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Systems Biology
Haixu Tang
School of Informatics
Two ways of looking a problem
• Top down or bottom up
– Either look at the whole organism and
abstract large portions of it
– Or try to understand each small piece and
then after understanding every small piece
assemble into the whole
– Both are used, valid and complement each
other
Bottom up (or reductionism) is
traditional approach
– You would study a biological process in details
not worrying about how that process might
interact with other elements in the cell.
– You would strive to understand a gene or
process in great detail, eventually you might
extend this knowledge to other organisms and
compare.
Systems Biology: a top-down
approach?
• Challenge: put the pieces back together
• Attempts to create predictive models of cells,
organs, biochemical processes and complete
organisms
– Data combined with computational, mathematical and
engineering disciplines
– Model <-> simulations <-> experiment
Definitions (Leroy Hood)
• Systems biology
– As global a view as possible
– Fundamentally quantitative
– Different scales integrated
Common “themes”
• Cross disciplinary:
– Systems biology = biology + CS/informatics +
engineering +…
• Massive data/information/knowledge
• Concepts of networks for abstract portrayal of
many interaction types.
• Model development
– Predictive models
– Models to drive experimentation
– Models to understand processes
What is a pathway?
• A series of interconnected steps linked
by the production of intermediates that
are used in the next step
• A series of consecutive reactions that
lead to the ultimate goal.
Requires a higher level of
understanding
• Heterogeneous information “feed” into
this understanding
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Microarrays
Homology tools (BLAST, alignments COGS)
Biochemical literature
Genomic sequence
Specialized databases
A complex problem: human cells
– 35,000 genes either on or off (huge
simplification!) would have 2^35,000 solutions
– Things can be simplified by grouping and
finding key genes which regulate many other
genes and genes which may only interact
with one other gene
– In reality there are lots of subtle interactions
and non-binary states.
Some real numbers from E. coli
• 630 transcription units controlled by 97 transcription factors.
• 100 enzymes that catalyse more than one biochemical reaction .
• 68 cases where the same reaction is catalysed by more than one
enzyme.
• 99 cases where one reaction participates in multiple pathways.
• The regulatory network is at most 3 nodes deep.
• 50 of 85 studied transcription factors do not regulate other
transcription factors, lots of negative auto-regulation
An example –
Bacterial Chemotaxis
• Bacteria are able to sense temporal gradients of
chemical ligands in their vicinity.
• Their movement is composed of:
– Smooth runs
– Tumbling – in which a new direction
is chosen randomly.
• By modifying tumbling frequency a bacterium is
able to direct its motion towards/away from
attractants/repellents.
E. coli and its flagella
Mechanism
1. Chemotactic ligands bind to specialized
receptors (MCP), a complex of proteins
CheA and CheW;
2. CheA is a kinase that phosphorylates the
response regulator, CheY, whose
phosphorylated form (CheYp) binds to the
flagellar motor and generates tumbling.
Changing the kinase activity of CheA
modifies the tumbling frequency.
3. The receptor can also be reversibly
methylated. Methylation enhances the
kinases activity and mediates adaptation
to changes in ligand concentration. CheR
methylates the receptor, CheB
demethylates it. A feedback mechanism is
achieved through the CheA-mediated
phosphorylation of CheB, which enhances
its demethylation activity.
Adaptation Property
• The steady state tumbling frequency in a
homogenous ligand environment is
insensitive to the ligand concentration.
• Allows bacteria to maintain sensitivity to
chemical gradient over a wide range of
ligand concentrations.
A Simple Two-State Model
• Receptor complex (E) has two states: Active,
inactive.
• Active receptor has kinase activity, which
induces tumbling.
• Receptor activity is probabilistic, depending
on
– methylation level, m (feedback from E)
– ligand occupancy, U or O
– Activity probabilities um for Eu m, om for Eo m
A Simple Two-State Model
• Time contained network
• Network input: Ligand
concentration (L).
• Network output:
Predicted average
complex activity.
Model Simulations
• Obtain biochemical parameters by experiments
• Solve differential equations using numerical
methods (~ 1min)
• Variations in these biochemical parameters would
occur naturally in a population —polymorphisms.
• Model robustness comes from the feedback
mechanism.
Importance of robustness
• Genetic polymorphism may change network
biochemical parameters.
• Enzyme concentrations are low and therefore
subject to considerable variations.
• Robust mechanism allows bacteria to tolerate
both.
Networks: the “system” of
systems biology
• Building network structure
• Learning parameters
• Simulation  predicting behaviors
• Dynamics