Systems Biology 124 / 9 2007

Bodil Nordlander – trained as a molecular biologist

Outline

1. What is Systems Biology?2. Why a need for Systems Biology (motivation)?3. How is Systems Biology conducted?4. Drivers for Technology5. Networks versus pathways6. Examples of systems; signal transduction pathways

metabolic pathways etc

What is Systems biology?

How is systems biology different from ”classical” reductionist biology?

-In classical biology the study of isolated parts, such as the function ofa protein, transcriptional control of a gene etc, of systems have beenperformed and from these functions hypothesis of different models/hypothesis have been set-up. It is these hypothesis that are the foundation for our under-standing of different processes in the cell.

-In systems biology, from complex biological phenomenon we try to identify ahypothesis (this is due to a combined effort between experimentalists and modelers)which can be used to perform studies of the biological process. The model is used to test different hypothesis.

What is Systems biology?

Central Dogma

• The central dogma of information flow in biology: Information flows from DNA toRNA to protein. With other words: the amino acid sequence making up a protein, itsstructure and function, is determined by the DNA transcription.

• “This states that once ‘information’ has passed into protein it cannot get outagain. In more detail, the transfer of information from nucleic acid to nucleic acid,or from nucleic acid to protein may be possible, but transfer from protein toprotein, or from protein to nucleic acid is impossible. Information means here theprecise determination of sequence, either of bases in the nucleic acid or of aminoacid residues in the protein.”Francis Crick, On Protein Synthesis, in Symp. Soc. Exp. Biol. XII, 138-167 (1958)

DNA RNA PROTEINTRANSCRIPTION TRANSLATION

REPLICATION

www.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction

What is Systems Biology?

The information about how a system works does not lie in the genome but rather in how proteins work together in the context of the organ / tissue / cell etc.

http://www.zum.de/Faecher/Materialien/beck/bilder/transsri5.jpg http://www.biochem.northwestern.edu/mayo/Lab%20GIF%20Images/Signaling.gifocw.mit.edu/.../0/chp_subtilisinbp.jpg

What is Systems Biology?

1. Understanding how biomolecules (proteins, metabolites, RNA....) function together (i.e. in a system), rather than in isolation. System-level understanding!

2. Airplane analogy (Hiroaki Kitano)

3. To get a system-level understanding you need to know: the system structure (protein-protein interactions, biochemical pathways etc), System dynamics (how does a system behave over time?) Few systems with this understanding!

4. What is a model? An abstract representation of the process which also can explainproperties / features of the process. (E.klipp, Systems biology in practice)

Mathematical modelers Experimentalists

Systems BiologistsCommon goal: Is to understand complex systems by combining mathematical modelingand experimental studies. Systems biology offer the chance to predict the outcome of complexprocesses. How do cells work ? How are cellular processes regulated? How do cells react toenvironmental pertubations? Etc etc etc etc etc

http://pubs.acs.org/cen/coverstory/8120/8120biology.html

What is a Systems Biologist?

What is Systems Biology?

Quantitative versus Qualitative??

Parameters: Quantities which have a value e.g. Km of an enzyme. These values arenormally set in a model whereas variables change.

Qualitative analysis: It tries to answer the questions why and how, it catagorises datainto patterns. In biology, qualitative research has provided a huge amount of informationwhich is the basis for today´s and future research. It has been the basis for the reductionist era of molecular biology.

Quantitative analysis: It tries to answer the questions what, where and when, relies onthe analysis on numerical data which can be quantified, time-series data. In SystemsBiology, the temporal and spatial dynamics of each molecular spicies are of interest!

(ref: http://en.wikipedia.org)

What is a biological system?:

1. Consists of components that interact such in order to form a functional unit.

2. Defined at different hierarchical levels with different extent of detail (enzyme, glycolysis, cellular, tissue, organ, whole organism, ecosystems).

System biology, Definitions and perspectives, Topics in current Genetics 2005

What is Systems Biology?

www4.liber.se/kemionline/gymkeb/bilder/12_a.jpg http://biologi.uio.no/plfys/haa/gif/form142.gif http://www.acuhealthzone.com/images/anatomy_of_human_body.gif

Why a need for Systems Biology (motivation)?

Nucleotide sequence Nucleotide structure

Gene expression

Protein sequence Protein function

Protein-protein interactions (pathways)

Cell

Cell to cell signalling

Tissues

Organs

Physiology Organism

Why a need for Systems Biology (motivation)?

1. Testing if the biological hypothesis is accurate – is it likely that the experimental

data explains the model?

2. Testing quantitative predictions of behaviors. This allows us to minimize the number of experiments and do the critical ones which can give us most information.

