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Systems Biology Study GroupChapter 3
Walker Research Group
Spring 2007
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Overview
• Review• Biochemical Network reconstruction• Metabolic Networks
– Basic Features
• Hierarchy• Reconstruction Methods• Genome-scale Metabolic Reconstructions• Multiple Genome-scale Networks• Summary
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Review
• Systems Biology: Process of genome scale network reconstruction, followed by synthesis of in silico models describing their functionalities– Enumeration of biological components– Identification of links connecting processes– Modeling– Hypothesis generation and testing
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Review
• Roots of Systems Biology– Biology
• Molecular biology• High throughput
sequencing• Genome scale analysis
– Systems• Analog simulations• Large scale simulations
of metabolic networks• Genome scale models
and analysis
• Systems Biology constrained by:– Chemical
transformation properties
• Stoichiometry• Relative and absolute
rates
– Functional states• Physiochemical nature• Orientation
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Biochemical Network Reconstruction
• Network reconstruction: Process of identifying all reactions that comprise a network
• Networks are not separate and independent of each other
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Metabolic Networks
• Metabolism – Biochemical modification of chemical compounds within living cells.
• Metabolic networks are the collection of pathway through which this is accomplished
Source: Feigenson, G. 2006 BIOBM 331
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Basic Features
• Intermediary metabolism: chemical “engine”– Converts raw materials
into energy, building blocks for biological structures
– Obeys laws of physics and chemistry
– Elaborate regulatory structure
Source: Feigenson, G. 2006 BIOBM 331
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Hierarchy
• Simplify conceptualization of network functions• Level 1 – Cellular Inputs and Outputs
– Coarse description of overall activity
•First published experiments in human metabolism
• Italian physician Santorio Santorio in 1614
• Used a steelyard balance to weigh himself after eating, sleeping, working etc.
• Found that most of food intake was lost through “insensible perspiration”
Source: Metabolism. Wikipedia, public domain art. 18 July 2005
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Hierarchy
• Level 2 – Sectors– Metabolism has two basic sectors
• Catabolism – break down substrates into metabolites that cell can use
• Anabolism – synthesize amino acids, fatty acids, nucleic acids and other cellular building blocks
• Exchange of chemical groups and redox potentials takes place using carrier molecules, linking the two sectors
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Hierarchy• Level 2 – Sectors
Catabolism Anabolism
Growth
ATP
ADP
NADPH
NADP+
Sugar-phosphate
s
PEP
Pyruvate
AcCoa
Α-KG
SuccCoA
OA
Substrates Amino acids
Nucleotides
Fatty acids
Specialty products
CO2, H2O
O2
Energy from catabolism
Proteins
RNA/DNA
Membranes
etcAdapted from: Palsson, B. 2006. Systems Biology
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Hierarchy
• Level 3 – Pathways– Series of chemical reactions occurring within a cell,
usually catalyzed by an enzyme– Pathways in catabolism
• Substrate picked up by cell• Hydrolyzed if necessary• Activated by cofactor• Degraded to yield energy
– At this level metabolism relies on basic chemical principles such as stoichiometry and kinetics
GLUCOSE GLYCOLYSIS ACETATE CITRIC ACID CYCLE
Source: Feigenson, G. 2006 BIOBM 331
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Hierarchy
• Level 4 – Individual Reactions– High-throughput data makes this
level possible– Can reconstruct genome-scale
stoichiometric matrices of organisms
• May be on the order of hundreds of metabolites, thousands of chemical reactions
• It is at this level that the text is focused
Source: Feigenson, G. 2006 BIOBM 331
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Reconstruction Methods
• Define reaction list – assemble information on all biochemical reactions in network
• Sources:– Biochemistry– Genomics– Physiology– In silico modeling
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Reconstruction Methods
• Genome annotation– Open reading frames (ORF’s)
• Identified and assigned functions via experimentation or comparison to known sequences
– In silico modeling• Can achieve 40 – 70% functional assignment• Purely hypothetical
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Reconstruction Methods
• Sequence Data– List of sources– Sequence homology may be evidence of a
reaction in an organism
