MTech Report

86
Boolean Modeling and Simulation of Tumor Necrosis Factor- α Signaling Network Satyajit Rao 27 th June 2013

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Transcript of MTech Report

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Boolean Modeling and Simulation of Tumor

Necrosis Factor- α Signaling Network

Satyajit Rao

27th June 2013

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Abstract

Tumor necrosis factor- α is known for its anti tumor effects. Molecu-

lar details of TNF-α signaling pathway have been elucidated well. However,

mechanism of regulation between two opposing decisions, survival and death,

remains unclear. Understanding of this mechanism could lead to identifica-

tion of target molecules in order to favour apoptosis, and eventually to an

improvement in treatment. Using Boolean modeling, we try to compute dy-

namic steady states as a tool to predict a cell’s response to TNF-α ligation.

We analyse the steady states in a systematic manner, with the ultimate aim

of determining an optimal set of target nodes in the above stated interest.

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Contents

List of Tables iv

List of Figures v

1 Introduction 1

2 Basins of Attraction for Random Networks 4

2.1 Response to stimuli . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Dynamic Network Model . . . . . . . . . . . . . . . . . . . . . 5

2.3 Dynamic Equations . . . . . . . . . . . . . . . . . . . . . . . . 5

2.4 Attractors and basin of attraction . . . . . . . . . . . . . . . . 6

2.5 Numerical simulations . . . . . . . . . . . . . . . . . . . . . . 7

2.6 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Boolean Modeling and Simulation 11

3.1 Boolean formalism for qualitative modeling . . . . . . . . . . . 12

3.2 Structural Analysis . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.4 Network Reduction . . . . . . . . . . . . . . . . . . . . . . . . 15

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3.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.6 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.6.1 Housekeeping Constraint . . . . . . . . . . . . . . . . . 16

3.7 Simulations in MATLAB . . . . . . . . . . . . . . . . . . . . . 16

4 Methods of Quantitative Analysis of Attractors 18

4.1 Analysis of Distance . . . . . . . . . . . . . . . . . . . . . . . 18

4.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.3 Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.3.1 Steady State Stability . . . . . . . . . . . . . . . . . . 20

4.3.2 Asynchronous Markovian perturbation . . . . . . . . . 21

5 Case Study Results 23

5.1 Modeling TNF-α Network . . . . . . . . . . . . . . . . . . . . 23

5.2 Distance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2.1 min-Hamming Distance . . . . . . . . . . . . . . . . . 25

5.3 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.4 Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.4.1 Perturbation analysis for steady states . . . . . . . . . 26

5.4.2 Asychronous Markovian perturbation for cyclic attrac-

tors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.5 Phenotype switch . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.6 Toll-Like Receptor (TLR) Network . . . . . . . . . . . . . . . 30

6 Conclusions and future work 32

Bibliography 33

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Bibliography 34

Appendices 37

A A simulation example: TOYNET 38

B Identifying unsteady part of network 40

C TNF pathway 43

D List of Interactions 44

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List of Tables

5.1 Sets of nodes randomized or constrained while initializing . . . 24

5.2 Truth table for phenotype based classification of steady states 26

D.1 Table of Interactions in TNF-α signaling . . . . . . . . . . . . 44

D.2 Index of Species Alias used in Table D.1 . . . . . . . . . . . . 53

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List of Figures

2.1 Random network: distribution of size of basin of attraction . . 9

2.2 Basin of attraction distribution for Storkey & Valabregue rule 10

5.1 Flowchart for constructing Signaling Networks . . . . . . . . . 24

5.2 Distribution of Hamming distances for a set of attractors . . . 25

5.3 Histograms for transition fractions in TNF steady state sta-

bility analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.4 distribution of transition fractions returning to the same state

for asynchronous perturbations . . . . . . . . . . . . . . . . . 29

5.5 Transition fractions for TLR network . . . . . . . . . . . . . . 31

A.1 Interaction hypergraph of a sample network TOYNET . . . . 39

B.1 frequency with which a particular node is changing states in

a set of cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

B.2 Part of TNF network responsible for unsteady behaviour . . . 42

C.1 Figure shows a part of TNF-α signaling network in CellDesigner 43

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Chapter 1

Introduction

Interactions among biomolecules are being studied and elucidated at a very

fast pace. Complex networks of interactions involving both intracellular and

extracellular biomolecules (signaling networks) emerging out of these stud-

ies, give us clues as to how a cell reacts to environmental stimuli. Tools for

studying biological functions and reaction mechanisms of these biomolecules

are very well established. It is the integrated behaviour of these networks as

a whole, however, that has not been studied to a large extent.

Leaving the study of individual biological entities, a paradigm of study

of biological systems as a whole, has emerged as the field of Systems Biol-

ogy. This field uses Mathematical and Computational modeling techniques to

attempt to extract biological understanding from an integrated system’s per-

spective. The modeling formalism used is usually correlated with the amount

of data one has about the system. Various formalisms exist, one extreme be-

ing Ordinary Differential Equation paradigm, which is a true representation

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of interactions in a chemical reaction format. This is one extreme since it re-

quires a huge amount of kinetic data to completely model one network. The

other extreme is to compute graphical representations for networks, which

is a data-driven regression approach as opposed to the specificity of ODEs.[11]

Modeling of networks in a logic-based way is an approach lying between

the two extremes, in that it requires minimal data about the reaction ki-

netics, yet it can be used to study dynamics and make predictions on the

network’s behaviour. First used by Kauffman [8] to model gene regulation

process, this modeling technique has garnered a lot of interest, since it came

to light that certain dynamic steady states from logic-based model might

correspond to specific cell phenotypes.[7]

TNF-α is a multifunctional cytokine playing a key role in apoptosis as

well as cell survival in addition to inflammation and immunity. Antitumor

activity of TNF-α was shown in 1975 when William Coley found “Coleys

toxin”, whose active component was isolated as TNF-α, caused haemorrhagic

necrosis of mice tumors [13]. It is known to exert cytotoxicity towards some

cell lines in vitro while causing hemorrhagic tumor necrosis in vivo without

affecting normal cells [14]. Though the complete underlying mechanism is

unknown, the potential selective antitumoral activity has created interest

in the cytokine. Currently TNF-α is used synergistically with chemother-

apy drugs in regional treatment of locally advanced soft tissue sarcomas and

metastatic melanomas to avoid limb amputation [19].

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In this project, we curate, model and simulate a Boolean network of Tu-

mor Necrosis Factor - α signaling. The aim of the project is to find a set of

most likely targets which can switch a cell’s fate from survival to apoptosis

upon ligation by TNF-α. From the simulations, we find steady states and

limit cycles and conduct quantitative analysis of their stability. We also qual-

itatively classify the steady states based on the two end responses of TNF-α

signaling: apoptosis and survival, using biological markers. We conduct a

similar modeling and simulation exercise on another signaling network (Toll-

like receptor, TLR) and perform the same stability analysis.

Chapter 2 contains a graph theoretical exercise which deals with model-

ing and simulating undirected random networks. We create attractors using

some learning rules, and then study their stability to random stimuli (pertur-

bations) using dynamic network models. Chapter 3 discusses the formalism

of Boolean modeling and provides details of the simulation procedure.

Chapter 4 describes methods of quantitative analysis of attractors found

using synchronous simulation procedure. Chapter 5 presents the results ob-

tained by simulating real networks, TNF-α and TLR.

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Chapter 2

Basins of Attraction for

Random Networks

2.1 Response to stimuli

It is important for a network to respond with high sensitivity for small but

important stimuli as well as with robustness to large stimuli. Scale free

topologies that are often found in nature, are seen to enable high sensitivity

to directed stimuli yet protecting the network from failure to random stimuli.

Lot of recent studies have gone into studying topology of network formed by

links (connections) between nodes. The distribution of these links, the degree

distribution, carries important information about the networks capacity. [2]

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2.2 Dynamic Network Model

We use the Little-Hopfield model [10],[6]. This assumes that each node is

influenced by the states of other nodes to which it is linked. Such models are

useful to study the properties of multiple network states as attractors of the

dynamics [2]. An undirected graph with N labelled nodes n edges distributed

such that degree distribution is exponential or scale free, is the system. Each

node has two internal Ising spin states of σi = ±1 [4] The topology of the

network is stored in a network adjacency matrix I with element Iij = 1 if i and

j are nearest neighbours else zero [20]. The influence of nearest neighbours

depends on the patterns stored. In general we can store any number of

patterns using Hebbian learning rule of neural networks

Jij = Iij1√p

p∑=1

ξµi ξµj (2.1)

2.3 Dynamic Equations

Local field is defined at each vertex i by

hi(t) =∑j

Jijσj(t) (2.2)

Where σj(t) is the spin state at time t at vertex j. So the field is a weighted

sum of all spins states of nearest neighbouring nodes, the weights (Jij) being

determined by the p stored patterns. Parallel dynamics are used in which,

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at time t, all spins are updated synchronously using Glauber dynamics as[5],

σi(t+ ∆t) =

+1 with probability

(1 + exp

(−2hi(t)T0

))−1−1 with probability

(1 + exp

(2hi(t)T0

))−1 (2.3)

We use the local majority rules for time evolution of spin patterns, which is

actually Glauber dynamics at zero temperature. Thus,

σi(t+ 1) =

+1 if hi(t) > 0

−1 if hi(t) < 0

(2.4)

If however, hi(t) = 0 we select the spin of that state to be +1 or -1 with

equal probability. [20]

2.4 Attractors and basin of attraction

A subset of state space to which orbits originating from typical initial condi-

tions tend as time increases is called an attractor. For an attractor, the set

of initial conditions with long time behaviour approaching the attractor is

called its basin of attraction. An attractor can be identified with a functional

state of the system.[2]

Impact of external stimuli can be understood through response of a sys-

tem to perturbation as a measure of robustness for the process of switching

between attractors. Since this is an undirected network, an edge between i

and j is not different from an edge between j and i. hence it can be deduced

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that the influence matrix J is symmetric.

