SYSTEMS MODELLING OF CANCER Leto Kyritsi. 2May 2005ECCO Group (VUB) Talk Outline 1.Cancer. 2.Systems...

SYSTEMS MODELLING OF CANCERSYSTEMS MODELLING OF CANCER

Leto KyritsiLeto Kyritsi

22May 2005May 2005 ECCO Group (VUB)ECCO Group (VUB)

Talk OutlineTalk Outline

1.1. Cancer.Cancer.

2.2. Systems approach to biology.Systems approach to biology.

3.3. Cancer from a systems perspective.Cancer from a systems perspective.

4.4. Modelling biological systems.Modelling biological systems.

5.5. Modelling cancer.Modelling cancer.

6.6. References.References.


Cancer 1Cancer 1

Uncontrolled cellular proliferation.Uncontrolled cellular proliferation.Cancer cell arises out of a possible 10Cancer cell arises out of a possible 101414 cellular targets. cellular targets. ““At least 1 in 3 people will develop cancer - 1 in 4 men & 1 At least 1 in 3 people will develop cancer - 1 in 4 men & 1 in 5 women will die from it” (Franks, 1998).in 5 women will die from it” (Franks, 1998).Visible tumour: end result of complex series of events.Visible tumour: end result of complex series of events.Gradual process: 5-6 (up to 12) control mechanisms Gradual process: 5-6 (up to 12) control mechanisms deregulated.deregulated.

Accumulation of mutations in 3 types of genes: Accumulation of mutations in 3 types of genes: 1. Oncogenes (1. Oncogenes (↑ cell division).↑ cell division). 2. Tumour suppressor genes (↓ cell division).2. Tumour suppressor genes (↓ cell division). 3. DNA repair genes.3. DNA repair genes.

Lupina


Cancer 2Cancer 2

Hereditary & environmental component:Hereditary & environmental component: -Spontaneous mutations, physical mutagens, chemical -Spontaneous mutations, physical mutagens, chemical

mutagens, viruses… mutagens, viruses… -Mutagenesis: direct or indirect (DNA repair errors).-Mutagenesis: direct or indirect (DNA repair errors). -Or, mutations can be inherited (familial cancers).-Or, mutations can be inherited (familial cancers).

Changes are fixed through replication. Changes are fixed through replication. Initiation: initial change may persist in latent form. Initiation: initial change may persist in latent form. ““Promoting agents” induce initiated cells to divide.Promoting agents” induce initiated cells to divide.Outcome = balance between growth-inhibiting factors Outcome = balance between growth-inhibiting factors and extent of changes in initiated cells.and extent of changes in initiated cells.


The Eukaryotic Cell CycleThe Eukaryotic Cell Cycle

Cancer: a cell cycle disease.Cancer: a cell cycle disease.

Passage through different Passage through different phases determined at phases determined at “checkpoints”, where “checkpoints”, where integration of inputs of integration of inputs of intrinsic data or growth intrinsic data or growth signals reaching the exterior signals reaching the exterior of the cell, determines what of the cell, determines what happens next. happens next.

Mediated by sequential Mediated by sequential activation of key proteins.activation of key proteins.

http://www.bioteach.ubc.ca/CellBiologyhttp://www.bioteach.ubc.ca/CellBiology


Cell Signalling PathwaysCell Signalling Pathways

““Cell signalling or ‘signal transduction’ is the study of the Cell signalling or ‘signal transduction’ is the study of the mechanisms by which mechanisms by which biological informationbiological information is is

transferred between and within cells”transferred between and within cells”

(Systems biologist Olaf Wolkenhauer).(Systems biologist Olaf Wolkenhauer).


Cancer Signalling Pathways 1Cancer Signalling Pathways 11. Tumour suppressor pathways.1. Tumour suppressor pathways.

RB (retinoblastoma):RB (retinoblastoma): -Regulated by CyclinD-CDK4/6 & CyclinE-CDK2 complexes.-Regulated by CyclinD-CDK4/6 & CyclinE-CDK2 complexes. -Represses transcriptional activation of genes controlled by E2Fs -Represses transcriptional activation of genes controlled by E2Fs

(transcription factors that regulate cell cycle progression genes).(transcription factors that regulate cell cycle progression genes).