• Biochemical data– Enzyme isolation and function demonstration– Gives stoichiometry and reversibility of
reaction
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Reconstruction Methods
• Enzyme Commission Numbers– Used to systematically and
unambiguously characterize reactions
– E.C. 2.7.1.2 → Glucokinase
• Protein Database– http://www.rcsb.org/pdb/
Crystal structure of E. coli glucokinase in complex with glucose
Source: Protein database. http://www.rcsb.org/pdb/ 22 February 2007
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Reconstruction Methods
• Gene – Protein – Reaction Associations– Not all genes have one to one relationship
with corresponding enzymes or metabolic reactions
• May require multiple genes for enzyme to catalyze reaction
– Fumerate reductase requires 4 subunits, frdA, frdB, frdC frdD
• Genes may also encode promiscuous enzymes which catalyze several different reactions
– Transketolase I in pentose phosphate pathway
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Reconstruction Methods
• Organism specific sources– E. coli encyclopedia (EcoCyc) database– Yeast
• Comprehensive Yeast Genome Database• Yeast Protein Database• Saccharomyces Genome Database
• Additional issues include:– Demands on the network and composition– Physiological data and ability to reproduce
experimental conditions
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Genome-scale Metabolic Reconstructions
• Ongoing process since 1930s– Since glycolytic pathway determined
• First genome sequenced in 1995– H. influenzae
• First reconstruction of genome-scale metabolic network in 1999
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Genome-scale Metabolic Reconstructions
Table: Genome-scale reconstructions of metabolic networks in microbial cells
Number of
Organism Genes Metabolites Reactions
H. influenzae 296 343 488
E. coli 660 436 720
904 625 931
H. pylori 291 340 388
341 485 476
S. cerevisiae 708 584 842
750 646 1149
G. sulfurreducens 588 514 523
S. aureus 619 571 640
M. succinciproducens 335 352 373
Adapted from: Palsson, B. 2006. Systems Biology
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Genome-scale Metabolic Reconstructions
Table: Evolution of E. coli metabolic reconstructions
Number of metabolites Number of reactions Year
17 14 1990
118 146 1993
305 317 1997, 1998
436 720 2000
625 931 2003
Adapted from: Palsson, B. 2006. Systems Biology
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Multiple Genome-scale Networks
• Metabolic networks are not isolated– Interact with cellular processes
• Transcriptional regulation, cell motility
– Signaling networks in multicellular organisms– Cell fate processes
• Mitosis, apoptosis
• To fully describe a cell, all networks must be reconstructed
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Multiple Genome-scale Networks
• Multiple Network Reconstruction– Common components
• Same molecules participate in more than one network
• ATP– Metabolic – energy metabolism– Regulatory – global regulator of DNA coiling– Signaling – phosphate for signaling reactions
– Content in Context• Integrating all “omics” data
– Genomic, transcriptomic, proteomic, metabolomic– Biochemically and genetically accurate framework– Allows for predictions of function in environment
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Multiple Genome-scale Networks
• Regulation of metabolic networks– Modulating enzyme reaction rates, gene
expression• Activity, concentration or both
– Negative: repression or inhibition– Positive: induction or activation
– Gene expression is coarse metabolic control– Enzyme activity is fine tuning
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Multiple Genome-scale Networks
• Regulating Enzyme Activity– Allosteric mechanism
• Enzymes have binding site for substrate and for regulatory molecules
– Can activate or inhibit enzyme activity
• Conformational changes in enzyme molecule• Example: Hexokinase
– Catalyzes phosphorylation of glucose– Inhibited by ATP, product of glycolysis– Stimulated by ADP, product of ATP stored energy
consumption
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Summary
• Complex networks carry out complicated biological functions, like metabolism
• All networks based on biochemical reactions, described by stoichiometric matrix
• Hierarchy can be used to conceptualize networks at varying resolutions
• Metabolism is the best characterized network in terms of biochemistry, kinetics and thermodynamics
• Network reconstruction requires detailed examination of all components and links the network, many resources can provide this information
• Metabolic networks do not act independently of other networks, integration of all networks is necessary to describe cellular functions
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Thank you