A particular state of a network is a collection of spins of all the N nodes.

A pattern is a state which is a functional state (and so an attractor of the

dynamics) to be learnt by a network. For the current simulation, two random

states are chosen from the state space and are learnt as patterns. This learn-

ing sets the pairwise influence between nodes Jij such that they are stable

states of the network.

For sufficiently large number of links and for a broad range of network

topologies, this form of non-zero pairwise influence will make the randomly

selected patterns into attractors [2]. Now that the attractors are set, we

can confirm that these are attractors by measuring size of their basin of

attraction. A stimulus is modelled by flipping certain number of nodes of

the pre-selected patterns and the resulting state is used as initial state for

the dynamical equations. Size of basin of attraction for a given attractor

is that number of nodes that need to be flipped before the dynamics of the

network fails to bring it back to the attractor state.

2.5 Numerical simulations

A 200 node random network with connection probability of 0.04 was created.

The expected number of nodes n is 796. 1000 simulations were carried out

for random stimuli (flipping random nodes). The number of nodes flipped

was increased until the dynamics failed to bring it back to original state.

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Simulation procedure:

1. Generate a random network adjacency matrix was generated with pa-

rameters N=200, p=0.04

2. Generate two random state vectors out of the state space to store as

patterns in the adjacency matrix

3. Proceed to make them attractors by the Hebbian imprinting rule

4. Flip n nodes to get an initial state for dynamical equations, update

spins using dynamical equations.

5. If the state doesnt come back to the attractor, that n is the size of basin

of attraction. If it does come back, increase n and repeat procedure

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2.6 Simulation results

Figure 2.1: histogram obtained for a 1000 simulations of a 200 node network

with connection probability 0.04. B is the size of basin of attraction (in

number of nodes)

A histogram for size of basin of attraction in 1000 simulations was obtained.

The histogram is approximately centred around 100, this shows that basin of

attraction for a random network to a random stimulus is ≈ 50% of the nodes.

The same algorithm was employed, but with a different learning rule-

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Storkey and Valabregue [17] given by:

J0ij = 0∀i, j and Jνij = Jν−1ij +

1

nξνi ξ

νj −

1

nξνi h

νji −

1

nhνijξ

νj (2.5)

where

hµij =n∑

k=1,k 6=i,j

Jµ−1ik ξµk (2.6)

The distribution of sizes of basin of attraction for this rule is shown in 2.2

Figure 2.2: Basin of attraction distribution for Storkey & Valabregue rule

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Chapter 3

Boolean Modeling and

Simulation

With the increase in data available about biological interactions due to ad-

vances in technology, disciplines like Systems Biology have emerged. Such

disciplines try to model and analyze the immense amount of data using math-

ematical modeling techniques. Gene regulation, metabolism, signal trans-

duction are being studied at a high rate giving rise to the need for coherent

representation of whole systems.[15] Modeling complex interactions of a large

number of proteins is intuitively represented by a network. Interactions in a

network are represented by directed edges, pointing in the direction of mass

(flux) transfer or signal propagation. Species in the network are represented

by nodes. Further, an edge can have a sign along with a direction, with

positive sign modeling activation and negative, inhibition.[15]

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3.1 Boolean formalism for qualitative model-

ing

A set of biological interactions is represented by a network. For further anal-

ysis however, the graph must be converted into a set of equations, i.e. a

model. A signal transduction network can be modeled either in a discrete

or a continuous space. Continuous models include ODEs for the different

reactions while discrete models include Boolean models and Petri Nets [16].

A qualitative modeling formalism represents each entity by a variable which

can only take up a finite set of values. However, the set of values may not

be linearly correlated with the actual concentrations of the entity.

The continuous ODE model requires a large amount of kinetic data which

is rarely available. Hence Boolean models introduced by S. Kauffman [8] and

R. Thomas [18] are preferred since they require no parameters. Each node

can have two distinct states: ON (1) and OFF (0) when their concentration

levels are above and below respective thresholds.

Boolean equations are constructed with the following rules:

• The AND condition models the situation where two or more species are

simultaneously required to be at high concentrations for the reaction

to occur.

St+1(1) = St(5) AND St(6)

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• The OR conditions model situations where any one species at high con-

centration is enough for the reaction to occur.

St+1(2) = St(3) OR St(4)

• NOT condition models the inhibition of one species by another.

St+1(3) = NOT St(7)

Thus, modeling signal transduction interactions as a network provides

a unified template to coherently represent the whole system. Next, it is

important to develop effective approaches to analyze the assembled network.

A network model can be subject to two types of analyses- structural and

dynamic.

3.2 Structural Analysis

A network model of biological interactions is as such a graph. Graph-

theoretical measures can thus be used to shed light on the topological or-

ganization of the network. Local topological measures provide information

on individual nodes (e.g. node degree) while global topological measures

provide information on the whole network. A degree distribution is a use-

ful property to measure. In particular the node(s) with the highest degree,

which upon removal can break the network down into multiple clusters.

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3.3 Dynamic Analysis

Time can be easily incorporated into a Boolean network model. Each node

has its Boolean function. That function is evaluated to get the updated value

(state) of the node.

St+1(i) = fi(S(t)) (3.1)

where St+1(i) is the state of the ith node at time (t+1) and fi is the Boolean

function for that node. S(t) is the vector of states of all N nodes.

The update paradigm can be of two types:

1. Synchronous: In synchronous update paradigm, it is assumed that

all species update simultaneously. In other words, the time scales of

all reactions in the signaling network are assumed to be equal. This

is of course, not accurate. Time scales of reactions can vary from

a few seconds to a few hours. In that sense, synchronous update is

deterministic. The synchronous paradigm is computationally efficient,

but it gives rise to spurious cycles. [9]

2. Asynchronous: To overcome the time scale drawback of synchronous

paradigm, asynchronous algorithms are used. Knowing time scale data

of some reactions, a metric can be assigned for each reaction, so that it

updates at time steps which are multiples of its metric. This is called

a deterministic asynchronous algorithm. Other algorithms include ran-

dom order asynchronous, general asynchronous, etc. Asynchronous

simulations are computationally exhaustive, especially for larger bio-

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logical systems, however they are able to replicate a wider range of

biological behaviours.

3.4 Network Reduction

In a Boolean framework, the number of possible states of a network (the state

space) is 2N where N is the number of nodes. Hence, it becomes impossible

to map entire state transition graph for networks of large size. In such cases

it is beneficial to reduce the number of nodes while keeping the essential

biological features intact. For example, nodes which have both in-degree and

out-degree equal to 1 can be clubbed with their respective preceding nodes.

This reduces the actual number of nodes (and hence the state space) in the

model but does not alter the model’s essence. some nodes attain the same

state irrespective of update method or initial condition. Such nodes can be

determined and removed.

3.5 Simulations

Simulation of Boolean network aims at generating a state transition graph.

An example simulation of a dummy network is included in Appendix. In the

following simulations we have implemented a synchronous update protocol

for the Boolean network model since the focus was on finding steady states

and limit cycles. Various kinds of asynchronous protocols can however be

implemented easily. Synchronous update protocols are prone to generating

spurious cycles [9], however steady states found using both protocols are the

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same.

3.6 Initialization

Synchronous update algorithm evaluates states of all species at time step

t+1 using old values for all updates, that is, values at time step t. Hence it

requires initialization of all nodes at t = 0. We randomly assign a value of 0

to each node with half probability.

3.6.1 Housekeeping Constraint

From literature we have knowledge of the initial state of some species. Some

constitutive proteins are known to be present in high concentrations, also

called Housekeeping species. We found 114 such species and added the con-

straint of keeping these nodes equal to 1 during initialization.

3.7 Simulations in MATLAB

There are many softwares for the purpose of Boolean simulations like Boolean-

Net(Python) , SimboolNet(Cytoscape plugin) , GINsim, etc. However, for

a large network, these softwares provided less flexibility. Hence, a set of

MATLAB codes was written for the dynamic simulations with algorithms

to detect steady states and cyclic attractors with filters for repeated steady

states/ limit cycles.

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Finding all attractors of the dynamics of large size networks (e.g.TNF-

α-330 nodes) is a herculean task. But simulating this way we can get as

many attractors as required, forming a fairly random sample. The results

obtained here are then subject to various analysis methods. The algorithm

of synchronous simulations is given below.

1. State of the network is stored as a vector, S of dimension N × 1.

2. To generate an initial state of the network, we first fix the values of all

Housekeeping nodes to 1.

3. The complete state of the network at S(t = 0) , S0 is generated using

a random number generator for the rest of the nodes.

4. Update equations are written in the form of a function file. The input

to that file is S(t) and the output is S(t+1)

5. State vectors after each time step are stored in a matrix, STG (State

transition graph) with the first column being S0, second S1 and so on.

6. At each update step, we check if S(t+1) is equal to any column of the

matrix STG. If S(t+1)=S(t), it is a steady state. Else if S(t+1) is

equal to any other column, it is a cycle.

7. If S(t+1) is not equal to any column in STG, we store S(t+1) in STG

and move to the next iteration, till an attractor is reached.

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Chapter 4

Methods of Quantitative

Analysis of Attractors

4.1 Analysis of Distance

We can hypothesize that each attractor represents a functional state of the

cell. It is useful to have a metric to measure the distance between two

attractors. We have used two types of distances:

1. min-Hamming distance:

Min-Hamming distance between two attractors is the minimum number

of nodes that must be flipped so as to change the state of the network

directly from one attractor to another. In other words, it is the number

of nodes which do not have the same state in both attractors.