3. A model provides the opportunity to address critical scientific questions.

4. Cellular regulation depends on time and space, which a model can address.

A

B C

D E

F

G

H

I

Our model

Input to system

Output Function

Sln1AspP

ATPADP

Ypd1

Ssk1AspP

Pi

high osmolarity

?

Ypd1HisP

Ssk1

Sln1HisP Sln1v1

TCSv2TCS

v3TCS

v4TCS

v5TCS

Sln1

Ssk1

Hog1Glucose

DHAP

G3P

GlycerolTranslation

Gpd1, Gpp2,….

Gpd1

Gpp2

Signalpathway

Metabolism

Fps1

Osmotic stress

Osmoticstress

Glycerolextern

Plasma membrane

cytosol nucleus

e

i

MAP kinase cascade

Phospho relaysystem

Hog1

TranscriptionGPD1, GPP2,….

Gene expressionSsk2 Ssk2P

Pi

Pbs2 Pbs2P Pbs2P2

Pi

ATP ADP ATP ADP

Pi

Hog1

Ssk1

v1MAP

v2MAP

v-1MAP

v3MAP

v-2MAP v-3

MAP

ATP ADPATP ADP

Hog1P Hog1P2

Pi

ATP ADP ATP ADP

Pi

v4MAP v5

MAP

v-4MAP v-5

MAP

Hog1P2

Hog1P2nuc

mRNAnuc mRNAcyt

Proteinsnucleus

cytosol

vts

vex vrd

vpd

Hog1nuc

Hog1

vtrans

vtrans1

vtrans2

Glucose

Gluc-6-P

Fruc-1,6-BP

GAP DHAP

Pyruvate

Ethanol

synthesis

synthesis

3 CO2

G3P

Glycerol

NADH NAD

ADP ATP

4 NAD

4 NADH

NAD

NADH

NADH

NAD

2 ADP2 ATP NADH NAD

ATP

ADP

ATP

ADP

ATP ADP

ADP ATP

Glk1

Gpp2Gpd1

Fps1

Glucose uptake

Glycerol, ex

Phosphorelay module

MAP kinasecascade module

Gene expression module

Biophysical changes

i = f (Glycerol)Waterflow over membrane = f (i, e, t)

Volume change = f (Waterflow)(see text)

I nternal osmotic pressure

External osmotic pressure

Metabolismmodule

Figure 1

vdephos

Ptp2

vtl

v15

v14

v16

v13

v12v11

v3

v10

v1

v2

v4

v5

v6

v9

v7 v8

Example of a model which links together different biological processes taking into account time and space, e.g. the compartments cytosol and nucleus are included.

Why a need for Systems Biology (motivation)?

5. If you have a model you can analyse which parts of the system which contribute most to the desired properties of the model.

6. Signaling networks can interact in multivarious ways which complexity requires a model.

7. Investigate the principles underlying biological robustness. It is an essential property of biological systems (Kitano H, Science v.292, 2002). ”The persistent of a system´s characteristic behaviour under perturbation or conditions of uncertainty” (System modeling in cellular biology, zoltan Szallasi et al, 2006).

What design elements are thought to be required to avoid harmful disturbances: 1) redundancy (back- up systems) 2) Feedback control 3) Structure complex systems into modules which have semi-autonomous functions etc etc.

A robust system is for instance believed to adapt to environmental stresses, it has slow degradation of a system´s function after damage and parameter insensitivity to specific kinetic parameters. To take into account principles of robustness might provide some guidelines for how we model and analyse model complexity.

Why a need for Systems Biology (motivation)?

THE EQUILIBRIA OF LIFE

NUTRIENTS

TEMPERATURE

CHEMICALS

WATER AVAILABILITY

COMPETITION WASTE

RADIATION SURVIVAL

OPTIMISATION OF GROWTH

From Marcus Krantz

Why a need for Systems Biology (motivation)?

8. To understand general ”design principles” shaped by evolution; some peoplebelieve that there exist functional modules as a critical level of biological organisation (ref. Hartwell L.H. Nature 1999, vol 402, 2 Dec). A module ” a discrete entity whosefunction is separable from those of other modules”, e.g. a ribosome whichsynthesizes proteins is spatially isolating its function, signalling pathways etc.

What are ”design principles” : e.g. positive or negative feedback-loops, amplifiers, parallel circuits (common terms to engineers)? Are they found in nature?

Negative feedback: reduces outputPositive feedback: increases output, or Bipolar feedback: Either increase or decrease output.

Hypothetical module

A signalling pathway provides the means for the cell to sense aspects of its surroundings and/or condition. It usually consists of:

A sensor or receptor able to respond to the environment.

One or more cytoplasmic signal transducers, perhaps acting on cytoplasmic targets.

