"Networks" The Organizing Principle of System Biology

8/12/2019 "Networks" The Organizing Principle of System Biology..

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NETWORKSThe Organising Principle of System Biology..

Presented by: Lochana Patar

DBT-AAU Centre


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Complexity of Biological System

Hierarchical organisation of life

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Complexity of Biological System

Metabolites

Genes

Proteins

Moleculesof li fe do not function in

isolation ..

but form complex networks that def ine a

cel l ....

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Why study biology on the level of interacting

and interdependent entities???

Biological Reasons

The function of a gene is NOTspecified in the DNA language

Each gene plays roles inMULTIPLEfunctions

Each function arises from co-operation of MANY genes

Function also depends onimportant properties NOTspecified by genes; epigenetics,properties of water, lipids, self-assembly etc.

Nature has built-in fail-saferedundancy thisONLYemerges at the functional level;

Mathematical Reasons

Combinatorial explosion:suppose a physiological

functions are determinedby 2 genes. With 25000genes 312*106

possibilities.

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System Biology Paradoxical

Approaches

Biology

Technology

Computatio

n

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System Biology??

Starting point for System Biology:

To have a quantitative understanding of the process taking

place in a biological Process.

Aim of System Biology:

To understand the behaviour, dynamics of a biological system.

Heart of System Biology Approach:

An iterative process between laboratory experiments and

mathematical modelling.

Systems biology is characterised as the quantitativeanalysis of dynamic interactionsbetween the

components of a biological system with the aim of

understanding the behaviour of the system as awhole and enabling predictions of its behaviour to be

made. To this end, mathematical concepts areapplied to biological systems so that an iterative

process takes place between laboratory experimentsand computer modelling.

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Main goals of system biology

1. To identity the components that constitute the biologicalsystem;

2. To know the dynamic behaviour of these components (i.e.,how their abundance or activity changes over time in various

conditions); and

3. The interactions among these components

Ultimately, this information can be combined into a model that isnot only consistent with current knowledge but provides newinsights and predictions, such as the behaviour of the system inconditions that were previously unexplored.

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NETWORKS as a Promising tool for

SYSTEM BIOLOGY

Life is a relationship among molecules and not a property ofany molecule

- Linus Pauling

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Basic Network Nomenclature

Networka collection of nodes and links

Nodes represent entities

Links represent interactions between entities

A network can be directedor undirected

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Architectural feature of biological

networks

A. Random Network B. Scale Free Network C. Hierarchical Network

It starts with N nodes and

connects each pair of nodes

with probabilityp.

Number of nodes areconnected randomly to each

other.

the nodes degrees follow a

Poisson distribution, which

indicates that most nodes have

roughly the same number of

links.

These are characterized by a

power-law degree distribution.

The probability that a node is

highly connected is statistically

more significantthan in a random

graph.highly non-uniform, most of

the nodes have only a few links.

A few nodes with a very

large number of links, which are

often called hubs, hold

these nodes together.

scale-free networks could easily

be called scale-rich

The starting point of this construction

is a small cluster of four densely

linked nodes.

This model integrates the scale free

topology with an inherent modularstructure.

It implies that sparsely connected

nodes are part of highly clustered

areas, with communication between

the different highly clustered

neighbourhoods being maintained by a

few hubs.

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Biological Networks are Scale free

Scale free networks are ubiquitous in both biological and technological system.

The origin of the scale-free topology in complex networks can be reduced to two basic mechanisms:

1. Growth Process

2. Preferential attachment

Before duplication

After duplication

B.A.

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Structure of Biological Networks

Organisation of Biological Networks

Transcription factor

Target gene

Components Local level Global level

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Biological networkscontain repeating patterns (local level)

Network MotifPatterns of

interconnections

that recur at

dif ferent parts and

with specif ic

information

processing task

Feed Forward

Motif

`Single input

MotifMultiple input

Motif

Filter noises Co-ordinates noisesIntegrates different

signals

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Network RobustnessRobustnessThe ability of complex systems to maintain their

function even when the structure of the system changessignificantly.

Scale-free networks exhibits robustness.

Tolerant to random removal of nodes (mutations)

Vulnerable to targeted attack of hubs

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Networks in Biology

v

Networks

Nodes

Links

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Yeast Protein Interaction

Networks

It illustrates the disassortativenature

of cellular

networks.

A map of proteinprotein

interactions in Saccharomyces

cerevisiae, which is based on early

yeast two-hybrid measurements.

Links represents a mutual binding

relationship.

It has a feature of scale free

networks.

Colours of the nodes indicates the

phenotypic effect of interactionbetween the corresponding proteins.

Red- lethal

Greennon lethal

Orange- slow growth

Yellow- unknown

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Tools for Network construction and

visualization

This can be done using different software :

CytoscapeCytoscape is an open source software platform for visualizing molecular interaction networks

and biological pathways and integrating these networks with annotations, gene expression

profiles and other state data.

Cell DesignerCellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks.

Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano,and are stored using the Systems Biology Markup Language (SBML

GephiGephi is an interactive visualization and exploration platform for all kinds of networks and

complex systems, dynamic and hierarchical graphs.

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Conclusion

Systems biology develops through an ongoing dialog andfeedback among experimental, computational, and theoreticalapproaches.

High-throughput experiments reveal, or allow the inference of,

the edges of global interaction networks. Despite the significant advances in the past few years,

(molecular) network biology is only in its infancy.

Future progress is expected in many directions, ranging from the

development of new theoretical methods to characterize thenetwork topology to insights into the dynamics of motif clustersand biological function.

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References

Barabasi AL, Oltvai ZN (2004). Network Biology:

Understanding the cells functional organization. NatureReviews 5, 101-112.

Albert R, (2007). Network Inference, Analysis and

Modelling in system biology. The Plant Cell 19, 3327-3338.

Chawla K, Barah P, Kuiper M and Bones A, (2011).Sysyem Biology: A Promising tool to study Abiotic stressresponses. Omics and Plant Abiotic stress tolerance, 163-

172.

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Thank you

Documents

"Networks" The Organizing Principle of System Biology