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Computational Systems Computational Systems BiologyBiology

Prepared by:Prepared by:Rhia TrogoRhia Trogo

Rafael CabredoRafael CabredoLevi Jones MonteverdeLevi Jones Monteverde

What are Biological Systems?What are Biological Systems?

Popular Notion:Popular Notion:

It is a It is a complex systemcomplex system consisting of consisting of very many simple and identical elements very many simple and identical elements interacting to produce what appears to be interacting to produce what appears to be complex behaviorcomplex behavior

Example: Cells, ProteinsExample: Cells, Proteins


Realistic Notion:Realistic Notion:

It is a system composed of many It is a system composed of many different kinds of multifunctional elements different kinds of multifunctional elements interacting selectively and nonlinearly with interacting selectively and nonlinearly with others to produce coherent behavior.others to produce coherent behavior.


Complex systems Complex systems of simple elements have of simple elements have functions that emerge from the properties functions that emerge from the properties of the networks they form.of the networks they form.

Biological systemsBiological systems have functions that rely have functions that rely on a combination of the network and the on a combination of the network and the specific elements involved.specific elements involved.

Molecular vs. Systems Molecular vs. Systems Biology BiologyBiology Biology

In In molecular biologymolecular biology, , gene structure and gene structure and function is studied at function is studied at the molecular level.the molecular level.In In systems biologysystems biology, , specific interactions of specific interactions of components in the components in the biological system are biological system are studied – cells, studied – cells, tissues, organs, and tissues, organs, and ecological webs.ecological webs.

From Systems Biology to From Systems Biology to Computational BiologyComputational Biology

Biological Systems are complex, thus, a Biological Systems are complex, thus, a

combination of experimental and combination of experimental and

computational approaches are needed.computational approaches are needed.

Linkages need to be made between Linkages need to be made between molecular characteristics and systems molecular characteristics and systems biology results biology results

Databases and ToolsDatabases and Tools

LanguagesLanguages– Systems Biology Markup LanguageSystems Biology Markup Language– CellML CellML – Systems Biology Workbench Systems Biology Workbench

DatabasesDatabases– Kyoto Encyclopedia of Genes and GenomesKyoto Encyclopedia of Genes and Genomes– Alliance for Cellular SignalingAlliance for Cellular Signaling– Signal Transduction Knowledge EnvironmentSignal Transduction Knowledge Environment

p53p53

Protein 53Protein 53

Produces 53 proteins kiloDaltonsProduces 53 proteins kiloDaltons

Guardian of the genomeGuardian of the genome

Detects DNA damagesDetects DNA damages

Halts the cell cycle if damage is detected Halts the cell cycle if damage is detected to give DNA time to repair itselfto give DNA time to repair itself

p53p53

If (damage equals true and repairable = true)If (damage equals true and repairable = true)

halt cell cyclehalt cell cycle

elseelse

if(damage equals true and repairable = false)if(damage equals true and repairable = false)

induce apoptosis (suicide) induce apoptosis (suicide)

The Cell CycleThe Cell Cycle

G1 - Growth and G1 - Growth and preparation of the preparation of the chromosome chromosome replicationreplicationS - DNA replicationS - DNA replicationG2 - Preparation for G2 - Preparation for MitosisMitosisM - Chromosomes M - Chromosomes separate separate

Checkpoints for DNA Double Checkpoints for DNA Double Strand BreakageStrand Breakage

ataxia-telangiectasia mutated

Cancer Cell NetworkCancer Cell Network

p53p53

p53activates

p21deactivates

CDK

No cell cycle!