A shuttling component able to carry the signal into the nucleus, activating

one or more transcription factors.

Mechanism of feedback control.

Kinases and phosphates are common, using (de)phosphorylation as the signal.

PLASMA MEMBRANE

CY

TO

SO

LN

UC

LEU

S

GENE EXPRESSION

NUCLEAR MEMBRANE

From Marcus Krantz

How is Systems Biology conducted?

Integrated study of:

1. Experimental data2. Data processing3. Modeling

It is also a coordinated study of:

1. Investigating cellular components and their interactions.2. Experimentation3. Computational methods.

A

B C

D E

F

G

H

I

Our model

Input to system

E.Klipp, Systems Biology in PracticeOutput or funcion

How is Systems Biology conducted? How did we do?

A signalling pathwayIn yeast – HOG pathway

1. The biological knowledge was gathered from literature and own observations.2. The structure of the pathway was decided and converted into equations (static).3. Static model dynamic model. The model structure was analysed and

parameters optimised. Quantitative experimental data was used to compare with simulations.

4. The model was tested by simulations and new experiments –validation! etc etc.

4. Drivers for Technology

Experimental techniques steadily improves in the direction of Systems Biology

- Large Scale studies (-Omics) which produces an enormous amount of data at different levels of cellular organization. This data can be integrated into mathematicalmodels and to fill gaps of unknown players. These methods constantely improves and new arise.

- Improved conventional methods; better quantification methods, single-cellanalysis methods (e.g. microscopy with microfluidic systems), quantitative measurements of gene expression, protein levels etc.

-Increased awareness of studying the favourite system quantitatively instead of qualitatively leading to improved techniques and an increased usage of certain methods. This awareness might lead to better planned experiments if using a mathematical model. Experimental planning!

-To include engineers in biology will lead to improved or new highly sophisticatedtechniques. And more statistical analysis!!!

Omics -

Focuses on large scale and holistic data/information to understand life in encapsulated omes

- Genomics (the study of genes, regulatory and non-coding sequences )

- Transcriptomics (RNA and gene expression)

- Proteomics (Systematic study of protein expression)

- Interactomics (studying the interactome, which is the interaction among proteins)

-Metabolomics (the study of small-molecule metabolite profiles in cells)

- Phenomics (describes the state of an organism as it changes with time)

- and so on......

4. Drivers for Technology

5. Networks versus pathways

Pathways or Networks (common terms in systems biology)?

-Pathways: a more defined system which you analyse and study. Interactions are shown by arrows and in most cases the nature ofthis interaction is known.

-Networks: a complex connectivity. You link many proteins togetherwith arrows to get the general topology. We probably know some biochemicalsteps but we do not understand the whole network.

http://www1.qiagen.com/literature/qiagennews/weeklyarticle/05_06/e8/images/GeneNetwork.gif http://www.bio.davidson.edu/COURSES/GENOMICS/2002/James/pathway.jpg

Network Pathway

6. Examples of systems

I. Swameye, PNAS, Feb.4, 2003

Core model

JAK-STAT signaling pathway

-Hormone (Epo)

-Receptor binding Epo

-Binding leads to transphosphorylationof JAK2 and phosphorylation of the cytoplasmic receptor domains.

-Phosphotyrosine residues 343 and 401recruit monomeric STAT-5 (x1), which gets phosphorylated (x2), it then dimerises (x3),and migrates to the nucleus (x4).

In nucleus: Stimulated transcription of targetgenes.

What happens then?

Biology

6. Examples of systems

Data - Simulations

A + B : experimental data

C + D : testing two hypothesisTime-series measurements

6. Examples of systems

Sln1AspP

ATPADP

Ypd1

Ssk1AspP

Pi

high osmolarity

?

Ypd1HisP

Ssk1

Sln1HisP Sln1v1

TCSv2TCS

v3TCS

v4TCS

v5TCS

Sln1

Ssk1

Hog1Glucose

DHAP

G3P

GlycerolTranslation

Gpd1, Gpp2,….

Gpd1

Gpp2

Signalpathway

Metabolism

Fps1

Osmotic stress

Osmoticstress

Glycerolextern

Plasma membrane

cytosol nucleus

e

i

MAP kinase cascade

Phospho relaysystem

Hog1

TranscriptionGPD1, GPP2,….