2. Euclidean distance:

Considering a state of the network as an n × 1 vector, the Euclidean

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distance between two attractors is

d(Ai − Aj) =

√√√√ n∑k=1

(vik − vjk)2 (4.1)

where vi is the attractor state Ai represented as a vector.

4.2 Classification

The main aim of this project is to be able to identify ways to increase net-

work’s disposition to one of the end results (apoptosis) over the other (pro-

liferation). To enable this, we must first identify markers which differentiate

a apoptosis-bound cell phenotype from that which is proliferation-bound.

Caspase 8 is known for its role in initiation of apoptosis, but it is also

required in non-apoptotic roles. The role of caspase 8 along with Fas-

associated death domain (FADD), FLICE like inhibitory protein (FLIP),

receptor-interacting protein kinase 1 and 3 (RIPK1, RIPK3) was reconciled

with some previous findings by [12].

Following ligation of TNF Receptor, RIPK1 is recruited to form a re-

ceptor complex. This complex-I activates NF-κB transcription. FLIP is a

transcriptional target of NF-κB whose upregulation is responsible for the

non-apoptotic phenotype witnessed upon TNF ligation in most cell types.

[12] FLIP forms a heterodimer with caspase-8 and inhibits the downstream

activation of RIPK3 by RIPK1. If FLIP is blocked, TNF signalling follows

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the caspase 8 induced pro-apoptotic pathway. In the absence of caspase-8

however, RIPK1-RIPK3 signalling proceeds unchecked resulting in RIPK3

dependent programmed necrosis.

To summarize,

a High levels of FLIP: RIPK1-RIPK3 signalling inhibited by caspase 8-

FLIP heterodimer, leads to cell survival.

b Low levels of FLIP: caspase 8 activation proceeds unchecked, leads to

apoptosis.

c No caspase 8: RIPK3 induced programmed necrosis.

4.3 Stability Analysis

Similar to stability analysis of steady states in other mathematical mod-

elling paradigms, stability of steady states and limit cycles can be checked

in Boolean networks.

4.3.1 Steady State Stability

The steady states obtained from Boolean simulations are subject to per-

turbation, and the resulting perturbed state is dynamically simulated till it

reaches a steady state. Perturbation of an attractor Ai is done as follows.

• Only one node Ai(j),j ∈ (1, N) of the state is flipped at a time. Input

nodes, i.e. nodes which only have outgoing edges are not flipped, since

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they will always transition to a new attractor. Hence, for each attractor,

we will have N perturbed states Aip

Aip(j) = NOT(Ai(j)) (4.2)

• The perturbed state Aip is then subjected to dynamic simulation as an

input state.

• The resulting steady state/limit cycle is studied. A transition prob-

ability is calculated based on fractions of the N perturbations of Ai

returning to the same steady state or reaching a different steady state

or limit cycle.

Essentially, we study the dynamics of a subset of state space falling in the

region of unit Hamming distance from an attractor. The transition probabil-

ities thus help us define the shape of the basin around the attractor. If each

perturbation returns to the same attractor, we can conclude it is a completely

rounded basin.

4.3.2 Asynchronous Markovian perturbation

Synchronous Boolean framework does not account for the stochasticity that

is possible in state transitions. Markov processes have been used to analyze

gene regulatory networks[3]. Kervizic and Corcos (2008) [9] have described a

methodology that computes the stability of cycles. A set of cycles is chosen

as the state space for this analysis. Let S be the result of simulations. S is

a set of cyclic attractors, and C is a cycle having k states {s1, s2, s3, . . . , sk}.

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Each state in C, si is perturbed by sequentially reevaluating each species by

its own Boolean function one at a time, hence asynchronous.

It is possible that in asynchronously triggering Node i, si will not change

it’s state. The set of all input nodes will not be a part of this triggering since

they do not update. This is similar in methodology to steady state stability

analysis, only differing in the perturbation strategy. Here for each C∈ S

having k states,

sip(j) = fj(X(t)),∀j ∈ (1, N)&i ∈ (1, k) (4.3)

Thus each cycle C gives rise to Nk perturbed states. These Nk states are

simulated synchronously until they reach attractors. Again, as computed in

stability analysis of steady states, we compute fractions of kN perturbations.

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Chapter 5

Case Study Results

5.1 Modeling TNF-α Network

The signaling network of Tumor Necrosis Factor -α was constructed from

various studies available in literature. The representation was drawn using

CellDesigner. Figure 3.1 provides a flowchart of the procedure used for con-

struction of TNF-α network. 330 nodes take part in the complex network, of

which 216 nodes have Boolean equations associated with them. Hence, the

rest 114 nodes are all input nodes, with only outgoing edges. The Boolean

update rules for the 216 nodes were constructed by combining a subset of

330 nodes using logical operators AND, OR or NOT.

Next we study the set of nodes with housekeeping constraints (section

3.6.1). Of 114 housekeeping nodes, 84 are input nodes. Hence, these nodes

are effectively always ON in any simulation at any time step. Hence the

initialization procedure for TNF-α network is described in Table 5.1

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Figure 5.1: Flowchart for constructing Signaling Networks

SET Input nodes Non-input nodes Type TOTAL

SET A 30 84 constrained 114

SET B 84 132 randomized 216

TOTAL 114 216 - 330

Table 5.1: Sets of nodes randomized or constrained while initializing

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5.2 Distance Analysis

5.2.1 min-Hamming Distance

Figure 5.2: Distribution of Hamming distances for a set of attractors

Figure 5.2 shows the distribution of Hamming distance between pairs of

steady states. On an average, the distance between two attractors is approx-

imately 80 nodes, that is, two attractors have on an average 80 nodes (out

of 330) differing in states.

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5.3 Classification

The dynamics of a network goes through many states. While non-attractor

states are visited in a transient fashion, attractors are visited often and from

multiple paths. Attractors are also independent of the update paradigm

used, and hence can be identified with phenotypes. Phenotypes of interest

to us for the TNF-α network are: Apoptosis and Survival. The markers

for phenotypes are described in section 4.2. Accordingly, we classify steady

states with respect to the states of the marker nodes.

CASPASE-8 0 1 1 0

FLIP 0 0 1 1

Phenotype Necrosis Apoptosis Survival Necrosis

Table 5.2: Truth table for phenotype based classification of steady states

Of the 9638 steady states available, 45% (4411) attractors identify with

the apoptosis phenotype markers while the rest (5227) fall under the survival

category.

5.4 Stability Analysis

5.4.1 Perturbation analysis for steady states

Following the procedure listed in section 4.3, all steady states were subject

to unit perturbations per node. The distributions of transition fractions is

shown in Figure 5.3. The results for about 9600 steady states are obtained

as follows

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• For 90 % of the steady states (approx. 8500) the results are the same.

37% of 216 unit perturbations (79) return to the same steady state and

the rest 63% take up new steady states.

• The set of 79 (and hence rest 137) nodes is also the same for these 8500

attractors.

• The remaining 10% attractors show different results.

• We verified the possibility that the 8500 steady states might have the

same states for this set of 79 nodes, and vary only in the states of the

rest. It is a possibility since the set of 8500 states can easily fall in the

2137 state sub-space. However, that is not the case.

5.4.2 Asychronous Markovian perturbation for cyclic

attractors

Following the procedure in section 4.3.2, the set of cyclic attractors obtained

from synchronous simulations was analysed. It follows that input nodes

(nodes with outgoing edges only) will not be a triggered since they do not

have an update function. Thus we trigger each of the 216 updating nodes

asynchronously. The results obtained are shown in figure 5.4.

• Transition fraction returning to the same cycle is greater than 0.9 for

88% of state cycles.

• The same transition fraction is equal to 1 for 23% of state cycles

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Figure 5.3: Histograms for transition fractions in TNF steady state stabilityanalysis

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Figure 5.4: distribution of transition fractions returning to the same statefor asynchronous perturbations

• State cycles which have all perturbations transitioning back to the same

cycle are termed as absorbing states. Other states are concluded to be

spurious. [9]

5.5 Phenotype switch

Having classified the steady state attractors of TNF-α network, we look at

the possibility of phenotype switching. The procedure is same as the previous

section, however, the set of attractors classified under the ”Survival” category

is used in stead of the whole set of attractors. At each perturbation, we check

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against the classification markers and note if the new steady state matches

the markers of the switched phenotype, i.e. apoptosis. It is interesting to note

that for 5227×216 independent perturbations, not one incident of phenotype

switch is observed. The same computation done with the set of attractors

classified under apoptosis also yielded no instances of phenotype switch. It

follows that all the unit-perturbed states of both sets fall in the basin of

attraction of the respective phenotypes.

5.6 Toll-Like Receptor (TLR) Network

We have access to many curated signal transduction networks, and our lab

members have developed codes to generate Boolean equations from graphs.

One such network is that of Toll-like receptor. TLR network’s Boolean model

was simulated synchronously to get attractors. The stability analysis de-

scribed in section 4.3 was performed on steady state attractors of TLR net-

work as well.

• Figure 5.5 shows the transition fractions obtained from stability anal-

ysis. Subplots 1 and 2 show histograms of transition fractions to other

steady states and limit cycles respectively.

• For 92% of the 2460 steady states subject to this analysis, we have

obtained a transition fraction of 95% (of 1229) per attractor.

• Rest of the transitions all occur to limit cycles.

• For all 2460 × 1229 perturbations, the transition back to same steady

state is 0.

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Figure 5.5: The two plots show a histogram of transition fractions upon

producing unit perturbations. TLR network has 1229 nodes. 2460 steady

states were studied. x-axis is number of nodes as a fraction of 1229. Y- axis

is the number of times (out of 2460) that a particular fraction was obtained.

subplot 1 is for transitions to other steady states, subplot 2 is for transitions

to limit cycles. For the set of steady states studied, fraction of transitions

coming back to the same steady states is 0.