P53:P53: -A transcription factor that activates apoptosis.-A transcription factor that activates apoptosis. -Blocks G0 to G1 transition by activating p21 transcription (p21 -Blocks G0 to G1 transition by activating p21 transcription (p21

inhibits a number of cyclin-CDK complexes).inhibits a number of cyclin-CDK complexes). -Monitors genomic instability.-Monitors genomic instability.

TGF- TGF- ββ (Transforming Growth Factor - (Transforming Growth Factor - ββ):): -Activate SMAD transcription factors who regulate large number of -Activate SMAD transcription factors who regulate large number of

cell cycle regulators.cell cycle regulators. -Dual role: can promote invasion and metastasis.-Dual role: can promote invasion and metastasis.


Cancer Signalling Pathways 2Cancer Signalling Pathways 22. Oncogenic pathways.2. Oncogenic pathways.

RAS. RAS. MYC (c-, N-, L-, B-).MYC (c-, N-, L-, B-). WNT- Frizzled.WNT- Frizzled.

3. Anti-apoptotic pathways.3. Anti-apoptotic pathways. PI3K.PI3K.

4. Angiogenesis pathways.4. Angiogenesis pathways. FGF-2 (fibroblast growth factor 2).FGF-2 (fibroblast growth factor 2). VEGF (vascular endothelial growth factor).VEGF (vascular endothelial growth factor).

5. Metastasis pathways5. Metastasis pathways TGF.TGF. Hepatocyte Growth Factor.Hepatocyte Growth Factor. Loss of adhesion molecules.Loss of adhesion molecules.


Cancer Signalling Pathways 3Cancer Signalling Pathways 3

6. Telomere end maintenance.6. Telomere end maintenance. TERT (protein component of telomerase enzyme). TERT (protein component of telomerase enzyme). ALT (alternative lengthening in absence of telomerase).ALT (alternative lengthening in absence of telomerase).

7. Mobilisation of resources.7. Mobilisation of resources. -Activation of metabolic programmes that confer specific -Activation of metabolic programmes that confer specific

advantages to the cancer cell. advantages to the cancer cell. -E.g. differentiation-associated antigens, enzymes involved in -E.g. differentiation-associated antigens, enzymes involved in

nutrient metabolism and enzymes that regulate oxidative potential. nutrient metabolism and enzymes that regulate oxidative potential.

8. Tissue-specific pathways.8. Tissue-specific pathways.

9. Immune surveillance.9. Immune surveillance. TNF-TNF-αα, TNF-, TNF-ββ from macrophages: dual role. from macrophages: dual role.


Cancer Signalling “Subway Map”Cancer Signalling “Subway Map”http://www.nature.com/nrc/journal/v2/n5/weinberg_poster/index.htmlhttp://www.nature.com/nrc/journal/v2/n5/weinberg_poster/index.html


The Cancer ChallengeThe Cancer Challenge

Very complex disease to address.Very complex disease to address.

Metastasis via circulatory system is the main problem in Metastasis via circulatory system is the main problem in cancer treatment cancer treatment the “macroscopic” component. the “macroscopic” component.

Phenotypic heterogeneity (both inter- and intra-tumour, Phenotypic heterogeneity (both inter- and intra-tumour, Struikmans 1999, Less 2002) esp. with respect to Struikmans 1999, Less 2002) esp. with respect to treatment response treatment response the “sub-cellular”/”cellular” the “sub-cellular”/”cellular” component. component.

Amazing genetic diversity among cancer cells, so much so Amazing genetic diversity among cancer cells, so much so that some cancer cells "might fairly be called new species" that some cancer cells "might fairly be called new species" (Gibbs, 2003). (Gibbs, 2003).


Systems Approach To Biology 1Systems Approach To Biology 1

“…“…If we break up a living organism by isolating its different parts, If we break up a living organism by isolating its different parts, it is only for the sake of ease in analysis and by no means in it is only for the sake of ease in analysis and by no means in order to conceive them separately. Indeed when we wish to order to conceive them separately. Indeed when we wish to ascribe to a physiological quality its value and true significance, ascribe to a physiological quality its value and true significance, we must always refer it to this whole and draw our final we must always refer it to this whole and draw our final conclusions only in relation to its effects on the whole.”conclusions only in relation to its effects on the whole.”

(Physiologist Claude Bernard, 1865)(Physiologist Claude Bernard, 1865)



Integration of data (informatics perspective) or dynamic Integration of data (informatics perspective) or dynamic interactions?interactions?

