro-activity and organisational invariance, leading to a better understanding of biological entailment and information processing
Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle
Cancer cells utilize large amounts of ATP to sustain growth, relying primarily on non-oxidative,
fermentative pathways for its production. In many types of cancers this leads, even in the presence
of oxygen, to the secretion of carbon equivalents (usually in the form of lactate) in the cell’s
surroundings, a feature known as the Warburg effect. While the molecular basis of this phenomenon
are still to be elucidated, it is clear that the spilling of energy resources contributes to creating a
peculiar microenvironment for tumors, possibly characterized by a degree of toxicity. This suggests
that mechanisms for recycling the fermentation products (e.g. a lactate shuttle) may be active,
effectively inducing a mutually beneficial metabolic coupling between aberrant and non-aberrant
cells. Here we analyze this scenario through a large-scale in silico metabolic model of interacting
human cells. By going beyond the cell-autonomous description, we show that elementary physico-
chemical constraints indeed favor the establishment of such a coupling under very broad conditions.
The characterization we obtained by tuning the aberrant cell’s demand for ATP, amino-acids and
fatty acids and/or the imbalance in nutrient partitioning provides quantitative support to the idea
that synergistic multi-cell effects play a central role in cancer sustainmen
Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities
Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio
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