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Chapter 6

Conclusions and future work

From the examples of TNF-α and TLR networks, we can see that we have

developed a methodology to model and simulate Boolean networks to obtain

attractors: point as well as cycles, and examine their stability. This method-

ology can be applied to any known network, and we may observe systemic

properties of signaling networks in general. Since attractors may be corre-

lated with phenotypes, this methodology can be extended to finding domain

of attraction of a particular phenotype.

Future Work With the study of random networks, we found that it takes

approximately 50% node flips to leave a particular attractor’s basin and reach

another attractor. Though degree distributions of random networks and bio-

logical networks are significantly different, it is a fair result that a unit node

flip does not result in a switch from one phenotype’s basin of attraction to

another. Seeing as real networks have a degree distribution that is scale free

as opposed to a Poissonian for random networks [1], we can fairly predict

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that the number of node flips, on an average, will not be as high as 50% to

switch phenotype. It is then an interesting problem to identify, for a first,

a single instance of phenotype switch, and eventually to identify an optimal

set of nodes key to switch from survival phenotype to apoptosis.

Synchronous simulation is computationally efficient, but wrong in its as-

sumption of equal time scales of all reactions. Incorporating time-scales of

different reactions, i.e. a deterministic asynchronous algorithm, will generate

biologically more relevant attractors since it represents dynamics of a signal-

ing network more accurately than a simple synchronous algorithm.

As of now, we are scanning a large state space. However, the network

can be simplified by removing particular nodes and interactions, without

affecting the biological model. This procedure will require manual curation,

but a reduced network will increase computational efficiency.

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Appendices

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Appendix A

A simulation example:

TOYNET

Figure A.1 shows a sample interaction hypergraph with signed and directed

edges. The model equations for TOYNET can be written as follows:

A(t+1) = NOT D(t)

B(t+1) = A(t) AND I1(t)

C(t+1) = B(t) OR E(t)

D(t+1) = C(t)

E(t+1) = NOT I1(t) AND I2(t)

F(t+1) = E(t) OR G(t)

G(t+1) = F(t)

We have 9 nodes and 7 Boolean functions. Nodes I1 and I2 are input

nodes, they will not be updated. The state vector

S(t)=[I1(t) I2(t) A(t) B(t) C(t) D(t) E(t) F(t) G(t)]

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Figure A.1: Interaction hypergraph of a sample network TOYNET

keeps track of the state of the network. The network size is small, so is

the state space (29 = 512). A state number can be assigned to each vector,

which is the decimal counterpart of the binary number formed by S(t). For

example,

S = [1 0 0 0 0 1 1 0 0] will be termed State-268.

1. Initialize S randomly.

2. Calculate state number.

3. Obtain S(t+1) from S(t) from the Boolean equations

4. Update till an attractor is reached

Say S(0) = [1 0 1 1 0 1 0 0 1] or “361” (hereafter referred to by state number).

The state transitions which occur are: 361→ 53→ 89→ 381→ 125→ 125

hence state 125 is an attractor.

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Appendix B

Identifying unsteady part of

network

Initially, synchronous simulations would yield only limit cycles of length 4 for

all inputs. Hence, we estimated there must be a continuously cycling part of

the network. To detect that, we ran a simulation for all cycles which would

detect nodes which were changing their value from one state in a cycle to

another. The set of cycles C consists of m cycles {c1, c2, . . . , cm}of varying

lengths {l1, l2, . . . , lm,}. For each ci , ci(x)− ci(x+ 1) is computed and those

nodes for which this difference is not zero are noted for all x ∈ (1, li). Thus we

can compute the frequency with which a particular node is changing states

in a set of cycles.

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Figure B.1: This shows a frequency bar graph for a set of 500 cycles, with fre-

quency of appearance of a node plotted against node number. As is evident,

4 nodes are changing states in each of the 500 cycles.

We infer from the above bar chart that these 4 nodes are responsible for

the unsteady behaviour of the network. They are all connected and form a

part of the network which cannot attain steady state for any input values.

This part was later modified to reflect correct biological behavior. As can be

seen from figure B.2, this part of the network does not have a steady state

for any combination of inputs.

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Figure B.2: Part of TNF network responsible for unsteady behaviour

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Appendix C

TNF pathway

Figure C.1: Figure shows a part of TNF-α signaling network in CellDesigner

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Appendix D

List of Interactions

Table D.1: Table of Interactions in TNF-α signaling

Target

Node

Boolean Equation

SA39 SA40 |(SA31 & SA42 & SA539 ) |(SA31 & SA45 )

SA137 (SA136 & SA135 ) |(SA136 & SA177 ) |(SA136 & SA27 )

SA141 SA140 & SA139 & ˜(SA688 |SA700 )

SA134 ˜CSA2 |(SA133 & SA132 ) |(SA133 & SA156 ) |(SA133 &

˜SA292 )

SA152 (CSA31 & ˜SA291 ) |(CSA31 & SA151 )

SA158 (SA159 & ˜SA291 ) |(SA159 & SA135 ) |(SA159 & SA154 )

SA161 (SA162 & ˜SA291 ) |(SA162 & CSA31 )

CSA31 (SA145 & SA149 ) |(SA145 & SA150 )

SA222 (CSA36 & ˜SA220 ) |(CSA36 & SA445 & SA58 & SA125 )

SA223 (CSA36 & ˜SA220 ) |(CSA36 & SA445 & SA58 & SA125 )

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SA451 (SA450 & SA126 ) |(SA450 & SA127 ) |(SA450 & SA130 )

SA129 (˜SA530 ) |(˜SA291 ) |(SA128 & SA92 & SA90 ) |(SA128

& SA21 ) |(SA128 & SA51 )

SA58 (SA59 & SA21 ) |(SA59 & SA57 )

SA125 (˜SA92 ) |(SA124 & SA106 ) |(SA124 & SA21 ) |(SA124 &

SA51 )

SA73 (SA72 & SA65 ) |(SA72 & SA71 )

SA92 (SA88 & SA100 ) |(SA88 & SA102 )

SA91 (SA87 & SA77 ) |(SA87 & SA100 & SA102 )

CSA19 (CSA20 & SA31 ) |(CSA20 & SA76 ) |(CSA20 & SA84 &

SA58 )

SA117 (SA116 & SA115 ) |(SA116 & SA179 )

CSA22 SA108 |CSA15

SA254 SA243 |SA256

SA263 SA256 |SA284

SA276 SA243 |SA284

SA272 SA256 |SA284

SA146 SA141 |SA142

SA154 SA152 |SA157

SA250 SA247 |SA252 |SA249 |SA268 |SA266 |SA271 |SA246

|SA251 |SA255 |SA257 |SA260 |SA262 |SA261 |SA264

|SA267 |SA265 |SA269 |SA270 |SA143 |SA147

SA568 ˜SA567 |SA585

SA572 SA571 |SA608 |SA609 |SA607

SA291 ˜SA146 |˜SA144

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SA701 CSA3 |˜SA135

SA208 ˜SA205 |˜SA202 |SA129

SA77 SA539 |SA113 |(SA74 & CSA5 )

SA176 ˜SA21 |(SA175 & SA174 )

SA31 ˜SA135 |˜SA113 |(CSA3 & SA667 & SA38 )

SA38 ˜SA42 |˜SA113 |(SA37 & SA97 & SA58 )

SA452 (SA451 & SA126 ) |(SA451 & SA130 )

SA491 (SA492 & SA500 ) |(SA492 & SA503 )

SA49 ˜SA125 |(SA48 & SA47 )

SA57 ˜SA533 |(SA55 & SA56 & SA21 )

CSA29 ˜SA676 |˜SA677 |˜SA594 |(CSA27 & SA119 )

SA106 ˜SA125 |(SA105 & CSA11 )

CSA18 ˜SA113 |(CSA19 & SA76 & SA102 )

SA112 ˜SA113 |(SA111 & CSA5 )