p53p53

Cancer DrugsCancer Drugs

Alkylating agents - Alkylating agents - interfere with cell division and affect the cancer interfere with cell division and affect the cancer cells in all phases of their life cycle. They confuse the DNA by cells in all phases of their life cycle. They confuse the DNA by directly reacting with it.directly reacting with it.Antimetabolites - Antimetabolites - interfere with the cell's ability for normal interfere with the cell's ability for normal metabolism. They either give the cells wrong information or block metabolism. They either give the cells wrong information or block the formation of "building block" chemical reactions one phase of the formation of "building block" chemical reactions one phase of the cell's life cycle.the cell's life cycle.Vinca alkaloids - Vinca alkaloids - (plant alkaloids) are naturally-occurring chemicals (plant alkaloids) are naturally-occurring chemicals that stop cell division in a specific phase.that stop cell division in a specific phase.Taxanes - Taxanes - are derived from natural substances in yew trees. They are derived from natural substances in yew trees. They disrupt a network inside cancer cells that is needed for the cells to disrupt a network inside cancer cells that is needed for the cells to divide and grow.divide and grow.

all inhibit the cell cycleall inhibit the cell cycle

The Cost of RobustnessThe Cost of Robustness

Robustness is not a good characteristic for Robustness is not a good characteristic for all types of cells.all types of cells.

Example: The robust cancer cell!Example: The robust cancer cell!

Systems that are robust against common Systems that are robust against common perturbations are often fragile to new perturbations are often fragile to new perturbations (vulnerability of complex perturbations (vulnerability of complex networks)networks)

Advantages of Computational Advantages of Computational Systems BiologySystems Biology

It is highly relevant in discovering more It is highly relevant in discovering more complex relationships involving multiple complex relationships involving multiple genesgenes

This may create new opportunities for drug This may create new opportunities for drug discoverydiscovery

Better medical therapies for individual Better medical therapies for individual treatmentstreatments

What’s to come?What’s to come?

Current work is on small sub-networks Current work is on small sub-networks within cells.within cells.– Feedback circuit of bacteria chemotaxisFeedback circuit of bacteria chemotaxis– Circadian RhythmCircadian Rhythm– Parts of signal-transduction pathwaysParts of signal-transduction pathways– Simplified models of the cell cycleSimplified models of the cell cycle– Models of the Red blood cellsModels of the Red blood cells

What’s to come?What’s to come?

Research has begun on larger-scale Research has begun on larger-scale simulationssimulations– Biochemical network levelBiochemical network level– Simulation of Epidermal Growth Factor (EGF) Simulation of Epidermal Growth Factor (EGF)

signal-transduction cascadesignal-transduction cascade– The Physiome ProjectThe Physiome Project

Available: [online] http://www.brc.dcs.glAvailable: [online] http://www.brc.dcs.gla.ac.uk/projects/bps/bps_slides/bps_slida.ac.uk/projects/bps/bps_slides/bps_slides.pdfes.pdf

Biochemical NetworksBiochemical Networks

Problem:Problem:The behavior of cells is governed and The behavior of cells is governed and coordinated by coordinated by biochemical signaling networks biochemical signaling networks that translate external cues (hormones, growth that translate external cues (hormones, growth factors, stress, etc.) into adequate biological factors, stress, etc.) into adequate biological responses such as cell proliferation, responses such as cell proliferation, specialization or death, and metabolic control.specialization or death, and metabolic control.

Motivation:Motivation:Deep understanding of cell malfunction is crucial Deep understanding of cell malfunction is crucial for drug development and other therapies.for drug development and other therapies.

Biochemical NetworksBiochemical Networks

Interpreting Biochemical Networks as Interpreting Biochemical Networks as Concurrent Communicating SystemsConcurrent Communicating Systems

Biochemical networks are analogous to Biochemical networks are analogous to concurrent computer systems in many respects.concurrent computer systems in many respects.

Concurrent systems are built up using basic Concurrent systems are built up using basic concepts such as choice, recursion, modularity, concepts such as choice, recursion, modularity, synchronization, and mobility.synchronization, and mobility.

By exploiting these analogies, the existing tools By exploiting these analogies, the existing tools and formalisms for computing systems can be and formalisms for computing systems can be applied to biochemical networks.applied to biochemical networks.

