Transcript Higher Order Systems

Higher Order
Systems
In this presentation……
Part 1 – Genetic Regulatory Networks
Part 2 – Molecular Pathways
Part 3 – Protein Interactions
Part 4 – Modeling Regulatory Networks
Part
1
Genetic Regulatory
Networks
Genetic regulatory networks
Higher order systems
• Although genes and proteins can be studied
individually, more insight into their functions
can be gained by studying higher-order
systems, that is, molecular pathways and
networks, cells, tissues, organs and whole
organisms
• This allows their physical and functional
interactions to be determined in the widest
possible context
• The work of Tavazoie et al (1999) is vividly
known for systematic determination of genetic
network architecture
• Cell signaling pathways are linked to genetic
regulatory pathways in ways we are just beginning
to unscramble
• The most enormous bioinformatics project in front
of the scientists is unscrambling this regulatory
network, which controls cell development from
the fertilized egg to the adult
• It would become possible to know which gene to
perturb – or which sequence of genes to perturb,
and in what order – to guide a cancer cell to
nonmalignant behaviour or to apoptosis
[programmed to cell death]
• Or to guide the regeneration of some tissues, so
that if someone has lost half of the pancreas, the
damaged portion could be regenerated
• Or to regenerate the beta cells in people who have
diabetes
• Suppose about 10 genes are picked out that are
known to regulate one another, then a circuit could
be built about their behaviour. It is a good thing and
one should do this but the down side will be that
those 10 genes have inputs from other genes outside
that circuit. Therefore, it is like taking a little chunk
of the circuitry that is embedded in a much larger
circuit of thousands of genes in it. The behaviour
can not be then properly assessed as to how and what
impact the outside genes would create
• It is known for years that every neuron in the
lobster gastric ganglia [a nerve bundle going to
the animal’s digestive system], all the synaptic
connections and the neurotransmitters
• There would be 13 or 20 neurons in the
ganglion and still its behaviour cannot be
figured out
• No mathematician would ever think that
understanding a system with 13 variables is
going to be an easy thing to do
• In the human genome case, there would be
more than 100,000 variables i.e. there would
be 2100,000 states, which is roughly 1030,000
• So even if genes are treated to be on or off,
there are 1030,000 states (which is false as
genes show graded level of activity)
• It is mind boggling because the number of
particles in known universe is 1080
Types of pathways
•
•
•
•
Molecular pathways
Metabolic pathways
Signaling and regulatory pathways
Protein interaction networks
Part
2
Molecular
Pathways
Representation of pathways and
networks
• Molecular pathways and networks can be represented
by graphs, with molecules at the nodes and
relationships shown by links
• In metabolic pathways, nodes represent substrates or
intermediates and links represent their catalytic
interconversion by enzymes
• In signaling and regulatory pathways, nodes represent
proteins and links indicate the transfer of information
• Graphs of molecular pathways are generally
directional and can show positive and negative
interactions
Reconstruction of molecular
pathways
• Pathways and networks can be mapped
directly by substrate feeding experiments and
in vitro enzyme assays
• More recently, a number of indirect but highthroughput methods have been developed
thanks to the advent of functional genomics
• These methods include pathway reconstruction
from expression data, protein interaction and
comprehensive mutagenesis programs
Modeling molecular pathways
• Mathematical models of biochemical reactions
are often based on differential equations that
predict the change in concentration of
particular molecules over time
• Simultaneous differential equations can be
used to model entire pathways and several
software applications are available for this
task, including GEPASI and BioQuest
• There are limitations to the use of
simultaneous differential equations and
these have been addressed through the
development of stochastic models based
on the Gillespie algorithm, which is
incorporated into programs such as
StochSim
Subgraph with main interactions between GAD and GABA-receptors,
derived from the linear model. P. D'haeseleer, X. Wen, S. Fuhrman, and
R. Somogyi (1999) Linear Modeling of mRNA Expression Levels During
CNS Development and Injury
Overview of Procedures for Preparing and Analyzing Microarrays of Complementary DNA
(cDNA) and Breast-Tumor Tissue. As shown in Panel A, reference RNA and tumor RNA are
labeled by reverse transcription with different fluorescent dyes (green for the reference cells and
red for the tumor cells) and hybridized to a cDNA microarray containing robotically printed cDNA
clones. As shown in Panel B, the slides are scanned with a confocal laser scanning microscope,
and color images are generated for each hybridization with RNA from the tumor and reference
cells. Genes up-regulated in the tumors appear red, whereas those with decreased expression
appear green. Genes with similar levels of expression in the two samples appear yellow. Genes
of interest are selected on the basis of the differences in the level of expression by known tumor
classes (e.g., BRCA1-mutation–positive and BRCA2-mutation–positive). Statistical analysis
determines whether these differences in the gene-expression profiles are greater than would be
expected by chance. As shown in Panel C, the differences in the patterns of gene expression
between tumor classes can be portrayed in the form of a color-coded plot, and the relations
between tumors can be portrayed in the form of a multidimensional-scaling plot. Tumors with
similar gene-expression profiles cluster close to one another in the multidimensional-scaling
plot. As shown in Panel D, particular genes of interest can be further studied through the use of
a large number of arrayed, paraffin embedded tumor specimens, referred to as tissue
microarrays. As shown in Panel E, immunohistochemical analyses of hundreds or thousands of
these arrayed biopsy specimens can be performed in order to extend the microarray findings.