SA273 SA284

SA245 SA243

SA281 SA284

SA253 SA284

SA274 SA284

SA282 SA284

SA287 SA284

SA279 SA284

SA275 SA243

SA280 SA284

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SA244 SA243

SA248 SA243

SA542 SA539

SA294 SA539

SA135 SA134

SA144 SA141

SA145 SA141

SA143 SA141

SA147 SA141

SA138 SA136 & SA129

SA139 SA137

SA19 SA18 & CSA1

SA142 SA138

SA148 SA151 & SA58

SA157 SA158

SA156 SA155 & SA157

SA21 SA20 & SA23

SA160 SA161

SA153 SA154 & SA125

SA163 SA156

SA23 SA22 & SA11

SA173 SA172 & SA17

SA174 SA173

SA179 SA180 & SA176

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SA177 SA178

CSA33 CSA32

SA178 CSA33 & SA179

SA175 CSA33 & CSA1 & SA132 & SA135 & SA160

SA25 SA24 & SA157

SA27 SA26

SA26 SA25 & SA19

CSA3 CSA1 & CSA2 & SA28 & SA29 & SA32

SA34 SA33 & CSA1

SA220 SA221 & SA222

SA32 SA31 & SA34

SA284 SA157

SA243 SA163

CSA1 SA2 & SA5

SA249 SA259 & SA284

SA258 SA259 & SA284

SA268 SA283 & SA284

SA36 SA35 & SA174

SA37 SA36

SA256 SA160

SA289 SA288 & SA129

SA35 CSA3

SA98 ˜SA208

SA45 SA44 & CSA1

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SA292 SA294

SA293 SA294

SA444 SA443 & SA446

SA445 CSA15

SA446 SA445

SA448 SA447 & SA130 & SA127

SA449 SA448 & SA130

SA453 SA452 & SA126

SA455 SA454 & SA126

CSA54 CSA52 & SA471

CSA55 CSA53 & SA471

CSA52 SA453 & SA470

CSA53 SA455 & SA470

SA486 SA485 & SA127 & SA126

SA485 SA484

SA492 SA493 & SA130

SA42 SA43 & CSA18

SA490 SA491 & SA500

SA489 SA490 & SA503

SA500 SA109

SA502 SA501 & SA58

SA503 SA502

CSA56 SA494 & SA492 & SA449

SA47 SA46 & CSA1

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SA2 SA1

SA539 SA538 & SA129

SA540 SA542

SA541 SA540 & SA130

CSA57 SA486 & SA541

SA51 SA50 & CSA1

SA560 SA562

SA561 SA560 & SA503

SA56 SA53 & SA49

CSA59 SA569 & SA568

SA570 CSA59

SA571 SA570

SA565 CSA58 & SA268

SA567 SA566 & SA565

SA566 SA559

SA577 SA550

SA578 SA558

CSA58 SA577 & SA578

SA581 CSA61 & CSA60

SA582 CSA61 & CSA60

CSA60 SA560 & SA580

SA595 SA596 & SA126

SA594 SA595 & SA503

SA59 SA58 & SA61

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SA607 SA603

SA608 SA604

SA609 SA605

SA61 SA60 & SA11

SA615 SA616

SA614 SA130 & SA615

SA128 SA614

SA63 SA62 & SA57

SA65 SA64 & SA68

SA616 ˜SA208

SA662 SA660 & SA125 & SA38

SA667 SA665 & SA663

SA663 SA662

SA68 SA69 & SA63

SA8 SA9 & CSA1

SA29 ˜SA146

SA676 SA533

SA677 SA288

SA71 SA70 & SA38

SA688 SA288

SA606 SA563

CSA17 ˜SA288

SA700 SA223

CSA5 CSA6 & SA29 & SA31

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CSA4 CSA5 & SA123

SA89 SA85 & SA100

SA76 SA75 & SA77

SA84 SA80 & SA77 & SA38

SA104 SA92 & SA58

SA7 SA6 & SA8

SA90 SA86 & SA102

SA81 SA84 & CSA19

CSA9 SA93 & SA94

SA97 SA96 & SA17

CSA11 CSA8 & SA23

SA96 SA97 & SA61

CSA7 CSA9 & SA65 & SA56

CSA12 CSA10 & SA445

SA11 SA10 & SA7

CSA8 CSA7 & SA97

CSA10 CSA11 & SA125

SA100 SA99 & SA38

SA99 ˜SA98

SA102 SA73 & SA31

CSA20 CSA14 & SA29 & SA112

CSA16 CSA17 & CSA18

CSA15 CSA16 & CSA25

CSA23 CSA15

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CSA24 CSA23 & CSA18

CSA21 CSA24 & SA445

CSA26 CSA21 & SA109

SA111 SA110

SA115 SA114 & SA31

SA119 SA118

CSA27 CSA30 & SA120

CSA30 CSA28 & SA444

CSA28 CSA26 & ˜SA565

SA15 SA14 & CSA1

SA120 SA117

SA118 SA121 & SA123 & SA125

SA123 SA122 & SA89 & SA91

SA127 SA123

SA126 SA125

SA130 SA129

SA132 SA131 & CSA3

SA17 SA16 & SA15

Table D.2: Index of Species Alias used in Table D.1

Species

Alias

Species Name

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CSA1 TNF-R1-STNF ALPHA BR COMPLEX COM ACT

C1-OUT CRD2 SIM PLAD SIM STNF ALPHA PRO

ACT MULTI TNF-R1 PRO ACT MULTI CRD3 SIM

CSA10 RAF COMPLEX COM C1-INS 14-3-3 PRO ACT B-RAF

PRO PHO PHO CRAF/RAF-1 PRO ACT EMP PHO

PHO PHO PHO PHO

CSA11 RAF COMPLEX COM ACT C1-INS CRAF/RAF-1 PRO

ACT EMP PHO PHO PHO PHO PHO PHO B-RAF PRO

ACT PHO 14-3-3 PRO ACT

CSA12 RAF COMPLEX COM C1-INS 14-3-3 PRO ACT B-RAF

PRO PHO PHO CRAF/RAF-1 PRO PHO PHO PHO

PHO PHO PHO

CSA14 IKK COMPLEX COM C1-INS IKK ALPHA BR IKK1

PRO EMP EMP MULTI IKK BETA BR IKK2 PRO EMP

EMP MULTI IKK GAMMA BR NEMO PRO EMP

MULTI ELKS PRO ACT HSP 90 PRO ACT MULTI

CDC37 PRO ACT

CSA15 NF- KAPPA B-I KAPPA B-PKA COMPLEX COM

C1-INS NF- KAPPA B COM I KAPPA B COMPLEX

COM PKAC PRO

CSA16 NF- KAPPA B-I KAPPA B-PKA COMPLEX COM

C1-INS I KAPPA B COMPLEX COM NF- KAPPA B

COM PKAC PRO

CSA17 NF- KAPPA B-I KAPPA B-PKA COMPLEX COM

C1-INS I KAPPA B COMPLEX COM ACT NF- KAPPA

B COM PKAC PRO P65/RELA PRO

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CSA18 IKK COMPLEX COM ACT C1-INS ELKS PRO ACT

HSP 90 PRO ACT MULTI CDC37 PRO ACT IKK

GAMMA BR NEMO PRO ACT UBI MULTI IKK BETA

BR IKK2 PRO ACT PHO PHO MULTI IKK ALPHA BR

IKK1 PRO ACT PHO PHO PHO MULTI

CSA19 IKK COMPLEX COM C1-INS IKK BETA BR IKK2

PRO EMP EMP MULTI IKK ALPHA BR IKK1 PRO

ACT PHO PHO PHO MULTI IKK GAMMA BR NEMO

PRO ACT UBI MULTI ELKS PRO ACT HSP 90 PRO

ACT MULTI CDC37 PRO ACT

CSA2 TRAF1/2 COMPLEX COM C1-INS TRAF 2 PRO TRAF

1 PRO

CSA20 IKK COMPLEX COM C1-INS IKK ALPHA BR IKK1

PRO EMP EMP MULTI IKK BETA BR IKK2 PRO EMP

EMP MULTI IKK GAMMA BR NEMO PRO ACT UBI

MULTI ELKS PRO ACT HSP 90 PRO ACT MULTI

CDC37 PRO ACT

CSA21 NF- KAPPA B COM C1-INS P50 PRO ACT P65/RELA

PRO PHO PHO

CSA22 I KAPPA B COMPLEX COM C1-INS I KAPPA B

ALPHA PRO PHO PHO UBI UBI I KAPPA B BETA

PRO PHO PHO UBI

CSA23 NF- KAPPA B COM C1-INS P65/RELA PRO P50 PRO

CSA24 NF- KAPPA B COM C1-INS P65/RELA PRO PHO P50

PRO ACT

CSA25 (SCF)-TYPE E3 COM ACT C1-INS CUL1 PRO ACT

SKP1 PRO ACT

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CSA26 NF- KAPPA B COM C1-INS P50 PRO ACT P65/RELA

PRO PHO PHO PHO

CSA27 NF- KAPPA B COM C2-INS P50 PRO ACT P65/RELA

PRO PHO PHO PHO PHO UBI CREB/P300 PRO ACT

PHO

CSA28 NF- KAPPA B COM C2-INS P50 PRO ACT P65/RELA

PRO PHO PHO PHO

CSA29 NF- KAPPA B COM ACT C2-INS P50 PRO ACT

P65/RELA PRO ACT PHO PHO PHO PHO UBI

CREB/P300 PRO ACT PHO

CSA3 TRADD COMPLEX COM ACT C1-INS TRAF1/2-CIAP

COMPLEX COM TRADD PRO RIP PRO CIAP1/2 PRO

CSA30 NF- KAPPA B COM C2-INS P50 PRO ACT P65/RELA

PRO PHO PHO PHO UBI CREB/P300 PRO ACT PHO

CSA31 APOPTOSOME COM ACT C1-INS APAF-1 PRO ACT

DATP SIM CYTOCHROME C PRO ACT

CSA32 CTSD COMPLEX COM C3-INS PRO-A-SMASE PRO

PRE-PRO-CTSD PRO

CSA33 CTSD COMPLEX COM C4-INN PRE-PRO-CTSD PRO

PRO-A-SMASE PRO

CSA36 BCL-BAD BR COMPLEX COM C1-INS BCL-XL PRO

BAD PRO ACT

CSA4 TAB COMPLEX COM C1-INS TAB1 PRO PHO PHO

TAB2 PRO ACT PHO PHO PHO TAB3 PRO PHO PHO

PHO

CSA41 TRAF1/2-CIAP COMPLEX COM TRAF 1 PRO TRAF 2

PRO

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CSA42 I KAPPA B COMPLEX COM I KAPPA B ALPHA PRO

PHO PHO UBI UBI I KAPPA B BETA PRO PHO PHO

UBI

CSA43 NF- KAPPA B COM P50 PRO P65/RELA PRO

CSA44 NF- KAPPA B COM P65/RELA PRO P50 PRO

CSA45 I KAPPA B COMPLEX COM I KAPPA B BETA PRO

PHO PHO I KAPPA B ALPHA PRO PHO PHO

CSA46 NF- KAPPA B COM P50 PRO

CSA47 I KAPPA B COMPLEX COM ACT I KAPPA B ALPHA

PRO ACT EMP EMP I KAPPA B BETA PRO ACT

EMP EMP

CSA5 TAB COMPLEX COM ACT C1-INS TAB1 PRO ACT

TAB2 PRO ACT PHO PHO TAB3 PRO ACT EMP EMP

CSA52 TCF-SRECOMPLEX COM C2-INS SRE PRO ACT

ELK-1 PRO ACT PHO PHO PHO PHO PHO

CSA53 SAP1A-SRE COMPLEX COM C2-INS SAP-1A PRO

ACT PHO PHO PHO PHO SRE PRO ACT

CSA54 TCF-SRE-SRF COMPLEX COM ACT C2-INS SRE PRO

ACT ELK-1 PRO ACT PHO PHO PHO PHO PHO SRF

PRO ACT

CSA55 SAP1A-SRE-SRF COMPLEX COM ACT C2-INS SRE

PRO ACT SAP-1A PRO ACT PHO PHO PHO PHO SRF

PRO ACT

CSA56 TRE COMPLEX COM ACT C2-INS TRE PRO ACT

ATF-2 PRO ACT PHO PHO PHO C-JUN PRO ACT

PHO PHO PHO PHO

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CSA57 AP1 COM ACT C2-INS C-FOS PRO ACT PHO PHO