Concurrency TheoryConcurrency Theory

Concurrent, communicating systems have been the Concurrent, communicating systems have been the subject of intense study by Computing Scientists. Rich subject of intense study by Computing Scientists. Rich theories and tools have been developed to aid in design, theories and tools have been developed to aid in design, analysis and verification of such systems.analysis and verification of such systems.Concurrent systems are inherently complex. To manage Concurrent systems are inherently complex. To manage complexity, theories and tools have been developed to complexity, theories and tools have been developed to allow programmers to allow programmers to simulate simulate behaviour. Simulators behaviour. Simulators allow the analysis of traces through concurrent allow the analysis of traces through concurrent executions and provide a testbed for experimentation.executions and provide a testbed for experimentation.At a more abstract level, At a more abstract level, temporal temporal analysis involves analysis involves proving that a concurrent system adheres to a temporal proving that a concurrent system adheres to a temporal property, i. e. it can be shown that a network protocol property, i. e. it can be shown that a network protocol always delivers data packets in the same order they always delivers data packets in the same order they were sent.were sent.

ConcurrencyConcurrencyA concurrent system is one where multiple processes exist at the same time. These 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 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 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 processes exist at the same time, with interactions occurring between the individual clients and the server.clients and the server.

Concurrency in Biochemical Concurrency in Biochemical NetworksNetworks

Biochemical networks are also concurrent communicating systems. Pathways consist of sequences of 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 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 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 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.concurrently with some interaction i. e. Akt inhibits Raf.

Complex modeling of Complex modeling of concurrent systemsconcurrent systems

Asynchronous circuits have been used to Asynchronous circuits have been used to simplify circuit analysissimplify circuit analysis

Perhaps they could be used to examining Perhaps they could be used to examining concurrent biological systems.concurrent biological systems.

http://www.async.ece.utah.edu/http://www.async.ece.utah.edu/

QuickTime™ and aTIFF (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



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.



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).



There is a promoter called PRE that is activated by cII and cIII (which are produced after the anti-terminator N is made).



either the cI or the cro will predominate, and one of the following two patterns of gene expression will result:



OverviewOverviewthe first pattern leads to the growth of the virus (only the first pattern leads to the growth of the virus (only a fraction of the genes involved in lysis are actually a fraction of the genes involved in lysis are actually shown) and the death of the cell. shown) and the death of the cell. The second pattern is the more interesting for our The second pattern is the more interesting for our purposes. purposes. – In the lower panel, cI is the only gene that is being In the lower panel, cI is the only gene that is being

expressed in the virus, and it is involved in a positive expressed in the virus, and it is involved in a positive feedback loop to induce more of its own expression.This feedback loop to induce more of its own expression.This explains why explains why the lysogenic state is stablethe lysogenic state is stable. The genome . The genome of the virus is essentially shut down during lysogeny, except of the virus is essentially shut down during lysogeny, except for a single repressor protein. If another lambda happens to for a single repressor protein. If another lambda happens to come along it's out of luck! The cI repressor from the first come along it's out of luck! The cI repressor from the first lambda simply prevents expression of the second lambda lambda simply prevents expression of the second lambda genome, and it fails to enter a lytic cycle. genome, and it fails to enter a lytic cycle.

ImmunityImmunity

That explains That explains bacteriophage immunitybacteriophage immunity!One !One allele of cI that is important in the laboratory is allele of cI that is important in the laboratory is cI857, which is temperature sensitive (the cI857, which is temperature sensitive (the protein is active at 32 degrees centigrade but protein is active at 32 degrees centigrade but inactivated at 39 degrees centigrade). inactivated at 39 degrees centigrade). We may therefore grow a lambda phage We may therefore grow a lambda phage carrying cI857 as a lysogen at low temperature, carrying cI857 as a lysogen at low temperature, then induce lytic growth by simply moving it to a then induce lytic growth by simply moving it to a warmer incubator. warmer incubator.

DatabaseDatabase

http://www.biocyc.org/http://www.biocyc.org/

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