where computer science meets biology

Where Computer Science meets Biology

Systems Biology

Professor Adriana CompagnoniComputer Science Department

With: Joe Glavy, Svetlana Sukhishvili, Tommy WhiteAmanda DiGuilio (CCBBME)

Yifei Bao, Vishakha Sharma, Justin Sousa, and Peter Zafonte (CS)

OverviewOverview

• The Virtual Lab

Modeling HER2

• Modeling Bio-films

• Experimenting with 5 different modeling techniques

Modeling the G-protein Cycle

• Incorporating Space

Process model for HER2 Signaling Pathways

HER2HER2

HER2 – Signaling Pathways HER signaling is initiated when two receptor

molecules join together in a process called dimerization, which occurs in response to the presence of a growth factor molecule (ligand).

Dimerization of different members of ErbB family, lead to activation of different kinase mediated intracellular signaling cascades, like PI3 K/AKT pathway MAPK pathway JAK/STAT pathway

Process Algebra ModelProcess Algebra Model

Overview of different speciesOverview of different species

Differential HER2 concentrationsDifferential HER2 concentrations

a. 100 HER2 are initialized a. 30 HER2 are initialized

Inhibition of Raf activation(activation rate is changed from 27.0 to 1.0E-4 )Inhibition of Raf activation(activation rate is changed from 27.0 to 1.0E-4 )

Pathways Crosstalk

Computational Modeling of Bio-films

for Drug Delivery

Computational Modeling of Bio-films

for Drug Delivery

Hydrogen-bonded multilayer destruction

Changing pH

3.2 μm

3.2 μm

fast release of capsule cargo

Computational Modeling For The G-protein Cycle

Computational Modeling For The G-protein Cycle

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Using 5 different modeling techniques:

• Differential equations• Stochastic Pi-calculus• Stochastic Petri-Nets• Kappa• Bio-Pepa

G-Protein Couple Receptors(GPCRs)G-Protein Couple

Receptors(GPCRs)

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• G-protein couple receptors (GPCRs) sense molecules outside the cell and activate inside signal transduction pathways.

• An estimated 50% of the current pharmaceuticals target GPCRs.

Activation cycle of G-proteins by G-protein-coupled receptors

Activation cycle of G-proteins by G-protein-coupled receptors

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1

2

3

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Chemical Reactions and RatesChemical Reactions and Rates

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The law of mass actionThe law of mass action

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The law of mass action is a mathematical model that prescribes the evolution of a chemical system in terms of changes of concentrations of the chemical species over time.

In its simplest form, it says that a reaction

X+Yk Z has a rate k[X][Y]: the rate is proportional to the concentration of one species ([X]) times the concentration of the other species ([Y]) by the base rate k.

Ordinary Differential Equations (ODEs)

Ordinary Differential Equations (ODEs)

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Process AlgebrasAn alternative ot ODEsProcess Algebras

An alternative ot ODEs

Formal languages originally designed to model complex reactive computer systems.

Because of the similarities between reactive computer systems and biological systems, process algebra have recently been used to model biological systems.

Formal languages originally designed to model complex reactive computer systems.

Because of the similarities between reactive computer systems and biological systems, process algebra have recently been used to model biological systems.

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Process AlgebrasProcess Algebras

Typically two halves of a communication: Sending and Receiving.

!ch(msg) : to send message msg on channel ch.

?ch(msg) : to receive a message msg on channel ch.

Typically two halves of a communication: Sending and Receiving.

!ch(msg) : to send message msg on channel ch.

?ch(msg) : to receive a message msg on channel ch.

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Process AlgebrasProcess Algebras

A and B bind

A and B dissociate

A and B bind

A and B dissociate

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A = new msg !ch (msg); Ab(msg) B = ?ch(msg); Bb (msg)

Ab(msg) = !msg(); A Bb(msg) = ?msg(); B

Stochastic Pi-CalculusStochastic Pi-Calculus

The stochastic pi-calculus is a process algebra where stochastic rates are imposed on processes, allowing a more accurate description of biological processes.

SPiM (stochastic pi machine) is an implementation of the stochastic pi-calculus that can be used to run in-silico simulations that display the change over time in the populations of the different species of the system being modeled.

The stochastic pi-calculus is a process algebra where stochastic rates are imposed on processes, allowing a more accurate description of biological processes.

SPiM (stochastic pi machine) is an implementation of the stochastic pi-calculus that can be used to run in-silico simulations that display the change over time in the populations of the different species of the system being modeled.

Step 1 to Step 2 Step 1 to Step 2

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1

2

Gd

Gbg

G!bindb

?bindb

Gd represents alphaGbg represents the beta-gamma complex

Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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Process modeling for G-protein CycleProcess modeling for G-protein Cycle

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SPiMSPiMdirective plot RL(); abrD(); aT()

Petri Nets ModelingPetri Nets Modeling

The basic Petri Net is a directed bipartite graph with two kinds of nodes which are either places or transitions and directed arcs which connect nodes. In modeling biological processes, place nodes represent molecular species and transition nodes represent reactions. We use Cell Illustrator to develop our model of the G-protein cycle (www.cellillustrator.com).

The basic Petri Net is a directed bipartite graph with two kinds of nodes which are either places or transitions and directed arcs which connect nodes. In modeling biological processes, place nodes represent molecular species and transition nodes represent reactions. We use Cell Illustrator to develop our model of the G-protein cycle (www.cellillustrator.com).