SB is about methodologies rather than tools and SB is about methodologies rather than tools and technologies: “modelling process itself more important technologies: “modelling process itself more important than the model” (Wolkenhauer).than the model” (Wolkenhauer).

Hypothesis- or Data-driven?Hypothesis- or Data-driven?

Distinction between “-omics” family approaches for data Distinction between “-omics” family approaches for data integration and fusion…integration and fusion…

……and data-based modelling and simulation (system and data-based modelling and simulation (system identification).identification).



Human genes: initially believed ~100,000, now known to be Human genes: initially believed ~100,000, now known to be 20,000.20,000.

Mechanisms of gene regulation:Mechanisms of gene regulation: 1. Transcriptional1. Transcriptional 2. Post-transcriptional2. Post-transcriptional 3. Translational3. Translational 4. Post-translational4. Post-translational

Context-specific: “genetic information turns out not to be Context-specific: “genetic information turns out not to be physical at all, and to be subject to contextual modulations” physical at all, and to be subject to contextual modulations” (G. Kampis).(G. Kampis).Genome plays data-like, software-like, hardware-like roles Genome plays data-like, software-like, hardware-like roles (Hofstadter).(Hofstadter).


Life’s Complexity PyramidLife’s Complexity Pyramid

Oltvai Oltvai et.al.et.al. (2002). (2002).

Large-scale structures emerge from low-level interactions.Large-scale structures emerge from low-level interactions.

..

FUNCTIONAL MODULES

REGULATORY MOTIFS

MOLECULES

Principle Universality

Organism Specificity

•Koestler (1967): “holon”

•Jacob (1974): “integron”


Cancer From A Systems Perspective 1Cancer From A Systems Perspective 1Acquired tumour “robustness” (Kitano, 2004).Acquired tumour “robustness” (Kitano, 2004).Enabled by: Enabled by:

-Heterogeneous functional redundancy of the tumour mass.-Heterogeneous functional redundancy of the tumour mass. -Intracellular (mutation-associated) feedback loops, inherent cell -Intracellular (mutation-associated) feedback loops, inherent cell

cycle robustness, environment-associated feedback loops.cycle robustness, environment-associated feedback loops.Many anticancer drugs increase heterogeneity by causing drug-Many anticancer drugs increase heterogeneity by causing drug-resistant mutations!resistant mutations!

Emergence: “no deductive causality”.Emergence: “no deductive causality”.Bhalla et al (1999): signalling networks exhibit emergent Bhalla et al (1999): signalling networks exhibit emergent properties (e.g. self-sustaining feedback loops that result in properties (e.g. self-sustaining feedback loops that result in bistable behavior with discrete steady states). bistable behavior with discrete steady states). Cancer, which results from signalling network deregulation, can Cancer, which results from signalling network deregulation, can be studied as an emergent system.be studied as an emergent system.


Cancer From A Systems Perspective 2Cancer From A Systems Perspective 2

Homeostasis: the automatic maintenance of constant Homeostasis: the automatic maintenance of constant conditions in open systems by counteracting influences conditions in open systems by counteracting influences tending toward disequilibrium.tending toward disequilibrium.

“… “… an examination of the self-righting methods employed in an examination of the self-righting methods employed in the more complex living beings may offer hints for improving the more complex living beings may offer hints for improving and perfecting the methods which still operate inefficiently and perfecting the methods which still operate inefficiently and unsatisfactorily” (Walter B. Cannon, ”Wisdom of the and unsatisfactorily” (Walter B. Cannon, ”Wisdom of the Body”, 1932).Body”, 1932).

Complexity pyramid: “lower” levels often get “sacrificed” for Complexity pyramid: “lower” levels often get “sacrificed” for sake of “higher” levels (e.g. apoptosis).sake of “higher” levels (e.g. apoptosis).


““Wisdom Of The Body”Wisdom Of The Body”

G. Zajicek (Professor of Experimental Medicine and Cancer G. Zajicek (Professor of Experimental Medicine and Cancer Research, Hebrew University of Jerusalem ).Research, Hebrew University of Jerusalem ).WOB metaphor: attribute of all living systems, “movable WOB metaphor: attribute of all living systems, “movable equilibrium point”.equilibrium point”.WOB decides on the balance between disease-driving and WOB decides on the balance between disease-driving and homeostatic, health-maintaining mechanisms.homeostatic, health-maintaining mechanisms.

Consequences: biomedical, conceptual/philosophical.Consequences: biomedical, conceptual/philosophical.


