Download Systems Biology Study Group Chapter 3

Systems Biology Study Group
Chapter 3
Walker Research Group
Spring 2007
Overview
• Review
• Biochemical Network reconstruction
• Metabolic Networks
– Basic Features
•
•
•
•
•
Hierarchy
Reconstruction Methods
Genome-scale Metabolic Reconstructions
Multiple Genome-scale Networks
Summary
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
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
Biochemical Network
Reconstruction
• Network reconstruction: Process of
identifying all reactions that comprise a
network
• Networks are not separate and
independent of each other
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
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
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
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
Hierarchy
• Level 2 – Sectors
CO2, H2O
O2
ATP
Specialty
products
ADP
Energy from
catabolism
NADPH
Catabolism
Substrates
NADP+
Sugarphosphate
s
Anabolism
Amino acids
PEP
Pyruvate
AcCoa
Nucleotides
Growth
Fatty acids
Α-KG
SuccCoA
OA
Proteins
RNA/DNA
Membranes
Adapted from: Palsson, B. 2006. Systems Biology
etc
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
Source: Feigenson, G. 2006 BIOBM 331
ACETATE
CITRIC ACID CYCLE
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
Reconstruction Methods
• Define reaction list – assemble information
on all biochemical reactions in network
• Sources:
– Biochemistry
– Genomics
– Physiology
– In silico modeling
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
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
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
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
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
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
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
291
340
388
341
485
476
708
584
842
750
646
1149
G. sulfurreducens
588
514
523
S. aureus
619
571
640
M. succinciproducens
335
352
373
H. pylori
S. cerevisiae
Adapted from: Palsson, B. 2006. Systems Biology
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
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
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
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
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
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
Thank you