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Ligand Screens: RAW and B cells Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004. Signaling Network. 1. 1. Inputs. Outputs. n. m. The first Question of the AfCS:. How complex is signal processing in cells?. Signaling Network. 1. 1. Ligands. Outputs. n. m.

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Ligand Screens: RAW and B cells

Madhu Natarajan, Rama Ranganathan

AFCS Annual Meeting 2004


Signaling Network

1

1

Inputs

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

The purpose of the ligand screen:

(1) classify output responses

(2) determine degree of functional cross-talk between pathways


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

(a) A quantitative measure of similarity or dissimilarity of ligands (this talk)


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands

  • Quantitative evaluation of the interactions between pairs of ligand responses, and an estimation of total interaction density. (Rama, Elliott…)


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands.Remember: We are mapping input-output relationships, i.e., we are relating a measured set of outputs to an input.


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands.Remember: We are mapping input-output relationships, i.e., we are relating a measured set of outputs to an input.

  • This may or may not provide much information about specific mechanism.

  • The goal of the ligand screen is to profile ligands and identify interactions, which leads to bigger and better things.



Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands. Questions:

  • 1. How do we combine all the multivariate output data into general parameters that represent signaling?


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands. The issues:

  • 1. A way of combining all the multivariate output data into general parameters that represent signaling.

  • 2. Eliminating data redundancy: Calcium (100s of points), microarrays (thousands). Clearly not all are needed.


Signaling Network

1

1

Ligands

Outputs

n

m

The first Question of the AfCS:

How complex is signal processing in cells?

  • A quantitative measure of similarity or dissimilarity of ligands. The issues:

  • 1. A way of combining all the multivariate output data into general parameters that represent signaling.

  • 2. Eliminating data redundancy

  • 3. A formalism for calculating similarity of responses.


Merging different types of data:

A quantitative measure of similarity

  • Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

s

Observed value

basal


Merging different types of data:

A quantitative measure of similarity

  • Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

s

Observed value

basal

So, we define a parameter S (for significance or signaling):


Merging different types of data:

A quantitative measure of similarity

  • Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

0.5

10

2

Example: A basal value of 2 and a standard deviation of 0.5, gives us an S-score of 16


s

S

basal

Merging different types of data:

A quantitative measure of similarity

  • Our approach is to make an Gaussian error model for the unstimulated value of each variable. Then convert each variable for a ligand into the statistical significance of observing the value given the unstimulated value and error model.

Every data element we collect (regardless of type, time scale, method of collection) can now be put on a common basis for comparison, clustering, etc.

The only assumption is that the basal value is normally distributed around its mean.


Building a unified experiment space:

The structure of the matrix

Ligand profile

Calcium

cAMP

phosphoproteins

microarrays

S-scores

S-scores

S-scores

S-scores

Ligand 1

Ligand 2

.

.

.

Ligand 32

Time

Time

Time

Time


Building a unified experiment space:

Understanding each measured parameter: cAMP

Calcium

cAMP

phosphoproteins

microarrays

Ligand 1

Ligand 2

.

.

.

0.5

1

3

8

20

S-scores

5 dimensions

Ligand 32

Time

Time

Time

Time


Building a unified experiment space:

Understanding each measured parameter: phosphoproteins

Calcium

cAMP

phosphoproteins

microarrays

Ligand 1

Ligand 2

2.5

5

15

30

.

.

.

ST6

P90

AKT

ER1

ER2

PKM

ST3

P65

JNK1

JNKs

P38

S-scores

5 dimensions

44 dimensions

Ligand 32

Time

Time

Time

Time


Building a unified experiment space:

Understanding each measured parameter: calcium, microarrays

Calcium

cAMP

phosphoproteins

microarrays

Ligand 1

Ligand 2

30m

1

2

4

.

.

.

15000+ probes

@ each time

(80 dimensions)

200 timepoints

(5 dimensions)

5 dimensions

44 dimensions

Ligand 32

Time

Time

Time

Time


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Results: The merged unified experiment space

cAMP

Calcium

phosphoproteins

microarrays


The experiment space

cAMP

Calcium

phosphoproteins

microarrays

2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

cAMP: Each time-point in the experiment space is a separate dimension

Time: Left to Right:

0.5, 1, 3, 8, 20 min.


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

cAMP: S-space notation preserves information

Measured

Data

fold

minutes

S-space


2MA

40L

2.5

AIG

5

BAF

BLC

15

BOM

30

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Phosphoproteins: Each time-point in the experiment space is a separate dimension

For each timepoint:

11 phosphoproteins.


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Phosphoproteins: Examples

S-space

fold

Measured

minutes


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Phosphoproteins: Examples

S-space

fold

Measured

minutes


2MA

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Phosphoproteins: Examples

Phosphoproteins: Examples

S-space

fold

Measured

minutes

Measured

fold

minutes

minutes


Calcium dimensions in the experiment space

Calcium

Two issues:

Experiment to experiment variability.

