A paradigm shift

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A paradigm shift

• Reductionism ? Integration
• System properties are determined by concentration
  of each component and reaction rates even with
  steady state assumptions still a complex issue
• Model systems
• Signal transduction
• Metabolism

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Biological signaling occurs at multiple levels

• Intracellular signaling complexity results from
• Interactions between pathways
• Compartmentalization
• Signal channeling

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Compartments

• Many signaling components are membrane-bound, and
  there is a distinct dearth in our understanding
  of membrane biochemistry.
• Still, it has been readily identified that cells
  use compartments to derive specific
  microenvironments, which can offer distinct
  responses to the same signals
• Look at compartments as wires or appliances

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Reaction channeling

• Central tenet of metabolism
• Compartments communicate via transporters
• Consider transporters as switches (?) controlling
  the flow of signals down gradients
• An intersection between cell biology and
  biochemistry

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(No Transcript)
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Fatty acids are activated and transported into
the mitochondria
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Transduction by carnitine is the major regulatory
point of fatty acid oxidation
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Molecular scaffolds

• Once considered the function of rRNA
• Term used for a new class of signaling proteins
  that do not have information transfer capability
  of their own but interact with multiple signaling
  proteins in a pathway

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• The scaffold provides an assembly line along
  which a series of enzymes process their
  substrates in a well-defined sequence and with an
  efficiency and specificity that are orders of
  magnitude higher than would be possible.
  Everyone look up the MAP kinase example (Ref. 13)

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Approaches to the complexity issue

• Development of signaling databases (ie. BIND)
• Systematic cataloging of proteins, lipids,
  sugars, and other signaling molecules together
  with genomic data of model systems

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An example in modeling metabolic phenomics

• It is now clear that we need to develop creative
  approaches and technologies to use all of this
  information genomics and proteomics to explore
  and determine genome function. We must
  essentially take on the view of a gene that we
  began with over 50 years ago, wherin the focus
  was on the functional attributes of a gene within
  the context of the whole organism.

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FBA instead of MCA

• Derived a stoichiometric matrix of number of
  metabolites vs. number of fluxes occurring in
  network
• Distinguish between metabolic reaction fluxes and
  transport fluxes use both dynamic and steady
  state analysis
• In silico E. coli

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Surprise!

• Even when multiple knockouts are generated, a
  surprising number of mutants result in no effect
  on growth.
• Flexibility in metabolic genotype rerouting of
  metabolites
• Clear example given by PK knockout in E. coli

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Yet, some metabolic modeling and engineering
successes

• Prediction and correlation of defined growth
  media
• Glucose transporter confers heterotrophic growth
  upon a photosynthetic algae