LEADER 06033nam 2200769Ia 450 001 9910972045503321 005 20250618135649.0 010 $a1-107-22024-6 010 $a1-107-08665-5 010 $a1-283-29614-4 010 $a9786613296146 010 $a1-139-12316-5 010 $a0-511-73617-7 010 $a1-139-11741-6 010 $a1-139-12807-8 010 $a1-139-11305-4 010 $a1-139-11524-3 035 $a(CKB)2550000000056560 035 $a(EBL)775115 035 $a(OCoLC)769341822 035 $a(SSID)ssj0000541735 035 $a(PQKBManifestationID)11368704 035 $a(PQKBTitleCode)TC0000541735 035 $a(PQKBWorkID)10514638 035 $a(PQKB)10920919 035 $a(UkCbUP)CR9780511736179 035 $a(MiAaPQ)EBC775115 035 $a(Au-PeEL)EBL775115 035 $a(CaPaEBR)ebr10502672 035 $a(CaONFJC)MIL329614 035 $a(PPN)261297872 035 $a(EXLCZ)992550000000056560 100 $a20101205d2011 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSystems biology $esimulation of dynamic network states /$fBernhard Palsson 205 $a1st ed. 210 $aCambridge, UK $cCambridge University Press$d2011 215 $a1 online resource (xiii, 317 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 08$a1-107-00159-5 320 $aIncludes bibliographical references (p. 306-313) and index. 327 $aCover; Title; Copyright; Contents; Preface; 1 Introduction; 1.1 Biological networks; 1.2 Why build and study models?; 1.3 Characterizing dynamic states; 1.4 Formulating dynamic network models; 1.5 The basic information is in a matrix format; 1.6 Studying dynamic models; 1.7 Summary; 2 Basic concepts; 2.1 Properties of dynamic states; 2.2 Primer on rate laws; 2.3 More on aggregate variables; 2.4 Time-scale decomposition; 2.5 Network structure versus dynamics; 2.6 Physico-chemical effects; 2.7 Summary; Part I Simulation of dynamic states; 3 Dynamic simulation: the basic procedure 327 $a3.1 Numerical solutions3.2 Graphically displaying the solution; 3.3 Post-processing the solution; 3.4 Demonstration of the simulation procedure; 3.5 Summary; 4 Chemical reactions; 4.1 Basic properties of reactions; 4.2 The reversible linear reaction; 4.3 The reversible bilinear reaction; 4.4 Connected reversible linear reactions; 4.5 Connected reversible bilinear reactions; 4.6 Summary; 5 Enzyme kinetics; 5.1 Enzyme catalysis; 5.2 Deriving enzymatic rate laws; 5.3 Michaelis-Menten kinetics; 5.4 Hill kinetics for enzyme regulation; 5.5 The symmetry model; 5.6 Scaling dynamic descriptions 327 $a5.7 Summary6 Open systems; 6.1 Basic concepts; 6.2 Reversible reaction in an open environment; 6.3 Michaelis-Menten kinetics in an open environment; 6.4 Summary; Part II Biological characteristics; 7 Orders of magnitude; 7.1 Cellular composition and ultra-structure; 7.2 Metabolism; 7.2.1 What are typical concentrations?; 7.2.2 What are typical metabolic fluxes?; 7.2.3 What are typical turnover times?; 7.2.4 What are typical power densities?; 7.3 Macromolecules; 7.3.1 What are typical characteristics of a genome?; 7.3.2 What are typical protein concentrations?; 7.3.3 What are typical fluxes? 327 $a7.3.4 What are typical turnover times?7.4 Cell growth and phenotypic functions; 7.4.1 What are typical cell-specific production rates?; 7.4.2 Balancing the fluxes and composition in an entire cell; 7.5 Summary; 8 Stoichiometric structure; 8.1 Bilinear biochemical reactions; 8.2 Bilinearity leads to a tangle of cycles; 8.3 Trafficking of high-energy phosphate bonds; 8.3.1 The basic structure of the ``core'' module; 8.3.2 Buffering the energy charge; 8.3.3 Open system: long-term adjustment of the capacity; 8.4 Charging and recovering high-energy bonds; 8.5 Summary 327 $a9 Regulation as elementary phenomena9.1 Regulation of enzymes; 9.2 Regulatory signals: phenomenology; 9.3 The effects of regulation on dynamic states; 9.4 Local regulation with Hill kinetics; 9.4.1 Inhibition; 9.4.2 Activation; 9.5 Feedback inhibition of pathways; 9.6 Increasing network complexity; 9.6.1 Regulation of protein synthesis; 9.6.2 Tight regulation of enzyme activity; 9.7 Summary; Part III Metabolism; 10 Glycolysis; 10.1 Glycolysis as a system; 10.2 The stoichiometric matrix; 10.3 Defining the steady state; 10.4 Simulating mass balances: biochemistry 327 $a10.5 Pooling: towards systems biology 330 $aBiophysical models have been used in biology for decades, but they have been limited in scope and size. In this book, Bernhard Ø. Palsson shows how network reconstructions that are based on genomic and bibliomic data, and take the form of established stoichiometric matrices, can be converted into dynamic models using metabolomic and fluxomic data. The Mass Action Stoichiometric Simulation (MASS) procedure can be used for any cellular process for which data is available and allows a scalable step-by-step approach to the practical construction of network models. Specifically, it can treat integrated processes that need explicit accounting of small molecules and protein, which allows simulation at the molecular level. The material has been class-tested by the author at both the undergraduate and graduate level. All computations in the text are available online in MATLAB and MATHEMATICA® workbooks, allowing hands-on practice with the material. 606 $aComputational biology 606 $aGenomics 606 $aBioinformatics 615 0$aComputational biology. 615 0$aGenomics. 615 0$aBioinformatics. 676 $a572.80285 700 $aPalsson$b Bernhard$0501629 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910972045503321 996 $aSystems biology$9726520 997 $aUNINA