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NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

NJPLS2010@Stevens

Petri Nets ModelingPetri Nets Modeling

Kappa Language ModelingKappa Language Modeling Kappa is a formal language for defining agents

(typically meant to represent proteins) as sets of sites that constitute abstract resources for interaction. It is used to express rules of interactions between proteins characterized by discrete modification and binding states. The Kappa language modeling platform is Cellucidate.

Kappa is a formal language for defining agents (typically meant to represent proteins) as sets of sites that constitute abstract resources for interaction. It is used to express rules of interactions between proteins characterized by discrete modification and binding states. The Kappa language modeling platform is Cellucidate.

Kappa Language ModelingKappa Language Modeling

R and L are agent names, r is the binding site of R, l is the binding site of L, and 3.32e-09 is the reaction rate. In R(r!1) and L(l!1), 1 is the index of the link that binds R and L at their binding sites.

R and L are agent names, r is the binding site of R, l is the binding site of L, and 3.32e-09 is the reaction rate. In R(r!1) and L(l!1), 1 is the index of the link that binds R and L at their binding sites.

R + L -> RLR(r), L(l)->R(r!1), L(l!1) @ 3.32e-09

Kappa Language ModelingKappa Language Modeling

Bio-PEPA ModelingBio-PEPA Modeling Bio-PEPA is a process algebra for the modeling

and the analysis of biochemical networks. It is a modification of PEPA to deal with some features of biological models, such as stoichiometry and the use of generic kinetic laws.

Bio-PEPA is a process algebra for the modeling and the analysis of biochemical networks. It is a modification of PEPA to deal with some features of biological models, such as stoichiometry and the use of generic kinetic laws.

Bio-PEPA ModelingBio-PEPA Modeling

Bio-PEPA ModelingBio-PEPA Modeling

Result (ODEs and Pi-calculus)Result (ODEs and Pi-calculus)

Result (Petri Nets and Kappa)Result (Petri Nets and Kappa)

Result (Bio-PEPA)Result (Bio-PEPA)

High Level Notations (motivation)

High Level Notations (motivation)

Both ODEs and Petri Nets correspond closely to chemical reactions, and for the average biologist, they are relatively easy to understand.

Cellucidate provides a friendly user interface for Kappa that abstracts away from its syntax.

SPiM still needs encoding in the stochastic Pi-calculus.

Both ODEs and Petri Nets correspond closely to chemical reactions, and for the average biologist, they are relatively easy to understand.

Cellucidate provides a friendly user interface for Kappa that abstracts away from its syntax.

SPiM still needs encoding in the stochastic Pi-calculus.

High Level Notation (motivation)High Level Notation (motivation)

In order to hide Pi-calculus communication primitives and enable modeling using only terminology directly obtained from biological processes, we develop a high level notation that can be systematically translated into SPiM programs.

In order to hide Pi-calculus communication primitives and enable modeling using only terminology directly obtained from biological processes, we develop a high level notation that can be systematically translated into SPiM programs.

G-protein CycleG-protein Cycle

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1

2

3

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bind bind

activate and dissociate

dissociate

hydrolyze

Step 1 to Step 2 Step 1 to Step 2

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1

2

bind(Gd, Gbg, G, 1.0)

High Level NotationHigh Level Notation

bind(Gd, Gbg, G, 1.0); bind(R, L, RL, 3.32e-6); activateAnddissociate(G, RL, Ga, Gbg, 1.0e-5); dissociate(RL, R, L, 0.01); hydrolyze(Ga, Gd, 0.11); degrade(R, 4e-4); degrade(RL, 4e-3)

bind(Gd, Gbg, G, 1.0); bind(R, L, RL, 3.32e-6); activateAnddissociate(G, RL, Ga, Gbg, 1.0e-5); dissociate(RL, R, L, 0.01); hydrolyze(Ga, Gd, 0.11); degrade(R, 4e-4); degrade(RL, 4e-3)

High Level NotationHigh Level Notation

High Level NotationHigh Level Notation

We are currently modifying the Ocaml implementation of SPiM to Add a notion of Space (Affine Geometry + Process Algebra)

Incorporating SpaceIncorporating Space

ConclusionsConclusions

The models we build using stochastic modeling approaches can represent the G-protein cycle quite convincingly, which shows that stochastic modeling approaches could be efficient instruments to assist in biomedical research.

In fact, because of the randomness of dynamic biological systems, stochastic modeling approaches can make the description of biological process much simpler and more accurate.

The models we build using stochastic modeling approaches can represent the G-protein cycle quite convincingly, which shows that stochastic modeling approaches could be efficient instruments to assist in biomedical research.

In fact, because of the randomness of dynamic biological systems, stochastic modeling approaches can make the description of biological process much simpler and more accurate.

ConclusionConclusion

The high level notation that we designed is a domain specific notation that we developed for the G-protein cycle.

The high level notation that we designed is a domain specific notation that we developed for the G-protein cycle.

Thank you!Questions?

Thank you!Questions?

OverviewOverview

G-protein-coupled Receptor G-protein Cycle Ordinary Differential Equations (ODEs)

Modeling Stochastic Pi-Calculus Modeling Petri Nets Modeling Kappa Language Modeling High Level Notation

G-protein-coupled Receptor G-protein Cycle Ordinary Differential Equations (ODEs)

Modeling Stochastic Pi-Calculus Modeling Petri Nets Modeling Kappa Language Modeling High Level Notation

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