Systems Biology of Macrophages

  • Mano Ram Maurya
  • Christopher Benner
  • Sylvain Pradervand
  • Christopher Glass
  • Shankar Subramaniam
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 598)


Cells and tissues function in context. Under a given growth or survival medium they perform tasks, replicate and die. Given a stimulus they respond by invoking myriad biomolecular networks that result in a specified cellular outcome. At any given instant it can be argued that the cell is in a “state” defined by its components – their concentrations and locations, the interactions between components – that are modulated in space and time, and the complex circuitry – that involves a large number of interacting networks and a snapshot of the dynamical processes – such as gene expression, cell cycle, transport of components, etc. At present, we can measure, using high and low throughput methods, several cellular components in a context-dependent manner and obtain a partial picture of cellular networks and dynamical processes. Are these measurements sufficient to answer important biological questions and help reconstruct a systems-level understanding of a mammalian cell? This chapter will address systems biology strategies developed to address this question and demonstrate the power of integration of diverse cellular data for answering interesting biological questions in macrophages. We will use this systems biology approach to address the following questions: (1) How good are macrophage cell lines in addressing phenotypic biology of primary macrophages? (2) How do signals associated with inflammatory molecules regulate gene transcription in macrophages? (3) How can we combine proteomic and other cellular measurements to characterize the repertoire of upstream signaling networks invoked by macrophages? (4) How do designed knockdowns of proteins influence cellular phenotypes?


Embryonic Stem System Biology Principal Component Regression Bone Marrow Derive Macrophage Primary Macrophage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Mano Ram Maurya
    • 1
  • Christopher Benner
    • 2
  • Sylvain Pradervand
    • 1
  • Christopher Glass
    • 2
    • 3
  • Shankar Subramaniam
    • 1
    • 2
  1. 1.Department of BioengineeringUniversity of California at San DiegoLa Jolla
  2. 2.Graduate Program in Bioinformatics and Systems BiologyUniversity of California at San DiegoLa Jolla
  3. 3.Department of Cellular and Molecular MedicineUniversity of California at San DiegoLa Jolla

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