Computational Systems
Biology
Prepared by:
Rhia Trogo
Rafael Cabredo
Levi Jones Monteverde
What are Biological Systems?
Popular Notion:
It is a complex system consisting of
very many simple and identical elements
interacting to produce what appears to be
complex behavior
Example: Cells, Proteins
What are Biological Systems?
Realistic Notion:
It is a system composed of many
different kinds of multifunctional elements
interacting selectively and nonlinearly with
others to produce coherent behavior.
What are Biological Systems?
Complex systems of simple elements have
functions that emerge from the properties
of the networks they form.
Biological systems have functions that rely
on a combination of the network and the
specific elements involved.
Molecular vs. Systems
Biology
Biology
In molecular biology,
gene structure and
function is studied at
the molecular level.
In systems biology,
specific interactions of
components in the
biological system are
studied – cells,
tissues, organs, and
ecological webs.
From Systems Biology to
Computational Biology
Biological Systems are complex, thus, a
combination of experimental and
computational approaches are needed.
Linkages need to be made between
molecular characteristics and systems
biology results
Databases and Tools
Languages
– Systems Biology Markup Language
– CellML
– Systems Biology Workbench
Databases
– Kyoto Encyclopedia of Genes and Genomes
– Alliance for Cellular Signaling
– Signal Transduction Knowledge Environment
p53
Protein 53
Produces 53 proteins kiloDaltons
Guardian of the genome
Detects DNA damages
Halts the cell cycle if damage is detected
to give DNA time to repair itself
p53
If (damage equals true and repairable = true)
halt cell cycle
else
if(damage equals true and repairable = false)
induce apoptosis (suicide)
The Cell Cycle
G1 - Growth and
preparation of the
chromosome
replication
S - DNA replication
G2 - Preparation for
Mitosis
M - Chromosomes
separate
Checkpoints for DNA Double
Strand Breakage
ataxia-telangiectasia mutated
Cancer Cell Network
p53
p53
activates
p21
deactivates
No cell cycle!
CDK
p53
Cancer Drugs
Alkylating agents - interfere with cell division and affect the cancer
cells in all phases of their life cycle. They confuse the DNA by
directly reacting with it.
Antimetabolites - interfere with the cell's ability for normal
metabolism. They either give the cells wrong information or block
the formation of "building block" chemical reactions one phase of the
cell's life cycle.
Vinca alkaloids - (plant alkaloids) are naturally-occurring chemicals
that stop cell division in a specific phase.
Taxanes - are derived from natural substances in yew trees. They
disrupt a network inside cancer cells that is needed for the cells to
divide and grow.
all inhibit the cell cycle
The Cost of Robustness
Robustness is not a good characteristic for
all types of cells.
Example: The robust cancer cell!
Systems that are robust against common
perturbations are often fragile to new
perturbations (vulnerability of complex
networks)
Advantages of Computational
Systems Biology
It is highly relevant in discovering more
complex relationships involving multiple
genes
This may create new opportunities for drug
discovery
Better medical therapies for individual
treatments
What’s to come?
Current work is on small sub-networks
within cells.
– Feedback circuit of bacteria chemotaxis
– Circadian Rhythm
– Parts of signal-transduction pathways
– Simplified models of the cell cycle
– Models of the Red blood cells
What’s to come?
Research has begun on larger-scale
simulations
– Biochemical network level
– Simulation of Epidermal Growth Factor (EGF)
signal-transduction cascade
– The Physiome Project
Biochemical Networks
Problem:
The behavior of cells is governed and
coordinated by biochemical signaling networks
that translate external cues (hormones, growth
factors, stress, etc.) into adequate biological
responses such as cell proliferation,
specialization or death, and metabolic control.
Motivation:
Deep understanding of cell malfunction is crucial
for drug development and other therapies.
Available: [online]
http://www.brc.dcs.gla.ac.uk/projects/bp
s/bps_slides/bps_slides.pdf
Biochemical Networks
Biochemical Networks
Interpreting Biochemical Networks as
Concurrent Communicating Systems
Biochemical networks are analogous to
concurrent computer systems in many respects.
Concurrent systems are built up using basic
concepts such as choice, recursion, modularity,
synchronization, and mobility.
By exploiting these analogies, the existing tools
and formalisms for computing systems can be
applied to biochemical networks.
Concurrency Theory
Concurrent, communicating systems have been the
subject of intense study by Computing Scientists. Rich
theories and tools have been developed to aid in design,
analysis and verification of such systems.
Concurrent systems are inherently complex. To manage
complexity, theories and tools have been developed to
allow programmers to simulate behaviour. Simulators
allow the analysis of traces through concurrent
executions and provide a testbed for experimentation.
At a more abstract level, temporal analysis involves
proving that a concurrent system adheres to a temporal
property, i. e. it can be shown that a network protocol
always delivers data packets in the same order they
were sent.
Concurrency
A concurrent system is one where multiple processes exist at the same time. These
processes execute in parallel and potentially interact with each other. As an example
of a concurrent system, consider an internet banking site. The server and multiple
client processes exist at the same time, with interactions occurring between the
individual clients and the server.
Concurrency in Biochemical
Networks
Biochemical networks are also concurrent communicating systems. Pathways consist of
sequences of interactions which sometimes affect other parallel pathways. As an
example, consider two pathways involved in cell division. The Ras- Raf pathway which
triggers the cell division and the PI- 3K- Akt pathway which keeps the cell alive are both
triggered by the same growth factor. The sequences of interactions in both pathways run
concurrently with some interaction i. e. Akt inhibits Raf.
Complex modeling of
concurrent systems
Asynchronous circuits have been used to
simplify circuit analysis
Perhaps they could be used to examining
concurrent biological systems.
http://www.async.ece.utah.edu/
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
lambda DNA is initially transcribed from the promoters PL and PR, which direct
synthesis of RNA in opposite directions (left and right respectively). Transcription
is initially terminated at sites tL and tR, but expression of the N gene (in green)
leads to "antitermination" and production of longer transcripts
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
If PR wins and the protein cro is made, then production of cI will be repressed. If on
the other hand promoter PRM wins and the protein cI is made, then production of
cro will be repressed.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
If cro predominates, it hogs the operator region and prevents cI from being made. On
the other hand if cI predominates, it hogs the operator region, causing more of
itself to be made (from the PRM promoter).
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
There is a promoter called PRE that is activated by cII and cIII (which are produced
after the anti-terminator N is made).
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
either the cI or the cro will
predominate, and one of the
following two patterns of gene
expression will result:
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Overview
the first pattern leads to the growth of the virus
(only a fraction of the genes involved in lysis are
actually shown) and the death of the cell.
The second pattern is the more interesting for
our purposes.
– In the lower panel, cI is the only gene that is being
expressed in the virus, and it is involved in a positive
feedback loop to induce more of its own
expression.This explains why the lysogenic state is
stable. The genome of the virus is essentially shut
down during lysogeny, except for a single repressor
protein. If another lambda happens to come along it's
out of luck! The cI repressor from the first lambda
simply prevents expression of the second lambda
genome, and it fails to enter a lytic cycle.
Immunity
That explains bacteriophage immunity!One
allele of cI that is important in the laboratory is
cI857, which is temperature sensitive (the
protein is active at 32 degrees centigrade but
inactivated at 39 degrees centigrade).
We may therefore grow a lambda phage
carrying cI857 as a lysogen at low temperature,
then induce lytic growth by simply moving it to a
warmer incubator.
Database
http://www.biocyc.org/
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Computational Systems Biology