PHO PHO C-JUN PRO ACT PHO PHO PHO PHO

CSA58 P53-MDM-2 COMPLEX COM ACT C2-INS MDM-2

PRO ACT P53 PRO

CSA59 CYCLIN E-CDK2 COMPLEX COM C2-INS CDK2 PRO

ACT CYCLIN E PRO ACT

CSA6 TAB COMPLEX COM C1-INS TAB1 PRO TAB2 PRO

TAB3 PRO

CSA60 CYCLIN D1-CDK4 COMPLEX COM ACT C2-INS

CYCLIN D1 PRO ACT CDK4 PRO ACT

CSA61 RB COMPLEX COM ACT C2-INS RB PRO ACT E2F

PRO DP1 PRO ACT

CSA7 RAF COMPLEX COM C1-INS B-RAF PRO ACT PHO

CRAF/RAF-1 PRO EMP PHO PHO PHO 14-3-3 PRO

ACT

CSA8 RAF COMPLEX COM C1-INS CRAF/RAF-1 PRO EMP

PHO PHO PHO PHO PHO B-RAF PRO ACT PHO

14-3-3 PRO ACT

CSA9 RAF COMPLEX COM C1-INS CRAF/RAF-1 PRO EMP

PHO PHO 14-3-3 PRO ACT B-RAF PRO 14-3-3 PRO

ACT

SA1 TNF-R1 PRO C1-OUT

SA10 SPHINGOMYELIN SIM C1-OUT

SA100 ASK1 PRO ACT PHO PHO C1-INS

SA102 MEKK1 PRO ACT PHO PHO PHO PHO C1-INS

SA104 MKK4/JNKK1/SEK1 PRO PHO PHO C1-INS

SA105 MEK1/2 PRO C1-INS

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SA106 MEK1/2 PRO ACT PHO PHO C1-INS

SA107 IKB DEG C1-INS

SA108 26S PROTEOSOME PRO ACT C1-INS

SA109 CK II PRO ACT C1-INS

SA11 CERAMIDE SIM C1-INS

SA110 TRAF6 PRO C1-INS

SA111 TRAF6 PRO EMP MULTI C1-INS

SA112 TRAF6 PRO ACT UBI MULTI C1-INS

SA113 CYLD PRO ACT C1-INS

SA114 P62 PRO C1-INS

SA115 P62 PRO ACT C1-INS

SA116 PKC ZETA PRO C1-INS

SA117 PKC ZETA PRO ACT PHO C1-INS

SA118 MSK1/2 PRO ACT PHO C1-INS

SA119 MSK1/2 PRO ACT PHO C2-INS

SA120 PKC ZETA PRO ACT PHO C2-INN

SA121 MSK1/2 PRO C1-INS

SA122 P38 PRO EMP C1-INS

SA123 P38 PRO ACT PHO PHO C1-INS

SA124 ERK1/2 PRO C1-INS

SA125 ERK1/2 PRO ACT PHO PHO C1-INS

SA126 ERK1/2 PRO ACT PHO PHO C2-INS

SA127 P38 PRO ACT PHO PHO C2-INS

SA128 JNK 1 PRO EMP C1-INS

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SA129 JNK 1 PRO ACT PHO PHO C1-INS

SA130 JNK 1 PRO ACT PHO PHO C2-INS

SA131 FADD PRO C1-INS

SA132 FADD PRO ACT C1-INS

SA133 PROCASPASE 8 PRO C1-INS

SA134 CASPASE 8 BR (MACH COMMA FLICE COMMA BR

MCH5 ) PRO DON C1-INS

SA135 CASPASE 8 BR (MACH COMMA FLICE COMMA BR

MCH5 ) PRO ACT DON MULTI C1-INS

SA136 BID PRO C1-INS

SA137 TBID PRO ACT DON C1-INS

SA138 JBID PRO ACT C1-INS

SA139 TBID PRO ACT DON C5-INS

SA14 PC-PLC PRO C1-OUT

SA140 BAX/BAK PRO C5-INS

SA141 BAX/BAK PRO ACT MULTI C5-OUT

SA142 JBID PRO ACT C5-INN

SA143 AIF PRO ACT C1-INS

SA144 OMI/HTRA2 PRO ACT C1-INS

SA145 CYTOCHROME C PRO ACT C1-INS

SA146 SMAC/DIABLO PRO ACT C1-INS

SA147 ENDOG PRO ACT C1-INS

SA148 PROCASPASE 9 PRO PHO C1-INS

SA149 APAF-1 PRO ACT C1-INS

SA15 PC-PLC PRO ACT C1-INN

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SA150 DATP SIM C1-INS

SA151 PROCASPASE 9 PRO ACT EMP C1-INS

SA152 CASPASE 9 BR ( ICE-LAP6 COMMA BR MCH6) PRO

DON C1-INS

SA153 CASPASE 9 BR ( ICE-LAP6 COMMA BR MCH6) PRO

DON DON PHO MULTI C1-INS

SA154 CASPASE 9 BR ( ICE-LAP6 COMMA BR MCH6) PRO

ACT DON DON EMP MULTI C1-INS

SA155 PROCASPASE 6 PRO C1-INS

SA156 CASPASE 6 BR (MCH2 ALPHA ) PRO DON DON DON

C1-INS

SA157 CASPASE 3 BR (CPP32 COMMA YAMA COMMA BR

APOPAIN) PRO ACT DON MULTI C1-INS

SA158 CASPASE 3 BR (CPP32 COMMA YAMA COMMA BR

APOPAIN) PRO DON C1-INS

SA159 PROCASPASE 3 PRO C1-INS

SA16 PC SIM C1-OUT

SA160 CASPASE 7 BR (MCH3 COMMA ICE-LAP3 COMMA

BR CMH-1) PRO ACT DON MULTI C1-INS

SA161 CASPASE 7 BR (MCH3 COMMA ICE-LAP3 COMMA

BR CMH-1) PRO DON C1-INS

SA162 PROCASPASE 7 PRO C1-INS

SA163 CASPASE 6(MCH2 ALPHA ) PRO ACT DON DON

DON MULTI C1-INS

SA17 DAG PRO ACT C1-INN

SA172 PKC DELTA PRO C1-INS

SA173 PKC DELTA PRO EMP PHO C1-INS

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SA174 PKC DELTA PRO ACT PHO PHO C1-INS

