OEB 192 – 09.11.09 Optimality & evolution of networks.

10
OEB 192 – 09.11.0 Optimality & evolution of networks
  • date post

    21-Dec-2015
  • Category

    Documents

  • view

    223
  • download

    7

Transcript of OEB 192 – 09.11.09 Optimality & evolution of networks.

Page 1: OEB 192 – 09.11.09 Optimality & evolution of networks.

OEB 192 – 09.11.09

Optimality & evolution of networks

Page 2: OEB 192 – 09.11.09 Optimality & evolution of networks.
Page 3: OEB 192 – 09.11.09 Optimality & evolution of networks.
Page 4: OEB 192 – 09.11.09 Optimality & evolution of networks.

Extroverts vs Introverts

Page 5: OEB 192 – 09.11.09 Optimality & evolution of networks.
Page 6: OEB 192 – 09.11.09 Optimality & evolution of networks.
Page 7: OEB 192 – 09.11.09 Optimality & evolution of networks.
Page 8: OEB 192 – 09.11.09 Optimality & evolution of networks.

*Avida projects due 11/16 Monday (11/16):

Epistasis

Modular epistasis in yeast metabolismDaniel Segrè, Alexander DeLuna, George M. Church, Roy Kishony

*We will exchange Beaumont et al. (2009) in place of Ackermann et al. (2008) for 11/18*

Page 9: OEB 192 – 09.11.09 Optimality & evolution of networks.

Upcoming talks

Thursday, 11/12Michael McInerneyUniversity of Oklahoma

“Teamwork in Anaerobic Metabolism: Syntrophy and Interspecies Metabolic Interactions”

Thursday Nov. 12th @ 6PMLocation: Center for the Environment (Rm 310)24 Oxford St, Harvard University, Cambridge Please join us for a wine and cheese reception at 5:30 pm - MSI Seminar Schedule: http://www.msi.harvard.edu/thursdays.html- Directions to 24 Oxford St: http://www.msi.harvard.edu/ov_dir.html

Page 10: OEB 192 – 09.11.09 Optimality & evolution of networks.

Friday, 11/20

From genes to physiology via metabolism20 November 2009, 12:00 pm, Warren Alpert 563 (HMS)

Frederik NijhoutDepartment of BiologyDuke University

Michael ReedDepartment of MathematicsDuke University

AbstractMetabolic networks are typically analyzed under the assumption that they operate at steady-state. System properties such as control coefficients and robustness are studied by small perturbations of the steady-state, and the evolution of such networks is assumed to be driven by maximization or optimization of steady-state fluxes. Nothing could be farther from reality. In life, metabolic systems are subject to continuous and large fluctuations in input and demand, and the expression levels of many genes vary dramatically during the day. We will discuss our studies on the dynamics of metabolic networks, concentrating on (1) folate and glutathione metabolism, and (2) dopamine and serotonin metabolism. Our studies show that stability and robustness in metabolic networks is due to dynamically active homeostatic mechanisms, rather than passive establishment of a steady-state. Similar non-steady-state processes undoubtedly operate in genetic networks and signaling networks as well.For more details and papers see metabolism.math.duke.edu/