Cancer From A Systems Perspective 3Cancer From A Systems Perspective 3

Tumour cachexia: cause rather than effect?Tumour cachexia: cause rather than effect?““Systemic disease with local manifestations” (Deighton Systemic disease with local manifestations” (Deighton 1975).1975).Normal development / injury: precise mechanisms allow Normal development / injury: precise mechanisms allow organs to reach fixed size. organs to reach fixed size. Contact inhibition (homeostatic mechanism) but…Contact inhibition (homeostatic mechanism) but…Cells are acting as a Cells are acting as a systemsystem, in parallel. , in parallel. Single instruction multiple data stream. Single instruction multiple data stream. Distinction between Distinction between whatwhat happens (e.g. loss of happens (e.g. loss of homeostasis) and homeostasis) and howhow it happens (pathway deregulation it happens (pathway deregulation etc). etc). ““Egg or chicken”? Egg or chicken”?


MOLECULAR

CELLULAR

ORGANISMAL


Key Features of Biological Systems 1Key Features of Biological Systems 1

OpennessOpenness

ComplexityComplexity

Non-linearityNon-linearity

OrderOrder

IntegrationIntegration

HierarchyHierarchy

AdaptivenessAdaptiveness


Key Features of Biological Systems 2Key Features of Biological Systems 2

Self-organisationSelf-organisation

DissipativenessDissipativeness

Information-richnessInformation-richness

AutopoiesisAutopoiesis

EmergenceEmergence

OrganisationOrganisation

TeleonomyTeleonomy


Modelling Biological SystemsModelling Biological Systems

Model: idealised representation of one thing in terms of Model: idealised representation of one thing in terms of something else.something else.

Target object always of lower complexity than source object.Target object always of lower complexity than source object.

R. Rosen: “A measure of the complexity of a system is the R. Rosen: “A measure of the complexity of a system is the number of models required to understand its behaviour”.number of models required to understand its behaviour”.

R. Levins: “No model can be simultaneously optimised for R. Levins: “No model can be simultaneously optimised for generality, precision and realism”.generality, precision and realism”.

N. Dioguardi: N. Dioguardi: -“Every system in nature can be expressed in a formal -“Every system in nature can be expressed in a formal

language”.language”. - Hepatone: the liver abstracted as a fractal object.- Hepatone: the liver abstracted as a fractal object.


The “Six Challenges” (Wolkenhauer, 2002)The “Six Challenges” (Wolkenhauer, 2002)

How to:How to:

1. Simultaneously capture dynamic regulation and spatial 1. Simultaneously capture dynamic regulation and spatial organisation.organisation.

2. Capture intra- / inter-cellular actions and interactions.2. Capture intra- / inter-cellular actions and interactions.

3. Cross organisational levels.3. Cross organisational levels.

4. Integrate experimental levels (genome, transcriptome, 4. Integrate experimental levels (genome, transcriptome, proteome, metabolome, physiome).proteome, metabolome, physiome).

5. Combine data analysis and data management.5. Combine data analysis and data management.

6. Relate formal representations, provide conceptual 6. Relate formal representations, provide conceptual frameworks / theoretical foundations to the previous five frameworks / theoretical foundations to the previous five points.points.


Modelling CancerModelling Cancer

Flow of information / decision making.Flow of information / decision making.Integration of different hierarchical levels:Integration of different hierarchical levels:

-Timing.-Timing. -Topology.-Topology. -Precedence.-Precedence.

Cancer cell as self-organising system.Cancer cell as self-organising system.Cancer dynamics: Cancer dynamics:

-Benign -Benign In SituIn Situ Malignant Malignant -Discrete cancer states as system attractors.-Discrete cancer states as system attractors.

Flow rather than states.Flow rather than states. -Critical transition thresholds.-Critical transition thresholds.

The cancer knowledge domain (ontology).The cancer knowledge domain (ontology).


Modelling: Subcellular / Cellular LevelModelling: Subcellular / Cellular Level

Control/regulatory systems within cancer cell:Control/regulatory systems within cancer cell:

-Cancer signalling pathways. -Cancer signalling pathways.

-Cancer signalling networks.-Cancer signalling networks.

Action of mutagens on specific sites.Action of mutagens on specific sites.

Immune response. Immune response.

Endocrine response.Endocrine response.

Angiogenesis.Angiogenesis.