SA175 A-SMASE PRO DON C4-TRA

SA176 A-SMASE PRO ACT DON PHO C4-INS

SA177 CATHEPSIN D PRO ACT C1-INS

SA178 CATHEPSIN D PRO ACT C4-INN

SA179 CERAMIDE SIM C4-OUT

SA18 RAIDD PRO C1-INS

SA180 SPHINGOMYELIN SIM C4-OUT

SA19 RAIDD PRO ACT C1-INS

SA2 TNF-R1 PRO MULTI C1-OUT

SA20 C1P PRO C1-INS

SA202 FHC PRO ACT C5-INS

SA205 MN-SOD PRO ACT C5-INS

SA208 ROS SIM C5-INS

SA21 C1P PRO ACT C1-INS

SA22 CAPK PRO C1-INS

SA220 BAD14-3-3 PRO C1-INS

SA221 P14-3-3 PRO ACT C1-INS

SA222 BAD PRO PHO PHO PHO C1-INS

SA223 BCL-XL PRO ACT C1-INS

SA23 CAPK PRO ACT C1-INS

SA24 PROCASPASE 2 PRO C1-INS

SA243 CASPASE 6(MCH2 ALPHA ) PRO ACT DON DON

DON MULTI C2-INS

SA244 SATB1 PRO ACT C2-INS

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SA245 LAMIN PRO ACT C2-INS

SA246 S3734 DEG C2-INS

SA247 S3733 DEG C2-INS

SA248 AP2- ALPHA PRO ACT C2-INS

SA249 CAD PRO ACT C2-INS

SA25 CASPASE 2 BR (NEDD2 COMMA ICH-1) PRO DON

C1-INS

SA250 APOPTOSIS PHE C2-INS

SA251 S3732 DEG C2-INS

SA252 S1 DEG C2-INS

SA253 ACINUS PRO ACT C2-INS

SA254 VIMENTIN PRO ACT C2-INS

SA255 S11 DEG C2-INS

SA256 CASPASE 7 BR (MCH3 COMMA ICE-LAP3 COMMA

CMH-1) PRO ACT DON MULTI C2-INS

SA257 S12 DEG C2-INS

SA258 ICAD PRO C2-INS

SA259 DFF PRO ACT C2-INS

SA26 CASPASE 2 BR (NEDD2 COMMA ICH-1) PRO DON

DON C1-INS

SA260 S13 DEG C2-INS

SA261 S15 DEG C2-INS

SA262 S14 DEG C2-INS

SA263 GAS2 PRO ACT C2-INS

SA264 S16 DEG C2-INS

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SA265 S17 DEG C2-INS

SA266 S22 DEG C2-INS

SA267 S18 DEG C2-INS

SA268 T PARP PRO C2-INS

SA269 S19 DEG C2-INS

SA27 CASPASE 2 BR (NEDD2 COMMA ICH-1) PRO ACT

DON DON MULTI C1-INS

SA270 S20 DEG C2-INS

SA271 S21 DEG C2-INS

SA272 FAK PRO ACT C2-INS

SA273 GELSOLIN PRO ACT C2-INS

SA274 D4-GDI PRO ACT C2-INS

SA275 NUMA PRO ACT C2-INS

SA276 DNA BR TOPOISOMERASE I PRO ACT C2-INS

SA279 SREBPS PRO ACT C2-INS

SA28 TRADD PRO C1-INS

SA280 LIM-K1 PRO ACT C2-INS

SA281 ALPHA ACTIN PRO ACT C2-INS

SA282 SLK PRO ACT C2-INS

SA283 PARP PRO ACT C2-INS

SA284 CASPASE 3 BR (CPP32 COMMA YAMA COMMA

APOPAIN) PRO ACT DON MULTI C2-INS

SA287 AP24 PRO ACT C2-INS

SA288 BCL-2 PRO ACT C1-INS

SA289 BCL-2 PRO PHO C1-INS

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SA29 CIAP1/2 PRO ACT C1-INS

SA291 XIAP PRO ACT C1-INS

SA292 C-FLIP SUB L PRO ACT C1-INS

SA293 C-FLIP SUB S PRO C1-INS

SA294 C-FLIP PRO C1-INS

SA3 CRD3 SIM

SA31 RIP PRO ACT UBI C1-INS

SA317 CRD2 SIM

SA318 PLAD SIM

SA319 STNF ALPHA PRO ACT MULTI

SA32 RIP PRO C1-INS

SA320 TNF-R1 PRO ACT MULTI

SA321 CRD3 SIM

SA322 TRAF 2 PRO

SA323 TRAF 1 PRO

SA324 TRADD PRO

SA325 RIP PRO

SA326 CIAP1/2 PRO

SA327 TRAF 1 PRO

SA328 TRAF 2 PRO

SA329 TAB1 PRO PHO PHO

SA33 CEZANNE PRO C1-INN

SA330 TAB2 PRO ACT PHO PHO PHO

SA331 TAB3 PRO PHO PHO PHO

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SA332 TAB1 PRO ACT

SA333 TAB2 PRO ACT PHO PHO

SA334 TAB3 PRO ACT EMP EMP

SA335 TAB1 PRO

SA336 TAB2 PRO

SA337 TAB3 PRO

SA338 B-RAF PRO ACT PHO

SA339 CRAF/RAF-1 PRO EMP PHO PHO PHO

SA34 CEZANNE PRO ACT C1-INS

SA340 14-3-3 PRO ACT

SA341 CRAF/RAF-1 PRO EMP PHO PHO PHO PHO PHO

SA342 B-RAF PRO ACT PHO

SA343 14-3-3 PRO ACT

SA344 CRAF/RAF-1 PRO EMP PHO PHO

SA345 14-3-3 PRO ACT

SA346 B-RAF PRO

SA347 14-3-3 PRO ACT

SA348 14-3-3 PRO ACT

SA349 B-RAF PRO PHO PHO

SA35 TRAF2 PRO EMP EMP C1-INS

SA350 CRAF/RAF-1 PRO ACT EMP PHO PHO PHO PHO

PHO

SA351 CRAF/RAF-1 PRO ACT EMP PHO PHO PHO PHO

PHO PHO

SA352 B-RAF PRO ACT PHO

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SA353 14-3-3 PRO ACT

SA354 14-3-3 PRO ACT

SA355 B-RAF PRO PHO PHO

SA356 CRAF/RAF-1 PRO PHO PHO PHO PHO PHO PHO

SA359 IKK ALPHA BR IKK1 PRO EMP EMP MULTI

SA36 TRAF2 PRO PHO EMP C1-INS

SA360 IKK BETA BR IKK2 PRO EMP EMP MULTI

SA361 IKK GAMMA BR NEMO PRO EMP MULTI

SA362 ELKS PRO ACT

SA363 HSP 90 PRO ACT MULTI

SA364 CDC37 PRO ACT

SA365 PKAC PRO

SA366 I KAPPA B ALPHA PRO PHO PHO UBI UBI

SA367 I KAPPA B BETA PRO PHO PHO UBI

SA368 P50 PRO

SA369 P65/RELA PRO

SA37 TRAF2 PRO PHO UBI MULTI C1-INS

SA370 PKAC PRO

SA371 P65/RELA PRO

SA372 P50 PRO

SA373 I KAPPA B BETA PRO PHO PHO

SA374 I KAPPA B ALPHA PRO PHO PHO

SA375 PKAC PRO

SA376 P65/RELA PRO

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SA377 P50 PRO

SA378 I KAPPA B ALPHA PRO ACT EMP EMP

SA379 I KAPPA B BETA PRO ACT EMP EMP

SA38 TRAF2 PRO ACT PHO UBI PHO MULTI C1-INS

SA380 ELKS PRO ACT

SA381 HSP 90 PRO ACT MULTI

SA382 CDC37 PRO ACT

SA383 IKK GAMMA BR NEMO PRO ACT UBI MULTI

SA384 IKK BETA BR IKK2 PRO ACT PHO PHO MULTI

SA385 IKK ALPHA BR IKK1 PRO ACT PHO PHO PHO

MULTI

SA386 IKK BETA BR IKK2 PRO EMP EMP MULTI

SA387 IKK ALPHA BR IKK1 PRO ACT PHO PHO PHO

MULTI

SA388 IKK GAMMA BR NEMO PRO ACT UBI MULTI

SA389 ELKS PRO ACT

SA39 RIP PRO EMP UBI C1-INS

SA390 HSP 90 PRO ACT MULTI

SA391 CDC37 PRO ACT

SA392 IKK ALPHA BR IKK1 PRO EMP EMP MULTI

SA393 IKK BETA BR IKK2 PRO EMP EMP MULTI

SA394 IKK GAMMA BR NEMO PRO ACT UBI MULTI

SA395 ELKS PRO ACT

SA396 HSP 90 PRO ACT MULTI

SA397 CDC37 PRO ACT

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SA398 P50 PRO ACT

SA399 P65/RELA PRO PHO PHO

SA4 CRD2 SIM

SA40 26S PROTEOSOME PRO ACT C1-INS

SA400 I KAPPA B ALPHA PRO PHO PHO UBI UBI

SA401 I KAPPA B BETA PRO PHO PHO UBI

SA402 P65/RELA PRO

SA403 P50 PRO

SA404 P65/RELA PRO PHO

SA405 P50 PRO ACT

SA406 CUL1 PRO ACT

SA407 SKP1 PRO ACT

SA408 P50 PRO ACT

SA409 P65/RELA PRO PHO PHO PHO

SA41 S3815 DEG C1-INS

SA410 P50 PRO ACT

SA411 P65/RELA PRO PHO PHO PHO PHO UBI

SA413 P50 PRO ACT

SA414 P65/RELA PRO PHO PHO PHO

SA415 P50 PRO ACT

SA416 P65/RELA PRO ACT PHO PHO PHO PHO UBI

SA418 P50 PRO ACT

SA419 P65/RELA PRO PHO PHO PHO UBI

SA42 A20 PRO ACT PHO C1-INS

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SA421 APAF-1 PRO ACT

SA422 DATP SIM

SA423 CYTOCHROME C PRO ACT

SA424 PRO-A-SMASE PRO

SA425 PRE-PRO-CTSD PRO

SA426 PRE-PRO-CTSD PRO

SA427 PRO-A-SMASE PRO

SA43 A20 PRO C1-INS

SA432 BCL-XL PRO

SA433 BAD PRO ACT

SA44 CARP2 PRO C1-INS

SA443 CREB/P300 PRO EMP C2-INS

SA444 CREB/P300 PRO ACT PHO C2-INS

SA445 PKAC PRO ACT C1-INS

SA446 PKAC PRO ACT C2-INS

SA447 ATF-2 PRO C2-INS

SA448 ATF-2 PRO PHO PHO C2-INS

SA449 ATF-2 PRO ACT PHO PHO PHO C2-INS

SA45 CARP2 PRO ACT C1-INS

SA450 ELK-1 PRO C2-INS

SA451 ELK-1 PRO PHO C2-INS

SA452 ELK-1 PRO PHO PHO C2-INS

SA453 ELK-1 PRO ACT PHO PHO PHO PHO PHO C2-INS

SA454 SAP-1A PRO C2-INS

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SA455 SAP-1A PRO ACT PHO PHO PHO PHO C2-INS

SA46 GRB-2 PRO C1-INS

SA47 GRB-2 PRO ACT C1-INS

SA470 SRE PRO ACT C2-INS

SA471 SRF PRO ACT C2-INS

SA472 C-FOS GEN C2-INS

SA473 C-FOS RNA C2-INS

SA474 SRE PRO ACT

SA475 ELK-1 PRO ACT PHO PHO PHO PHO PHO

SA476 SAP-1A PRO ACT PHO PHO PHO PHO

SA477 SRE PRO ACT

SA478 SRE PRO ACT

SA479 ELK-1 PRO ACT PHO PHO PHO PHO PHO

SA48 SOS PRO C1-INS

SA480 SRF PRO ACT

SA481 SRE PRO ACT

SA482 SAP-1A PRO ACT PHO PHO PHO PHO

SA483 SRF PRO ACT

SA484 C-FOS PRO EMP EMP C1-INS

SA485 C-FOS PRO EMP EMP C2-INS

SA486 C-FOS PRO ACT PHO PHO PHO PHO C2-INS

SA489 C-JUN PRO PHO PHO PHO PHO PHO PHO PHO PHO

C2-INS

SA49 SOS PRO ACT C1-INS

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SA490 C-JUN PRO PHO PHO PHO EMP PHO PHO PHO PHO