Dealing with parameterization. Clearly we don’t need all 200+ timepoints

LIGANDS



1. Calcium: Amplitude normalization

AIG

Amplitude relative to peak

seconds


1. Calcium: Time Normalization

An example:

H. Flyvbjerg, E. Jobs, S. Leibler, P.N.A.S 1996, Kinetics of self-assembling microtubules: An “inverse problem” in biochemistry

“Phenomenological scaling” : When feasible? If overall behaviour common to the time series is dominated by a (single) set of mechanisms that can be scaled linearly...

Madhu Natarajan: May 04, 2004



Time and Amplitude normalization for calcium responses

Calcium (nM)

seconds

Scaled Calcium

Time relative to peak

T

10T


T

10T

20T

30T

Time and Amplitude normalization for calcium responses

Calcium (nM)

seconds

Scaled Calcium

Time relative to peak

A similar mechanistic process accounts for the calcium response to LPA

despite the difference in size and timing of responses


T

10T

20T

30T

Scaling on the time-axis does not reduce discrimination between ligands

Scaled Calcium

Time relative to LPA peak

The results are not artefactual despite the similarity of calcium profiles


1. Calcium day-to-day response differences are not “biological”

Experiments

on 7 different

days


2. Calcium: Dealing with parameterization “biological”

Amplitude

Time


2. Calcium: Dealing with parameterization “biological”

A1

Amplitude

Conventional approach

.

.

.

An

.

.

.

T1

Tm

Time (kinetics)


2. Calcium Data Reduction: A cluster-based approach “biological”

Let natural distinctions in the data describe parameters

0

600

Time (sec)


2. Calcium Data Reduction “biological”

Data separation mirrors what we have intuitively been using all along


2MA “biological”

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Results: The merged unified experiment space

cAMP

Calcium

phosphoproteins

microarrays


Microarrays “biological”

microarrays

15000+ probes

Need to define distinctions in a large dataset .. in response to a reasonably diverse set of perturbations

Stuart et al. “A gene-coexpression network for global discovery of conserved genetic modules”, Science, October 2003.


2. Microarrays “biological”

Evolutionary Conservation

Meta-genes: Evolutionary conservation as a criterion to identify genes that are functionally important from a set of co-regulated genes.

BLAST

Gene X

Gene Y

Gene A

Gene B

Stuart et al., Science 2003.


2. Microarrays “biological”

2. Microarrays

Gene List:

Gene 1

Gene 2…

Gene N

“Meta-genes”

Identify meta-genes that show correlation in multiple experimental conditions from several gene expression databanks.

Finally create a co-expression network.

correlation=high

Stuart et al., Science 2003.


2. Microarrays “biological”

3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation.

Stuart et al., Science 2003.


2. Microarrays “biological”

3-D mapping of meta-genes: Distance between genes is an index of probability of co-regulation.

AfCS data-set:

1. Identify meta-genes within the Bcell data.

2. Identify significantly changing genes.

3. Gene count

Stuart et al., Science 2003.


Microarrays “biological”

Signaling

Energy Generation

Translation Initiation & Elongation

Proteasome

Cell Cycle

General Transcription

Translation Initiation & Elongation

Ribosomal Subunits

Secretion

Lipid metabolism

30 m

1 hr

2 h

4 h


2MA “biological”

40L

30 min

AIG

1 hour

BAF

BLC

2 hours

BOM

4 hours

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Microarrays

30 min

1 hour

2 hours

4 hours

For each timepoint:

10 functional groups each

with 2 gene counts (up, down)


2MA “biological”

40L

AIG

BAF

BLC

BOM

CGS

CPG

DIM

ELC

FML

GRH

IL4

I10

IFB

IFG

IGF

LB4

LPA

LPS

M3A

NEB

NGF

NPY

PAF

PGE

S1P

SDF

SLC

TER

TGF

TNF

Results: The merged unified experiment space

cAMP

Calcium

phosphoproteins

microarrays



Summary: between ligands

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.


Summary: between ligands

  • 1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

  • 2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.


Summary: between ligands

1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.


Summary: between ligands

  • 1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

  • 2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

  • 3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.

  • 4. The method of analysis is independent of cell type or assays within the ligand screen. It is equally applicable to the B cell or the RAW cell data.


Summary: between ligands

  • 1. A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise.

  • 2. A potentially serious danger is over-parameterization, the usage of many non-independent variables to represent a biological process (say, the inactivation of a calcium response). We have shown two ways to address this problem on the calcium and microarray dimensions.

  • 3. We have assembled an experiment space of 32 ligands by 134 parameters that represents the single ligand profiles for each ligand. This space can be clustered to identify similarities/dissimilarities between ligands.

  • 4. The method of analysis is independent of cell type or assays within the ligand screen. It is equally applicable to the B cell or the RAW cell data.

  • Looking ahead: Applicability to the double ligand screen and beyond: Rama, Elliott.


Acknowledgements between ligands

Rama Ranganathan

Paul Sternweis

Elliott Ross

Ron Taussig

Mel Simon

Al Gilman


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