Adhesion mechanisms & metastasis.Adhesion mechanisms & metastasis.

Action of therapeutic agents at molecular level.Action of therapeutic agents at molecular level.


Modelling: Macroscopic LevelModelling: Macroscopic Level

Cell-to-cell interactions and behaviour.Cell-to-cell interactions and behaviour.

Tumour dynamics: Tumour dynamics:

-Tumour growth.-Tumour growth.

-Metastasis.-Metastasis.

-Angiogenesis.-Angiogenesis.

-Response to various treatments.-Response to various treatments.

Immune response to cancer:Immune response to cancer:

- Effect on metastasis: “double-edged sword”.- Effect on metastasis: “double-edged sword”.

Tumour is a heterogeneous system Tumour is a heterogeneous system distributed distributed parameters.parameters.


Questions / IssuesQuestions / Issues

Is the “Computer Metaphor” adequate?Is the “Computer Metaphor” adequate?

Oyama: [A computer program] has features […] for deciding Oyama: [A computer program] has features […] for deciding outcomes outcomes just because a computer lacks the biological just because a computer lacks the biological structure and dynamics of an organismstructure and dynamics of an organism. .

G. Kampis: G. Kampis: -Living systems able to evolve & re-define their state space.-Living systems able to evolve & re-define their state space. -Representation problem.-Representation problem. -External vs. internal programming: mimicking vs. evolution.-External vs. internal programming: mimicking vs. evolution. -Not simply manipulating interfaces to deal with complexity (“if -Not simply manipulating interfaces to deal with complexity (“if

you can define it, we can model it”).you can define it, we can model it”).


How To Model Cancer How To Model Cancer In SilicoIn Silico? ?

Choosing the scale: cancer cell / tumour / organism.Choosing the scale: cancer cell / tumour / organism.

Set of functions describing the relations between Set of functions describing the relations between different variables and adjacent levels. different variables and adjacent levels.

Predictive model: deterministic or stochastic?Predictive model: deterministic or stochastic?

Signalling pathways as networks of multivariable Signalling pathways as networks of multivariable Boolean switches.Boolean switches.

Cellular Automata (CA) – type machine models.Cellular Automata (CA) – type machine models.

Agent-based models (Individual-Based Models). Agent-based models (Individual-Based Models).

Weighted inputs into Neural Network Weighted inputs into Neural Network training. training.

Bi-directional data flow Bi-directional data flow Recurrent Network. Recurrent Network.

Lupina


Creating A Cancer Model - BenefitsCreating A Cancer Model - Benefits

““Interactome”.Interactome”.

Turning data into animated system behaviour.Turning data into animated system behaviour.

Different models can be built & tested against Different models can be built & tested against experimental / clinical data.experimental / clinical data.

Predictive models and novel therapeutics: e.g. cell cycle Predictive models and novel therapeutics: e.g. cell cycle phase-specific chemotherapy (Gardner, 2002).phase-specific chemotherapy (Gardner, 2002).

Introduction of personalised treatments.Introduction of personalised treatments.

Mapping lower-level states to higher-level states.Mapping lower-level states to higher-level states.

Understanding control & decision mechanisms in cancer Understanding control & decision mechanisms in cancer and living systems in general.and living systems in general.

Integration of research domains.Integration of research domains.


TrendsTrendsSince 1960’s:Since 1960’s:

-Models of solid tumours (collections of cells feeding from -Models of solid tumours (collections of cells feeding from nutrient supply).nutrient supply).

Since 1980’s: Since 1980’s:

-Models of angiogenesis/metastasis.-Models of angiogenesis/metastasis.

Since 1990’s: Since 1990’s:

-Specific, data-oriented models.-Specific, data-oriented models.

-Interconnected cell cycle signal transduction pathways -Interconnected cell cycle signal transduction pathways across spatial & temporal scales of organisation.across spatial & temporal scales of organisation.

-Fast increasing!-Fast increasing!


