Keywords:
Computational biology.
;
Electronic books.
Description / Table of Contents:
This textbook devoted to systems biology describes how to model networks, how to determine their properties, and how to relate these to phenotypic functions. The links between the mathematical ideas and biological processes are made clear, and the book reflects the irreversible trend of increasing mathematical content in biology education. Therefore to assist both teacher and student, Palsson provides problem sets on an associated website.
Type of Medium:
Online Resource
Pages:
1 online resource (336 pages)
Edition:
1st ed.
ISBN:
9780511146039
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=255015
DDC:
572.80285
Language:
English
Note:
Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Preface -- CHAPTER 1 Introduction -- 1.1 The Need for Systems Analysis in Biology -- Biological parts lists -- Beyond bioinformatics -- Genetic circuits -- 1.2 The Systems Biology Paradigm -- Systemic annotation -- Hierarchical thinking in systems biology -- Historical roots -- 1.3 About This Book -- Purpose -- Approach -- 1.4 Summary -- 1.5 Further Reading -- CHAPTER 2 Basic Concepts in Systems Biology -- 2.1 Components vs. Systems -- 2.2 Links and Functional States -- Links -- Functional states -- 2.3 Links to Networks -- 2.4 Constraining Allowable Functional States -- The constraints under which a cell operates -- Picking candidate states -- Hierarchical organization in biology -- 2.5 Summary -- 2.6 Further Reading -- PART ONE Reconstruction of Biochemical Networks -- CHAPTER 3 Metabolic Networks -- 3.1 Basic Features -- Hierarchy in function of metabolic networks -- 3.2 Reconstruction Methods -- Defining the reaction list -- Genome annotation -- Publicly available sources of sequence data -- Biochemical data -- Enzyme commission numbers -- Protein databases -- Gene-protein-reaction (GPR) associations -- Organism-specific sources of information -- Meeting demands and measured physiological states -- Reconciliation and curation -- Prospective design of experiments -- 3.3 Genome-scale Metabolic Reconstructions -- 3.4 Multiple Genome-scale Networks -- Common components -- Putting "content in context" -- Data types accounted for in a multinetwork reconstruction -- Regulation of metabolic networks -- Regulation of enzyme activity -- 3.5 Summary -- 3.6 Further Reading -- CHAPTER 4 Transcriptional Regulatory Networks -- 4.1 Basic Properties -- The lacoperon in Escherichia coli -- The GAL regulon in yeast -- Proteins that bind to DNA -- Fundamental building blocks.
,
Hierarchy in transcriptional regulatory networks -- 4.2 Reconstructing Regulatory Networks -- The magnitude of the task -- Three fundamental data types -- Top-down data types -- Bottom-up data types -- A combination of top-down and bottom-up methods is needed -- 4.3 Large-scale Reconstruction Efforts -- Cell cycle in Caulobacter -- Early development of the sea urchin -- Regulation of metabolism in E. coli -- Formal representation of regulatory networks -- 4.4 Summary -- 4.5 Further Reading -- CHAPTER 5 Signaling Networks -- 5.1 Basic Properties -- Steroids -- G-protein signaling -- The JAK-STAT network -- Families of signaling molecules and processes -- Fundamental building blocks -- Hierarchy in signaling networks -- 5.2 Reconstructing Signaling Networks -- Magnitude of the problem -- Combinatorial features -- Elements of reconstruction -- Level of detail in a reconstruction -- Data sources for reconstruction process -- Integration of data types -- Large-scale reconstruction efforts -- 5.3 Summary -- 5.4 Further Reading -- PART TWO Mathematical Representation of Reconstructed Networks -- CHAPTER 6 Basic Features of the Stoichiometric Matrix -- 6.1 S as a Linear Transformation -- Dynamic mass balances -- Dimensions -- The four fundamental subspaces -- The column and left null spaces -- The row and null spaces -- 6.2 S as a Connectivity Matrix -- 6.3 Elementary Biochemical Reactions -- Reversible conversion -- Bimolecular association -- A cofactor-coupled reaction -- 6.4 Linear and Nonlinear Maps -- 6.5 The Elemental Matrix -- Conserved quantities -- Nonconserved quantities -- Compounds as points in the elemental space -- Reaction vectors as connections between these points -- Chemical moieties -- Metabolic carrier molecules as conserved moieties -- Protein molecules as conserved moieties -- 6.6 Open and Closed Networks.