Gene expressionSsk2 Ssk2P

Pi

Pbs2 Pbs2P Pbs2P2

Pi

ATP ADP ATP ADP

Pi

Hog1

Ssk1

v1MAP

v2MAP

v-1MAP

v3MAP

v-2MAP v-3

MAP

ATP ADPATP ADP

Hog1P Hog1P2

Pi

ATP ADP ATP ADP

Pi

v4MAP v5

MAP

v-4MAP v-5

MAP

Hog1P2

Hog1P2nuc

mRNAnuc mRNAcyt

Proteinsnucleus

cytosol

vts

vex vrd

vpd

Hog1nuc

Hog1

vtrans

vtrans1

vtrans2

Glucose

Gluc-6-P

Fruc-1,6-BP

GAP DHAP

Pyruvate

Ethanol

synthesis

synthesis

3 CO2

G3P

Glycerol

NADH NAD

ADP ATP

4 NAD

4 NADH

NAD

NADH

NADH

NAD

2 ADP2 ATP NADH NAD

ATP

ADP

ATP

ADP

ATP ADP

ADP ATP

Glk1

Gpp2Gpd1

Fps1

Glucose uptake

Glycerol, ex

Phosphorelay module

MAP kinasecascade module

Gene expression module

Biophysical changes

i = f (Glycerol)Waterflow over membrane = f (i, e, t)

Volume change = f (Waterflow)(see text)

I nternal osmotic pressure

External osmotic pressure

Metabolismmodule

Figure 1

vdephos

Ptp2

vtl

v15

v14

v16

v13

v12v11

v3

v10

v1

v2

v4

v5

v6

v9

v7 v8

Edda Klipp et al, Nature Biotechnology 2005, number 8,

The High Osmolarity Glycerol (HOG) pathway in yeast

6. Examples of systems

0 30 60 90 120

0

0.2

0.4

0.6

0.8

1.

0 30 60 90 120

0.5

1.

1.5

2.

2.5

3.

0 30 60 90 120

0

0.5

1.

1.5

0 30 60 90 120

0

0.2

0.4

0.6

0.8

1.

0 30 60 90 120

0.7

0.8

0.9

1.

Time / min

Time / min

Time / min

Time / min

mRNA

Ssk1

Vo

lum

e,

rela

tive

Co

nce

ntr

atio

n,

rela

tive

Pre

ssu

re /

MP

a

Gly

cero

l, re

lativ

e

i

e

Hog1P2

Gpd1

Glycex

Glycin

Time / min

mRNA

Hog1P2

Gpd1

0 30 60 90 120Time / min

0

0.5

1

1.5

Gly

cero

l, re

lativ

e

Glycex

Glycin

Co

nce

ntr

atio

n,

rela

tive

A B

C

E F

D

t

Simulations Experimental data

Edda Klipp et al, Nature Biotechnology 2005, number 8,

Future perspectives

- Get answers to questions like: what happens, why does it happen and how is specificity achieved?

- To discover new principles and mechanisms for biological function

- Biotechnology: to get predictive cells

-To create a detailed model of cell regulation, focused on signal-transduction cascades. This could lead to system-level insights into mechanisms which could be the basis for drug discovery.

-To understand cells and eventually tissues and organs

In pharmaceutical industry: to get predictive medicines (to avoid side-effects,to individualise medicines)

Short term goal

Long term goal

Summary – What did we learn?

- Systems biology is an approach where mathematical modeling and quantitative experimental data are combined to get a system-level understanding of your biological system.

- Systems biology offers the chance to predict the outcome of complex processes and it decreases the number of experiments (experimental planninig).

- To take into account principles of robustness might provide some guidelines for how we model and analyse model complexity.

-To conduct systems biology often involves: 1) set up pathway structure based on previous knowledge (static) 2) Simulating experimental data to determine parameters 3) Predictions to test model.

-Qualitative data and quantitative data are of different types.

-It drives technology forward!!!! This might be the bottle-neck today, but when we have better technologies / methods systems biology could move faster towards a promising future.

Long term applications: To get better and predictive medicines.

References:

ArticlesHartwell et al. Nature,V 402,1999, From molecular to modular cell biologyPeter K Sorger, Current opinion in Cell Biology 2005, A reductionist´s systems biologyHiroaki Kitano, Science, V 295, 2002 Systems Biology: A Brief OverviewHiroaki Kitano, Nature, V420 2002, Computational Systems biology

BooksE.Klipp et al, System Biology in Practice, Wiley-vch verlag 2005, ISBN-13 978-3-527-31078-4L.Alberghina, H.V Westerhoff (Eds.), Systems Biology, Topics in Current Genetics, Springer-verlag 2005,ISBN-13 978-3-540-22968-1Zoltan Szallasi, Jörg Stelling, Vipul Periwal (Eds), System Modeling in Cellular Biology, A Bradford book 2006,ISBN 0-262-19548-8

Resources on the net:

http://en.wikipedia.org/wiki/Main_Pagewww.brc.dcs.gla.ac.uk, David Gilbert, Systems Biology (1) Introduction