@ARTICLE{essays_biochemistry,
   AUTHOR       = {E.D. Sontag},
   JOURNAL      = {Essays in Biochemistry},
   TITLE        = {Network reconstruction based on steady-state data},
   YEAR         = {2008},
   OPTMONTH     = {},
   OPTNOTE      = {},
   OPTNUMBER    = {},
   PAGES        = {161-176},
   VOLUME       = {45},
   KEYWORDS     = {systems biology, biochemical networks, 
      gene and protein networks, reverse engineering, 
      systems identification},
   PUBLISHER    = {Portland Press},
   PDF          = {../../FTPDIR/unravel_essays_biochem_reprint.pdf},
   ABSTRACT     = { The ``reverse engineering problem'' in systems biology 
      is that of unraveling of the web of interactions among the components 
      of protein and gene regulatory networks, so as to map out the direct 
      or local interactions among components. These direct interactions 
      capture the topology of the functional network. An intrinsic 
      difficulty in capturing these direct interactions, at least in intact 
      cells, is that any perturbation to a particular gene or signaling 
      component may rapidly propagate throughout the network, thus causing 
      global changes which cannot be easily distinguished from direct 
      effects. Thus, a major goal in reverse engineering is to use these 
      observed global responses - such as steady-state changes in 
      concentrations of active proteins, mRNA levels, or transcription 
      rates - in order to infer the local interactions between individual 
      nodes. One approach to solving this global-to-local problem is the 
      ``Modular Response Analysis'' (MRA) method proposed in work of the 
      author with Kholodenko et. al. (PNAS, 2002) and further elaborated in 
      other papers. The basic method deals only with steady-state data. 
      However, recently, quasi-steady state MRA has been used by Santos et. 
      al. (Nature Cell Biology, 2007) for quantifying positive and negative 
      feedback effects in the Raf/Mek/Erk MAPK network in rat adrenal 
      pheochromocytoma (PC-12) cells. This paper presents an overview of 
      the MRA technique, as well as a generalization of the algorithm to 
      that quasi-steady state case. }
}

{"_id":"GsBi2Dv5KvtFBsjzP","bibbaseid":"sontag-networkreconstructionbasedonsteadystatedata-2008","downloads":0,"creationDate":"2018-10-18T05:07:06.098Z","title":"Network reconstruction based on steady-state data","author_short":["Sontag, E."],"year":2008,"bibtype":"article","biburl":"http://www.sontaglab.org/PUBDIR/Biblio/complete-bibliography.bib","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["E.D."],"propositions":[],"lastnames":["Sontag"],"suffixes":[]}],"journal":"Essays in Biochemistry","title":"Network reconstruction based on steady-state data","year":"2008","optmonth":"","optnote":"","optnumber":"","pages":"161-176","volume":"45","keywords":"systems biology, biochemical networks, gene and protein networks, reverse engineering, systems identification","publisher":"Portland Press","pdf":"../../FTPDIR/unravel_essays_biochem_reprint.pdf","abstract":"The ``reverse engineering problem'' in systems biology is that of unraveling of the web of interactions among the components of protein and gene regulatory networks, so as to map out the direct or local interactions among components. These direct interactions capture the topology of the functional network. An intrinsic difficulty in capturing these direct interactions, at least in intact cells, is that any perturbation to a particular gene or signaling component may rapidly propagate throughout the network, thus causing global changes which cannot be easily distinguished from direct effects. Thus, a major goal in reverse engineering is to use these observed global responses - such as steady-state changes in concentrations of active proteins, mRNA levels, or transcription rates - in order to infer the local interactions between individual nodes. One approach to solving this global-to-local problem is the ``Modular Response Analysis'' (MRA) method proposed in work of the author with Kholodenko et. al. (PNAS, 2002) and further elaborated in other papers. The basic method deals only with steady-state data. However, recently, quasi-steady state MRA has been used by Santos et. al. (Nature Cell Biology, 2007) for quantifying positive and negative feedback effects in the Raf/Mek/Erk MAPK network in rat adrenal pheochromocytoma (PC-12) cells. This paper presents an overview of the MRA technique, as well as a generalization of the algorithm to that quasi-steady state case. ","bibtex":"@ARTICLE{essays_biochemistry,\n AUTHOR = {E.D. Sontag},\n JOURNAL = {Essays in Biochemistry},\n TITLE = {Network reconstruction based on steady-state data},\n YEAR = {2008},\n OPTMONTH = {},\n OPTNOTE = {},\n OPTNUMBER = {},\n PAGES = {161-176},\n VOLUME = {45},\n KEYWORDS = {systems biology, biochemical networks, \n gene and protein networks, reverse engineering, \n systems identification},\n PUBLISHER = {Portland Press},\n PDF = {../../FTPDIR/unravel_essays_biochem_reprint.pdf},\n ABSTRACT = { The ``reverse engineering problem'' in systems biology \n is that of unraveling of the web of interactions among the components \n of protein and gene regulatory networks, so as to map out the direct \n or local interactions among components. These direct interactions \n capture the topology of the functional network. An intrinsic \n difficulty in capturing these direct interactions, at least in intact \n cells, is that any perturbation to a particular gene or signaling \n component may rapidly propagate throughout the network, thus causing \n global changes which cannot be easily distinguished from direct \n effects. Thus, a major goal in reverse engineering is to use these \n observed global responses - such as steady-state changes in \n concentrations of active proteins, mRNA levels, or transcription \n rates - in order to infer the local interactions between individual \n nodes. One approach to solving this global-to-local problem is the \n ``Modular Response Analysis'' (MRA) method proposed in work of the \n author with Kholodenko et. al. (PNAS, 2002) and further elaborated in \n other papers. The basic method deals only with steady-state data. \n However, recently, quasi-steady state MRA has been used by Santos et. \n al. (Nature Cell Biology, 2007) for quantifying positive and negative \n feedback effects in the Raf/Mek/Erk MAPK network in rat adrenal \n pheochromocytoma (PC-12) cells. This paper presents an overview of \n the MRA technique, as well as a generalization of the algorithm to \n that quasi-steady state case. }\n}\n\n","author_short":["Sontag, E."],"key":"essays_biochemistry","id":"essays_biochemistry","bibbaseid":"sontag-networkreconstructionbasedonsteadystatedata-2008","role":"author","urls":{},"keyword":["systems biology","biochemical networks","gene and protein networks","reverse engineering","systems identification"],"downloads":0,"html":""},"search_terms":["network","reconstruction","based","steady","state","data","sontag"],"keywords":["systems biology","biochemical networks","gene and protein networks","reverse engineering","systems identification"],"authorIDs":[],"dataSources":["DKqZbTmd7peqE4THw"]}