Macroscopic Models Macroscopic Models Predictive epidemiological models from screening & clinical data.Predictive epidemiological models from screening & clinical data.Phenomenological modelling of tissue & tumour growth aspects.Phenomenological modelling of tissue & tumour growth aspects.Regression prediction based on tissue architecture: Regression prediction based on tissue architecture: http://www.bccrc.ca/ci/tm01_results2.htmlhttp://www.bccrc.ca/ci/tm01_results2.html3D simulation of tumour response to treatment (radiotherapy 3D simulation of tumour response to treatment (radiotherapy targeting).targeting).Fractal morphometry applied to tumoursFractal morphometry applied to tumours

-E.g. fractal tumour boundary a prognostic tool: -E.g. fractal tumour boundary a prognostic tool: http://www.europhysicsnews.com/full/09/article1/http://www.europhysicsnews.com/full/09/article1/

Greece : Greece : -”-”In SilicoIn Silico Oncology” Group: Oncology” Group: www.in-silico-oncology.iccs.ntua.gr/www.in-silico-oncology.iccs.ntua.gr/ -Simulation models of the evolution of malignant tumours -Simulation models of the evolution of malignant tumours inin vitrovitro

& & in vivoin vivo and their response to various therapeutic modalities & and their response to various therapeutic modalities & mapping to genetic status. mapping to genetic status.


Cellular ModelsCellular ModelsContinuous dynamics:Continuous dynamics:

-Kinetic models of cell population growth (differential equations).-Kinetic models of cell population growth (differential equations). -Topological models of cell population growth emphasising chaotic -Topological models of cell population growth emphasising chaotic

behaviour.behaviour.CA-based models of cell population growth.CA-based models of cell population growth.

Zajicek: Zajicek: http://www.what-is-cancer.com/papers/contents/http://www.what-is-cancer.com/papers/contents/ Extensive simulation of homeostatic mechanisms in stem cells.Extensive simulation of homeostatic mechanisms in stem cells. CA model of stem cell division: the “Proliferon” (Starlogo).CA model of stem cell division: the “Proliferon” (Starlogo).

CA model of immune system & hypersensitivity to chemotherapy: CA model of immune system & hypersensitivity to chemotherapy: www.imbm.org/PDF/chap12.pdfwww.imbm.org/PDF/chap12.pdf

Cancer detection via determination of fractal cell dimension: Cancer detection via determination of fractal cell dimension: www.pa.msu.edu/~bauer/cancer/cancer.pdfwww.pa.msu.edu/~bauer/cancer/cancer.pdf


Subcellular Models Subcellular Models

Time-course experiments on mRNA abundance & protein Time-course experiments on mRNA abundance & protein activity.activity.Mapping mutational profiles to therapeutic combinations.Mapping mutational profiles to therapeutic combinations.

Tyson & Novak (1996): Tyson & Novak (1996): http://www.euchromatin.org/Ciliberto01.htmhttp://www.euchromatin.org/Ciliberto01.htm

Eukaryotic cell cycle checkpoint controls modelled in yeast using Eukaryotic cell cycle checkpoint controls modelled in yeast using partial differential equations (XML).partial differential equations (XML).Tyson & Novak (2002): Tyson & Novak (2002): http://www.euchromatin.org/Ciliberto01.htmhttp://www.euchromatin.org/Ciliberto01.htm

Irreversible transitions in & out of cell cycle controlled by Irreversible transitions in & out of cell cycle controlled by hysteresis in control mechanisms – verified experimentally.hysteresis in control mechanisms – verified experimentally.

Increasing number of databases, modelling projects on genetic Increasing number of databases, modelling projects on genetic regulation, biochemical pathways, signal transductions. regulation, biochemical pathways, signal transductions.

http://www.brc.dcs.gla.ac.uk/projects/bps/links.htmlhttp://www.brc.dcs.gla.ac.uk/projects/bps/links.html


Proteomic Co-expression of GenesProteomic Co-expression of GenesWarenius Warenius et.al.et.al. (2004). (2004).

Human cell lines (normal keratinocytes vs. 19 cancer lines)Human cell lines (normal keratinocytes vs. 19 cancer lines)


““Neostasis”Neostasis”Warenius Warenius et.al.et.al. (2004). (2004).

Following immortalisation, neostasis is the stabilisation necessary for Following immortalisation, neostasis is the stabilisation necessary for continued cancer cell growth and survival.continued cancer cell growth and survival.


Random Graph Models Of Protein Random Graph Models Of Protein InteractionsInteractions

Warenius, Zito, Kyritsi (to be submitted).Warenius, Zito, Kyritsi (to be submitted).

Based on A.L. Barabasi’s work on Scale-Free (SF) Networks.Based on A.L. Barabasi’s work on Scale-Free (SF) Networks.

Plotted protein expression data as Connection or No Plotted protein expression data as Connection or No Connection (cut-off points: r>0.5, p<0.05).Connection (cut-off points: r>0.5, p<0.05).