C2-INS

SA491 C-JUN PRO PHO PHO PHO EMP PHO EMP PHO PHO

C2-INS

SA492 C-JUN PRO ACT PHO PHO PHO PHO C2-INS

SA493 C-JUN PRO EMP EMP EMP EMP EMP EMP EMP

EMP C2-INS

SA494 TRE PRO ACT C2-INS

SA495 C-JUN RNA C2-INS

SA496 C-JUN GEN C2-INS

SA497 TRE PRO ACT

SA498 ATF-2 PRO ACT PHO PHO PHO

SA499 C-JUN PRO ACT PHO PHO PHO PHO

SA5 STNF ALPHA PRO ACT MULTI

SA50 MADD PRO C1-INS

SA500 CK II PRO ACT C2-INS

SA501 GSK3 BETA PRO ACT EMP C1-INS

SA502 GSK3 BETA PRO PHO C1-INS

SA503 GSK3 BETA PRO ACT EMP C2-INS

SA506 XIAP GEN C2-INS

SA507 A20 GEN C2-INS

SA508 BCL2 GEN C2-INS

SA509 GADD45 BETA RNA C2-INS

SA51 MADD PRO ACT C1-INS

SA510 GADD45 BETA GEN C2-INS

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SA511 BCL2 RNA C2-INS

SA512 XIAP RNA C2-INS

SA513 A20 RNA C2-INS

SA514 CIAP1/2 GEN C2-INS

SA515 CIAP1/2 RNA C2-INS

SA516 I KAPPA B GEN C2-INS

SA517 I KAPPA B RNA C2-INS

SA518 CYLD RNA C2-INS

SA519 CYLD GEN C2-INS

SA520 TRAF2 GEN C2-INS

SA521 TRAF2 RNA C2-INS

SA522 TRAF1 GEN C2-INS

SA523 TRAF1 RNA C2-INS

SA524 CFLIP GEN C2-INS

SA525 CFLIP RNA C2-INS

SA526 MN-SOD GEN C2-INS

SA527 MN-SOD RNA C2-INS

SA528 FHC GEN C2-INS

SA529 FHC RNA C2-INS

SA53 RAS PRO C1-INS

SA530 GADD45 BETA PRO ACT C1-INS

SA531 PTEN GEN C2-INS

SA532 PTEN RNA C2-INS

SA533 PTEN PRO ACT C1-INS

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SA538 ITCH PRO EMP C1-INS

SA539 ITCH PRO ACT PHO PHO PHO C1-INS

SA540 C-JUN PRO EMP EMP EMP EMP EMP EMP EMP

EMP C2-INS

SA541 C-JUN PRO ACT PHO PHO PHO PHO C2-INS

SA542 C-JUN PRO EMP EMP EMP EMP EMP EMP EMP

EMP C1-INS

SA543 C-FOS PRO ACT PHO PHO PHO PHO

SA544 C-JUN PRO ACT PHO PHO PHO PHO

SA545 P53 GEN C2-INS

SA546 EGR-1 RNA C2-INS

SA547 P53 RNA C2-INS

SA548 P21 SUPER WAF1/CIP1 GEN C2-INS

SA549 P21 SUPER WAF1/CIP1 RNA C2-INS

SA55 PI3K (P85) PRO EMP C1-INS

SA550 MDM-2 PRO C1-INS

SA551 EGR-1 GEN C2-INS

SA552 CYCLIN D1 GEN C2-INS

SA553 CYCLIN D1 RNA C2-INS

SA554 MDM-2 GEN C2-INS

SA555 MDM-2 RNA C2-INS

SA558 P53 PRO C1-INS

SA559 P21 SUPER WAF1/CIP1 PRO C1-INS

SA56 RAS PRO ACT C1-INS

SA560 CYCLIN D1 PRO ACT C2-INS

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SA561 CYCLIN D1 PRO PHO C2-INS

SA562 CYCLIN D1 PRO C1-INS

SA563 EGR-1 PRO C1-INS

SA565 P53 PRO ACT PHO C2-INS

SA566 P21 SUPER WAF1/CIP1 PRO C2-INS

SA567 P21 SUPER WAF1/CIP1 PRO ACT C2-INS

SA568 CYCLIN E PRO ACT C2-INS

SA569 CDK2 PRO ACT C2-INS

SA57 PI3K (P85) PRO ACT PHO C1-INS

SA570 G1-S PHASE TRANSITION PHE C2-INS

SA571 CELL CYCLE PHE C2-INS

SA572 CELL PROLIFERATION PHE C2-INS

SA573 MDM-2 PRO ACT

SA574 P53 PRO

SA575 CDK2 PRO ACT

SA576 CYCLIN E PRO ACT

SA577 MDM-2 PRO ACT C2-INS

SA578 P53 PRO ACT C2-INS

SA58 AKT/PKB PRO ACT PHO PHO C1-INS

SA580 CDK4 PRO ACT C2-INS

SA581 RB PRO PHO C2-INS

SA582 E2F PRO ACT C2-INS

SA583 CYCLIN E GEN C2-INS

SA584 CYCLIN E RNA C2-INS

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SA585 CYCLIN E PRO C1-INS

SA587 CYCLIN D1 PRO ACT

SA588 CDK4 PRO ACT

SA589 RB PRO ACT

SA59 AKT/PKB PRO EMP C1-INS

SA590 E2F PRO

SA591 DP1 PRO ACT

SA594 C-MYC PRO ACT PHO PHO C2-INS

SA595 C-MYC PRO EMP PHO C2-INS

SA596 C-MYC PRO EMP EMP C2-INS

SA597 IL-6 GEN C2-INS

SA598 IL-8 GEN C2-INS

SA599 CCL-2 GEN C2-INS

SA6 NSMASE PRO C1-OUT

SA60 CAPP PRO C1-INS

SA600 CCL-2 RNA C2-INS

SA601 IL-8 RNA C2-INS

SA602 IL-6 RNA C2-INS

SA603 CCL-2 PRO C1-INS

SA604 IL-6 PRO ACT C1-INS

SA605 IL-8 PRO ACT C1-INS

SA606 EGR-1 PRO ACT C2-INS

SA607 CCL-2 PRO ACT C2-INS

SA608 IL-6 PRO ACT C2-INS

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SA609 IL-8 PRO ACT C2-INS

SA61 CAPP PRO ACT C1-INS

SA611 MKPS GEN C2-INS

SA612 MKPS RNA C2-INS

SA614 JNK 1 PRO EMP C2-INS

SA615 MKPS PRO ACT C2-INS

SA616 MKPS PRO ACT C1-INS

SA62 VAV PRO C1-INS

SA63 VAV PRO ACT PHO C1-INS

SA64 PAK 3 PRO C1-INS

SA65 PAK 3 PRO ACT PHO C1-INS

SA660 SPHK1 PRO C1-INS

SA661 SPHK1 PRO ACT C1-INS

SA662 SPHK1 PRO ACT PHO C1-INS

SA663 SPHK1 PRO ACT PHO C1-INS

SA665 SPHINGOSINE PRO C1-INS

SA667 SPHINGOSINE PRO ACT PHO C1-INS

SA672 S4560 DEG C1-INS

SA673 S4561 DEG C1-INS

SA676 PTEN PRO ACT C2-INS

SA677 BCL-2 PRO ACT C2-INS

SA68 RAC/CDC42 PRO ACT C1-INS

SA688 BCL-2 PRO ACT C5-INS

SA69 RAC/CDC42 PRO C1-INS

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SA7 NSMASE PRO ACT C1-INS

SA70 GCK PRO C1-INS

SA700 BCL-XL PRO ACT C5-INS

SA701 RIPK3 PRO ACT C1-INS

SA703 CREB/P300 PRO ACT PHO

SA704 CREB/P300 PRO ACT PHO

SA705 CREB/P300 PRO ACT PHO

SA71 GCK PRO ACT C1-INS

SA72 MEKK1 PRO EMP EMP C1-INS

SA73 MEKK1 PRO PHO PHO C1-INS

SA74 TAK1 PRO C1-INS

SA75 MEKK3 PRO C1-INS

SA76 MEKK3 PRO ACT PHO C1-INS

SA77 TAK1 PRO ACT PHO PHO PHO C1-INS

SA78 S4075 DEG C1-INS

SA8 FAN PRO ACT C1-INN

SA80 NIK PRO EMP C1-INS

SA81 NIK PRO EMP PHO PHO PHO C1-INS

SA84 NIK PRO ACT PHO C1-INS

SA85 MKK6 (MAPKK6 COMMA BR MEK6 COMMA

SAPKK3 ) PRO C1-INS

SA86 MKK7 BR (JNKK2) PRO C1-INS

SA87 MKK3 (MEK3 COMMA BR MAPKK3) PRO C1-INS

SA88 MKK4/JNKK1/SEK1 PRO EMP C1-INS

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SA89 MKK6 (MAPKK6 COMMA BR MEK6 COMMA

SAPKK3 ) PRO ACT PHO PHO C1-INS

SA9 FAN PRO C1-OUT

SA90 MKK7 BR (JNKK2) PRO ACT PHO PHO C1-INS

SA91 MKK3 (MEK3 COMMA BR MAPKK3) PRO ACT PHO

PHO C1-INS

SA92 MKK4/JNKK1/SEK1 PRO ACT PHO C1-INS

SA93 CRAF/RAF-1 PRO C1-INS

SA94 B-RAF PRO C1-INS

SA96 PKC ALPHA PRO C1-INS

SA97 PKC ALPHA PRO ACT PHO PHO PHO PHO C1-INS

SA98 TRX PRO ACT C1-INS

SA99 ASK1 PRO C1-INS

79