Systems Biology
Where Computer Science meets Biology
Professor Adriana Compagnoni
Computer Science Department
With:
Joe Glavy, Svetlana Sukhishvili, Tommy White
Amanda DiGuilio (CCBBME)
Yifei Bao, Vishakha Sharma, Justin Sousa, and
Peter Zafonte (CS)
Overview
• 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
HER2
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 Model
Overview of different species
Differential 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 )
Pathways Crosstalk
Computational Modeling
of Bio-films
for Drug Delivery
Hydrogen-bonded multilayer destruction
Changing pH
fast release of
capsule cargo
3.2
μm
3.2
μm
Computational Modeling For The
G-protein Cycle
Using 5 different modeling techniques:
• Differential equations
• Stochastic Pi-calculus
• Stochastic Petri-Nets
• Kappa
• Bio-Pepa
12
G-Protein Couple Receptors(GPCRs)
• 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.
13
Activation cycle of G-proteins by Gprotein-coupled receptors
2
1
3
5
4
14
Chemical Reactions and Rates
15
The law of mass action


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+Yk 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.
16
Ordinary Differential Equations
(ODEs)
17
Process 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.
18
Process 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.
19
Process Algebras
 A and B bind
A = new msg !ch (msg); Ab(msg)
B = ?ch(msg); Bb (msg)
 A and B dissociate
Ab(msg) = !msg(); A
Bb(msg) = ?msg(); B
20
Stochastic 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.
Step 1 to Step 2
2
1
!bindb
Gd
Gbg
G
?bindb
Gd represents alpha
Gbg represents the beta-gamma complex
22
Process modeling for G-protein Cycle
23
Process modeling for G-protein Cycle
24
Process modeling for G-protein Cycle
25
Process modeling for G-protein Cycle
26
Process modeling for G-protein Cycle
27
Process modeling for G-protein Cycle
28
SPiM
directive plot RL(); abrD(); aT()
Petri 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).
30
Petri Nets Modeling
NJPLS2010@Stevens
Petri Nets Modeling
NJPLS2010@Stevens
Petri Nets Modeling
NJPLS2010@Stevens
Petri Nets Modeling
NJPLS2010@Stevens
Petri Nets Modeling
NJPLS2010@Stevens
Petri Nets Modeling
NJPLS2010@Stevens
Kappa 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 Language Modeling
R + L -> RL
R(r), L(l)->R(r!1), L(l!1) @ 3.32e-09

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.
Kappa Language Modeling
Bio-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 Modeling
Bio-PEPA Modeling
Result (ODEs and Pi-calculus)
Result (Petri Nets and Kappa)
Result (Bio-PEPA)
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 Picalculus.
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.
G-protein Cycle
2
bind
bind
1
3
activate and
dissociate
4
hydrolyze
5
dissociate
48
Step 1 to Step 2
2
1
bind(Gd, Gbg, G, 1.0)
49
High 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)
High Level Notation
High Level Notation
Incorporating Space
We are currently modifying the Ocaml implementation of SPiM to
Add a notion of Space (Affine Geometry + Process Algebra)
Conclusions
 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.
Conclusion
 The high level notation that we designed is
a domain specific notation that we
developed for the G-protein cycle.
Thank you!
Questions?
Overview
 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
57