Results: Results:

-Cancer cells: -Cancer cells: P(k)P(k) decays following a power law (SF). decays following a power law (SF).

-Normal cells: no SF properties.-Normal cells: no SF properties.

Redundancy and attack survivability.Redundancy and attack survivability.

““Hubs” may be “Critical Normal Gene” products suitable for Hubs” may be “Critical Normal Gene” products suitable for therapeutic targeting?therapeutic targeting?

More work to be done.More work to be done.


Modelling The Knowledge DomainModelling The Knowledge Domain

R. Paton: R. Paton: -Metaphors in scientific thinking.-Metaphors in scientific thinking. -Modelling: representation of one thing in terms of another.-Modelling: representation of one thing in terms of another. -So, language of models is metaphorical by definition.-So, language of models is metaphorical by definition. -Pluralistic approach -Pluralistic approach richness of knowledge. richness of knowledge. -Displacement of ideas across knowledge domains.-Displacement of ideas across knowledge domains. -Recursive relations between systemic metaphors in the -Recursive relations between systemic metaphors in the

biosciences: biosciences: E.g. machine-as-text E.g. machine-as-text ↔↔ text-as-machine text-as-machine organism-as-machine organism-as-machine ↔↔ machine-as-organism. machine-as-organism.

Category Theory: formal tool / language / conceptual Category Theory: formal tool / language / conceptual framework for study of structures & systems of structures. framework for study of structures & systems of structures.


Modelling The Knowledge DomainModelling The Knowledge Domain

CELL CYCLE CHECKPOINT

CELL CYCLE REGULATION

CELL CYCLE

CELL PROLIFERATIONCELL GROWTH / MAINTENANCE

CELL PHYSIOLOGICAL PROCESS

PHYSIOLOGICAL PROCESS

CELLULAR PROCESS

BIOLOGICAL PROCESS

ALL ALL

is_a

is_a

is_a

is_a

is_a

is_a

is_apart_of

part_of

is_a

Gene Ontology

Consortium


Systems Biology Modelling ToolsSystems Biology Modelling ToolsCellML: CellML: http://www.cellml.org/http://www.cellml.org/

OSS XML-based standard for storage & exchange of computer-OSS XML-based standard for storage & exchange of computer-based mathematical models.based mathematical models.

BioPAX: BioPAX: http://www.biopax.org/http://www.biopax.org/ OSS OWL-based ontology for metabolic pathway & molecular data.OSS OWL-based ontology for metabolic pathway & molecular data.

SBML (Systems Biology Markup Language): SBML (Systems Biology Markup Language): http://sbml.org/http://sbml.org/ OSS XML-based ontology for models of biochemical reaction OSS XML-based ontology for models of biochemical reaction

networks. networks.

SBW (Systems Biology Workbench): SBW (Systems Biology Workbench): http://64.17.162.114/research/http://64.17.162.114/research/ Free C++ framework for communication & cooperation of application Free C++ framework for communication & cooperation of application

components (written in diverse languages). components (written in diverse languages).

SCIpath: SCIpath: http://www.ucl.ac.uk/oncology/MicroCore/http://www.ucl.ac.uk/oncology/MicroCore/ OSS Java-based suite ofOSS Java-based suite of programs for microarray data analysis. programs for microarray data analysis.


Summary Summary

Main focus: flow of information & control.Main focus: flow of information & control.

Integration of different hierarchical levels.Integration of different hierarchical levels.

Right assumptions.Right assumptions.

Well-defined parameters.Well-defined parameters.

Hypothesis- or Data-driven approach?Hypothesis- or Data-driven approach?

Which side (realism, precision, generality) to “sacrifice” in Which side (realism, precision, generality) to “sacrifice” in modelling?modelling?

-E.g. may need to narrow down to one type of cancer to -E.g. may need to narrow down to one type of cancer to avoid “idealised” descriptions.avoid “idealised” descriptions.

Pluralistic models: an interdisciplinary approach.Pluralistic models: an interdisciplinary approach.


References 1References 1

-Albert R. et.al. (2000). Error and attack tolerance of complex networks. -Albert R. et.al. (2000). Error and attack tolerance of complex networks. Nature 406: 378 – 382.Nature 406: 378 – 382.