,
The total stoichiometric matrix -- The exchange stoichiometric matrix -- The internal stoichiometric matrix -- Example -- Partitioning Sint further -- Defining the system boundary -- 6.7 Summary -- 6.8 Further Reading -- CHAPTER 7 Topological Properties -- 7.1 The Binary Form of S -- S is a sparse matrix -- 7.2 Compound Participation and Connectivity -- Connectivities in genome-scale matrices -- Biological interpretation -- Node connectivity and network states -- 7.3 The Adjacency Matrices of S -- The reaction adjacency matrix Av -- The compound adjacency matrix, Ax -- 7.4 Computation of the Adjacency Matrices -- The reversible reaction -- The reversible bimolecular association reaction -- The reversible cofactor exchange reaction -- Genome-scale matrices -- 7.5 Summary -- 7.6 Further Reading -- CHAPTER 8 Fundamental Subspaces of S -- 8.1 Dimensions of the Fundamental Subspaces -- Contents of the fundamental subspaces -- Basis for vector spaces -- 8.2 The Basics of Singular Value Decomposition -- The singular value spectrum -- Orthonormal bases for the four fundamental subspaces -- Mapping between the singular vectors -- Mode-by-mode reconstruction of S -- A note on nomenclature -- SVD as a series of transformations -- 8.3 SVD of S for the Elementary Reactions -- Reversible conversion -- The finite size of the fundamental subspaces -- Numerical example -- Bilinear association -- Linear combinations of fluxes and concentrations -- Nonorthonormal basis vectors -- 8.4 Interpretation of SVD: Systemic Reactions -- Simple example -- Decomposition of genome-scale matrices -- 8.5 Summary -- 8.6 Further Reading -- CHAPTER 9 The (Right) Null Space of S -- 9.1 Definition -- 9.2 Choice of Basis -- Linear basis -- Nonnegative linear basis -- Convex versus linear bases -- Finite or closed spaces -- Illustrative examples -- The simple flux split.
,
Varying constraints and biological interpretation -- Some key concepts: Mathematics versus biology -- Perspective: From reactions to pathways -- 9.3 Extreme Pathways -- The flux cone -- Classification of the extreme pathways -- Simple reactions -- Skeleton metabolic pathways -- Large-scale networks -- Computing extreme pathways -- History of convex pathway vectors -- Contrasting elementary modes and extreme pathways -- 9.4 Summary -- 9.5 Further Reading -- CHAPTER 10 The Left Null Space of S -- 10.1 Definition -- 10.2 The Time Invariants -- The conservation relationships -- Pool sizes -- Classifying the pools -- Reference states -- 10.3 Single Reactions and Pool Formation -- Simple reversible reaction -- Bilinear association -- Carrier-coupled reaction -- Redox carrier coupled reactions -- 10.4 Multiple Reactions and Pool Formation -- Combining elementary reactions -- Multiple redox coupled reactions -- 10.5 Pool Formation in Classical Pathways -- Simplified glycolysis -- Simplified TCA cycle -- 10.6 Summary -- 10.7 Further Reading -- CHAPTER 11 The Row and Column Spaces of S -- 11.1 The Column Space -- The reaction vectors form the basis for the column space -- Simple examples -- 11.2 The Row Space -- A basis for the row space -- Constraints on the flux values -- Thermodynamic driving forces -- 11.3 Summary -- 11.4 Further Reading -- PART THREE Capabilities of Reconstructed Networks -- CHAPTER 12 Dual Causality -- 12.1 Causation in Physics and Biology -- Physics -- Biology -- Hierarchy -- 12.2 Model Building in Biology -- Limitations of theory-based modeling approaches -- Constraining behaviors -- Successive imposition of constraints -- Developing genome-scale models -- A limited analogy to the engineering design process -- Redundancy, multifunctionality, and noncausality -- 12.3 Models Can Drive Discovery -- Failure modes.
,
The iterative model building process -- In silico models as hypotheses -- Experimental designs to probe network functions -- 12.4 Constraints in Biology -- Physicochemical constraints -- Topobiological constraints -- Environmental constraints -- Regulatory constraints -- Mathematical representation of constraints: balances and bounds -- Illustrative example -- 12.5 Constraint-Based Analysis Methods -- 12.6 Summary -- 12.7 Further Reading -- CHAPTER 13 Properties of Solution Spaces -- 13.1 Network Properties -- The pathway matrix -- SVD of the pathway matrix -- Systems properties of interest -- Example systems -- 13.2 Pathway Length -- E. coli core network -- Genome-scale studies -- 13.3 Reaction Participation and Correlated Reaction Subsets -- Reaction participation in the JAK-STAT signaling network -- Genome-scale studies -- Correlated subsets -- CoSets in core E. coli metabolism -- Flux-coupling assessment through optimization -- 13.4 Input-Output Relationships -- The IOFA -- The IOFA for the core E. coli network -- Computing the number of identical input/output states in a genome-scale metabolic network -- 13.5 Crosstalk -- Definition -- Classifying crosstalk -- Crosstalk in the JAK-STAT signaling network -- 13.6 Regulation and Elimination of Pathways -- Regulation shrinks the solution space -- Skeleton representation of the core metabolic pathways -- Growth on two carbon sources (C1 and C2) and oxygen (O2) -- Is the regulation of members of CoSets coordinated? -- 13.7 The alpha-Spectrum -- Defining the alpha-spectrum -- Computing the alpha-spectrum -- The conservative nature of the alpha-spectrum -- 13.8 Summary -- 13.9 Further Reading -- CHAPTER 14 Sampling Solution Spaces -- 14.1 The Basics -- A simple flux split -- The overall procedure -- 14.2 Sampling Low-Dimensional Spaces -- Parallelepipeds -- Sampling parallelepipeds.
,
Elimination of redundant constraints in determining….
Permalink