•The two basic clusters of a)
early and b) late upregulated
genes as identified by
percolation clustering. Color
coding of the expression profiles
is as follows: black means gene
expression is the same as it was
at 2 hours of development;
increasing tint of red color
means increasing expression
relative to 2 hours; and
increasing tint of green color
means decreasing expression
relative to 2 hours
•The bottom portions of the
figure display expression
profiles of the corresponding
genes; the red curves are the
mean expression. Only genes
whose connectivity to the cluster
origins is greater than 20% were
included in these plots.
Templates for Looking At Gene Expression Clustering
By Daniel B. Carr, Roland Somogyi and George Michaels
Gene co-expression pairs in CNS
development and injury
Mutual information tree for genes expressed in rat spinal cord. Michaels
G, Carr DB, Wen X, Fuhrman S, Askenazi M, Somogyi R (1998) Cluster
Analysis and Data Visualization of Large-Scale Gene Expression Data
Gene expression waves. (a) Normalized
gene expression trajectories from Fig. 2
are shown grouped by ‘‘waves’’
determined by Euclidean distance
clustering. Graphs show average
normalized expression pattern or ‘‘wave’’
over the nine time points for all the
genes in each cluster (the time of birth is
marked by a vertical line). Within each
wave, genes are grouped according to
gene families, not according to proximity
as determined by Euclidean distance.
(b) Euclidean distance tree of all gene
expression patterns (for annotated tree,
see http://rsb.info.nih.govymolphysiolyPNASytree.html). Major
branches correspond to waves in a. (c)
Plots of all normalized time series,
highlighting wave 3 (Left, white lines)
and a subcluster of wave 3 (Right, white
lines plotted on top of remaining genes
of wave 3 in red). Subclusters
(secondary branching) were selected by
visual inspection from tree in b; e.g., the
plotted time series of the wave 3
subcluster correspond to branchlet
highlighted in white within wave 3 in b.
(d) PCA. Principal components
projection viewed as a threedimensional stereo plot. Each point
mapped in three-dimensional space
represents an expression time series
corresponding to a gene in Fig. 2.
Highlighted points correspond to
Euclidean distance wave 3 (red
triangles), wave 4 (green squares), and
Molecular pathway resources
• There are many resources for viewing
molecular pathways on the Internet
• One of the most comprehensive for metabolic
pathways is KEGG and this also shows a
selected range of regulatory pathways
• An important feature of such resources is that
the contents of the maps are integrated with
other databases by way of hyperlinks
Part
3
Protein Interactions
Interactions and pathways
• Proteins that physically interact with each
other may be involved in the same molecular
pathway or network, or may form part of a
multi-subunit complex
• Using this principle, pathways can be
reconstructed based on evidence of protein
interactions
• However, information from other sources –
e.g. gene expression patterns and mutant
phenotypes – may also be useful
Handling Y2H data
• Yeast two-hybrid (Y2H) screens produce large
amounts of protein interaction data, but there is a
relatively high level of spurious results (false positives
and false negatives)
• This problem can be addressed by scoring interactions
for reliability, based either on the repeatability of
interactions over multiple experiments, or by the
number of times a given bait will trap independent
clones representing the same prey
• Even so, similar large-scale screens tend to identify
different (although) overlapping sets of interactions
Protein interaction databases
• Several databases have been set up to store
the interaction data arising from large-scale
Y2H screens
• However, much more information on
protein interactions is available in the
scientific literature and a current challenge
in bioinformatics is the assimilation of these
interaction data from diverse sources
The interactome
• It is sum of all protein interactions in the cell
• The simplest way to represent protein
interactions is a graph with proteins as nodes
and interactions as links
• However, when large numbers of proteins are
considered, the graphs become too complex
• They can be simplified by clustering
functionally similar proteins, resulting in a
functional interaction map that links
fundamental cellular processes
Part
4
Modeling Regulatory
Networks
The cell
• It can be regarded as a compartmentalized set
of molecular pathways and networks
distributed in space and restricted by
membranes
• Any model of a cell must incorporate these
features
• A useful modeling resource is Virtual Cell, in
which the cell is defined as a collection of
structures, molecules, reactions and fluxes
• The user can define biological or mathematical
models for cell function
Modeling tissues and organs
• Tissues and organs comprise organized
population of interdependent cells
• Modeling depends on an accurate description
of the geometry of the tissue and must include
any time-dependent processes
• For example, modeling the heart requires a
description of its anatomy and the way in
which action potentials are propagated
• The model must take into account the fact that
cardiac muscle is an anisotropic system
Modeling organisms
• In order to model an entire organism, it is
necessary to have a sound understanding of
the principles underlying development
• For most multicellular organisms there is
too little information and the developmental
program too complex for this to be
achieved
Nematode C. elegans modeled
• The nematode has a number of features that make it
an ideal system upon which to base a developmental
model
• It is a simple organism (it has about 1000 somatic
cells) whose somatic cell lineage is invariant, making
perturbations in development very easy to identify
• The genome has been sequenced; indeed, it was the
first genome of a multicellular organism to be
sequenced
• It also relatively easy to study the physiology of this
organism, and hence a complete wiring diagram of C.
elegans nervous system is available
Modeling spaces
• Models of C. elegans development have
been generated based on the concept of
three spaces:
– Genomic space
– Cellular space
– Developmental space
Genomic
space
Gene
expression
Metabolism
and signal
transduction
Cellular space
Developmental
space
Lineage of
cell types
3D arrangement of
cells in
embryo/organism
Relationships among ‘three spaces’