762 research outputs found
Genomic stuff: Governing the (im)matter of life
Emphasizing the context of what has often been referred to as âscarce natural resourcesâ, in particular forests, meadows, and fishing stocks, Elinor Ostromâs important work Governing the commons (1990) presents an institutional framework for discussing the development and use of collective action with respect to environmental problems. In this article we discuss extensions of Ostromâs approach to genes and genomes and explore its limits and usefulness. With the new genetics, we suggest, the biological gaze has not only been turned inward to the management and mining of the human body, also the very notion of the âbiologicalâ has been destabilized. This shift and destabilization, we argue, which is the result of human refashioning and appropriation of âlife itselfâ, raises important questions about the relevance and applicability of Ostromâs institutional framework in the context of what we call âgenomic stuffâ, genomic material, data, and information
Associations of iron status with dietary and other factors in 6-year-old children
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The Regularizing Capacity of Metabolic Networks
Despite their topological complexity almost all functional properties of
metabolic networks can be derived from steady-state dynamics. Indeed, many
theoretical investigations (like flux-balance analysis) rely on extracting
function from steady states. This leads to the interesting question, how
metabolic networks avoid complex dynamics and maintain a steady-state behavior.
Here, we expose metabolic network topologies to binary dynamics generated by
simple local rules. We find that the networks' response is highly specific:
Complex dynamics are systematically reduced on metabolic networks compared to
randomized networks with identical degree sequences. Already small topological
modifications substantially enhance the capacity of a network to host complex
dynamic behavior and thus reduce its regularizing potential. This exceptionally
pronounced regularization of dynamics encoded in the topology may explain, why
steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure
Culture perfusion schedules influence the metabolic activity and granulocyte-macrophage colony-stimulating factor production rates of human bone marrow stromal cells
The metabolic function and GM-CSF production rates of adherent human bone marrow stromal cells were investigated as functions of medium and serum feeding rates. A range of medium exchange schedules was studied, ranging from a typical Dexter culture protocol of one weekly medium exchange to a full media exchange daily, which more closely approximates what bone marrow cells experience in situ. Glucose consumption was found to be significantly higher at full daily exchange rate than at any other exchange schedule examined. However, the lactate yield on glucose was a constant, at 1.8 mol/mol, under all conditions considered. Differential serum vs. medium exchange experiment showed that both serum supply and medium nutrients were responsible for the altered behavior at high exchange rates. Glutamine consumption was found to be insignificant under all culture conditions examined. A change in exchange schedule from 50% daily medium exchange to full daily medium exchange after 14 days of culture was found to result in a transient production of GM-CSF and a change in metabolic behavior to resemble that of cultures which had full daily exchange from day one. These results suggest that both stromal cell metabolism and GM-CSF production are sensitive to medium exchange schedules. Taken together, the data presented indicate that attempts to model the function of human bone marrow in vitro may be well served by beginning with medium exchange schedules that more closely mimic the in vivo physiologic state of bone marrow.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49880/1/1041470221_ftp.pd
Quantifying the connectivity of a network: The network correlation function method
Networks are useful for describing systems of interacting objects, where the
nodes represent the objects and the edges represent the interactions between
them. The applications include chemical and metabolic systems, food webs as
well as social networks. Lately, it was found that many of these networks
display some common topological features, such as high clustering, small
average path length (small world networks) and a power-law degree distribution
(scale free networks). The topological features of a network are commonly
related to the network's functionality. However, the topology alone does not
account for the nature of the interactions in the network and their strength.
Here we introduce a method for evaluating the correlations between pairs of
nodes in the network. These correlations depend both on the topology and on the
functionality of the network. A network with high connectivity displays strong
correlations between its interacting nodes and thus features small-world
functionality. We quantify the correlations between all pairs of nodes in the
network, and express them as matrix elements in the correlation matrix. From
this information one can plot the correlation function for the network and to
extract the correlation length. The connectivity of a network is then defined
as the ratio between this correlation length and the average path length of the
network. Using this method we distinguish between a topological small world and
a functional small world, where the latter is characterized by long range
correlations and high connectivity. Clearly, networks which share the same
topology, may have different connectivities, based on the nature and strength
of their interactions. The method is demonstrated on metabolic networks, but
can be readily generalized to other types of networks.Comment: 10 figure
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