-Barabasi A.L., Oltvai Z.N. (2004). Network biology: understanding the cell’s -Barabasi A.L., Oltvai Z.N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics 5: 101-113.functional organization. Nature Reviews Genetics 5: 101-113.

-Bhalla et.al. (1999). Emergent properties of networks of biological signalling -Bhalla et.al. (1999). Emergent properties of networks of biological signalling pathways. Science 15: 283(5400): 381-387.pathways. Science 15: 283(5400): 381-387.

-Camphausen K. et.al. (2001). Radiation therapy to a primary tumour -Camphausen K. et.al. (2001). Radiation therapy to a primary tumour accelerates metastatic growth in mice. Cancer Res 61(5): 2207-11.accelerates metastatic growth in mice. Cancer Res 61(5): 2207-11.

-Deighton K.J. (1975). Cancer – a systemic disease with local manifestations. -Deighton K.J. (1975). Cancer – a systemic disease with local manifestations. Med Hypotheses 1(2): 37-41. Med Hypotheses 1(2): 37-41.

-Dioguardi N. (1992). Fegato a piu dimensioni. Etaslibri-RCS Medicina, -Dioguardi N. (1992). Fegato a piu dimensioni. Etaslibri-RCS Medicina, Milano.Milano.

-Kitano H. (2004). Cancer as a robust system. Nature Reviews Cancer 4: -Kitano H. (2004). Cancer as a robust system. Nature Reviews Cancer 4: 227-235.227-235.

-Oltvai Z.N., Barabasi A.L. (2002). Systems biology: life’s complexity pyramid. -Oltvai Z.N., Barabasi A.L. (2002). Systems biology: life’s complexity pyramid. Science 298(5594): 763-4. Science 298(5594): 763-4.


References 2References 2

-Paton R. et.al.(1998). Information processing in computational tissues. In: -Paton R. et.al.(1998). Information processing in computational tissues. In: Information processing in cells and tissues, Plenum Press, New York.Information processing in cells and tissues, Plenum Press, New York.

-Struikmans et.al. (1999). Regional heterogeneity and intra-observer -Struikmans et.al. (1999). Regional heterogeneity and intra-observer variability of DNA-content and cell proliferation markers determined by variability of DNA-content and cell proliferation markers determined by flow cytometry in head and neck tumours. Oral Oncol. 35: 217-213. flow cytometry in head and neck tumours. Oral Oncol. 35: 217-213.

-Tyson J.J. et.al. (1996). Chemical kinetic theory: understanding cell-cycle -Tyson J.J. et.al. (1996). Chemical kinetic theory: understanding cell-cycle regulation. Tr Biochem Sci 21(3): 89-96.regulation. Tr Biochem Sci 21(3): 89-96.

-Warenius H. et.al. (2002). Are Critical Gene Products in cancer cells the -Warenius H. et.al. (2002). Are Critical Gene Products in cancer cells the real therapeutic targets? Anticancer Res. 22(5): 2651-5. real therapeutic targets? Anticancer Res. 22(5): 2651-5.

-Warenius H. et.al. (to be submitted). Heterogeneity of therapeutic response -Warenius H. et.al. (to be submitted). Heterogeneity of therapeutic response in human cancer cell lines: relationship to RNA and protein profiles.in human cancer cell lines: relationship to RNA and protein profiles.

-Wolkenhauer O. (2002). Simulating what cannot be simulated. Dagstuhl -Wolkenhauer O. (2002). Simulating what cannot be simulated. Dagstuhl Position Statement. Position Statement.

-Zajicek G. (1994). Wisdom of the Body. Cancer J. 7: 212-213. -Zajicek G. (1994). Wisdom of the Body. Cancer J. 7: 212-213. -Zimmerman L.E. et.al. (1978). Does enucleation of the eye containing a -Zimmerman L.E. et.al. (1978). Does enucleation of the eye containing a

malignant melanoma prevent or accelerate the dissemination of tumour malignant melanoma prevent or accelerate the dissemination of tumour cells? Br J Ophthalmol 62(6): 420-5.cells? Br J Ophthalmol 62(6): 420-5.

SYSTEMS MODELLING OF CANCER Leto Kyritsi. 2May 2005ECCO Group (VUB) Talk Outline 1.Cancer. 2.Systems...

Documents

Transcript of SYSTEMS MODELLING OF CANCER Leto Kyritsi. 2May 2005ECCO Group (VUB) Talk Outline 1.Cancer. 2.Systems...