Computational Systems Biology

1
Computational Systems Biology

• Prepared by
• Rhia Trogo
• Rafael Cabredo
• Levi Jones Monteverde

2
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

3
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.

4
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.

5
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.

6
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

7
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

8
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

9
p53

• If (damage equals true and repairable true)
• halt cell cycle
• else
• if(damage equals true and repairable false)
• induce apoptosis (suicide)

10
The Cell Cycle

• G1 - Growth and preparation of the chromosome
  replication
• S - DNA replication
• G2 - Preparation for Mitosis
• M - Chromosomes separate

11
Checkpoints for DNA Double Strand Breakage
ataxia-telangiectasia mutated
12
Cancer Cell Network
13
p53
activates
deactivates
p53
p21
CDK
No cell cycle!
14
p53
15
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

16
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)

17
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

18
Whats 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

19
Whats to come?

• Research has begun on larger-scale simulations
• Biochemical network level
• Simulation of Epidermal Growth Factor (EGF)
  signal-transduction cascade
• The Physiome Project

20
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.

21
Biochemical Networks
22
Biochemical Networks
23
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.

24
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.

25
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.

26
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.
27
Complex modeling of concurrent systems

• Asynchronous circuits have been used to simplify
  circuit analysis
• Perhaps they could be used to examining
  concurrent biological systems.