Modeling Biological Networks Introductionorca.st.usm.edu/~pghosh/1.SystemBiology.Intro.pdf · 1. What is a Biological Network 2. What is a Cell and how it works as a signaling network

Modeling Biological Networks

Introduction

Instructor: Preetam Ghosh

http://orca.st.usm.edu/~pghosh/

[email protected]

What are Networks

• Network or Graph

– Node can have waiting spaces or no waiting spaces. It can have processing time to complete its function, once it gets the signal or no processing time. In that case the processing function may be a Boolean function to decide the outcome.

– Links can have finite throughput capacity or a relationship with instantaneous connectivity. It has directional property.

– Open Network: k nodes, nodes are interconnected by links li,j

(from node i to j) and external signals vector Sinput,i(t) and external signals vector Soutput,j.

– Closed Network: There is no external signal received by the system and there is no external output signal from the network. It is a closed system.

Node: physical/logical processing

center

Link: Physical/logical

connectivity relationshipNode

link

Sinput,i(t) Soutput,j(t).

Types of Networks/Graphs

• Open Network without waiting space at the node: Road Network, Factory Production Line (limited space)

• Open Network with waiting space at the nodes: Telecommunication Network.

• Closed Network with waiting space at the node: Computer network with batch processing.

• Network with relationship: citation network

• Random Network: Internet URL Graph (Dynamic)

• Boolean Network: Computer Logic diagram

Interesting Properties of Random Networks

• Six degrees of separation: Any two person in the world is

separated by at most six degrees. Small World property

Paul Cook

USA

Ramanath

India

degree-1 degree-2 degree-3 degree-4 degree-5 degree-6

Erdos Number: From the citation Graph, how far an author is from Erdos

(Hungarian Mathematicians who proposed the concept of Random Graphs)

Scale Free Network:

Power Law: Number of links hosted by a node is distributed

as a Power Law

% Nodes

K

Biological Networks

• Biological System

– Cell as networks of biological functions and regulatory pathways.

– Pathways are Boolean network of regulatory proteins

– Biological functions are a network of kinetic equations and biological activities.

– Tissue is a cellular automata network (?) of interacting cells of similar type with signaling transport mechanism

– Organ is a flat connected network of interacting tissues connected by different transport mechanism (links)

– Biological Process is a fully connected network of interacting organs connected by signal transport system

– Interaction of Biological processes creates a Biological system

Why Networking

• At present two approaches are used to model the Biological system

– Analytical Approach : Based on traditional mathematical methods like Biophysics, Biochemistry, Computational Biology. Computational Physiology

– Computational Approach: Based on algorithms and Artificial Intelligence like Cellular Automata, Genetic Networking. Bayesian learning.

• Networking approach is the integration of both techniques using discrete, stochastic modeling and learning algorithms with linear optimization.

Course ObjectiveOn completion of this course you will learn

1. What is a Biological Network

2. What is a Cell and how it works as a signaling network.

3. Different mathematical models of cell function

4. How to analyze a Biological network

5. How to model the Biological network by using different modeling

techniques.

Parametric modeling

Diffusion based

Force Field based molecular dynamics

Kinetic rate based

Stochastic

Metabolic Network

Boolean Network

Lesson Plan1. Elementary Bio Chemistry -1 lectures

2. Elementary Cell Biology

1. Cell Membrane -1 lecture

2. Gene Expression -1 lecture

3. Communication -1 lecture

4. Cell Growth -1 lecture

3. Introduction to Computational Cell Biology -2 lectures

4. Different Biological Network Modeling methods

1. Force Field Method -2 lecture

2. Kinetic Method - 2 lectures

3. Stochastic Method - 2 lectures

4. Metabolic Network - 2 lectures

5. Boolean Network - 2 lectures

5. Modeling Examples - 6 lectures

6. Projects:

1. Influenza viral life-cycle simulation -1 lecture

2. RNAi pathway simulation -1 lecture

3. Alzheimer’s Disease pathways simulation -1 lecture

4. S. aureus network analysis toolset -1 lecture

5. Gene regulatory network reconstruction -1 lecture

Course Grading Plan

1. Projects: Separate problem will be given to each student (or groups

of 2 students) to model/simulate biological systems: 60%

2. The grade distribution for the project:

1. Analysis of the problem and model definition: 10%

2. Assumptions and parameters definition 10%

3. Model development 20%

4. Results 40%

5. Report 20 %

3. Paper review and presentation (2) 40%

Reference books1. Molecular Biology of the Cell by Alberts et al, Garland Science,

ISBN 0-8153-3218-1

2. Computational Cell Biology, edited by C. P. Fall et al,

Interdisciplinary applied mathematics, Volume 20, Springer New

York, ISBN 0-387-95369-8

3. Mathematical Physiology, James Keener and James Sneyd,

Interdisciplinary Applied Mathematics, Volume 8, Springer, New

York, ISBN 0-387-98381-3

4.Computational Modeling of Genetic and Biochemical Networks,

edited by J. M. Bower and H. Bolouri, MIT Press, 2001, ISBN 0-

262-02481-0.

5. Fundamentals of system biology; edited by Hiroaki Kitano

Cell

• Cell is the building block of living system

• 10M-100M different living species on the earth

• Most living beings are single cell

• There are multi cellular living beings like human, that is built of many cells.

• All cells replicate their hereditary information called DNA.

• All Cells transcribe portions of DNA into same intermediary form called RNA

• All cells translate RNA to protein in the same way

• The Fragment of genetic information corresponding to one protein is one Gene

• All cells function as Biochemical factories dealing with the same basic molecular building blocks and algorithms but using different Input Signal vectors.

Cell Classification

• Eukaryotic Cell: DNA is bound by a membrane and this compartment is called nucleus.

– Example: Plants, animals

– Size: 10 mm diameter

• Prokaryotic Cell: There is no membrane separating the DNA i.e., the nucleus is not present. It is smaller in size and has a very simple structure.

– Mostly single cellular living beings.

– It is farther classified into two groups

• Bacteria– Example: E. Coli, Salmonella

– Size: 1 mm diameter

• Archaea: resembles more closely Eukaryotes at a molecular level– Example: Haloferex

Cell Classification

Three types

1. Bacteria

2. Archaea

3. Eukaryotes

Early divergent

prokaryotes

1000-4000 genes

40,000 proteinsTime

species

Evolution Vector

Ref: Molecular Biology of the cell, Alberts et al

Central Dogma

oteinRNADNA Pr

Replication

Copies of DNA created Transcription

Copy of a gene created

Translation

Amino Acids assembled

Macromolecules: Polymers of Amino Acids and Nucleotides

Double Helix

Alphabets of a DNA

{A,T,G,C}

Grammar of double Helix DNA

Complementary binding

GC

TA

AT

CG

Word delimiter :5’3’


Structure of the Double Helix


Helix Binding

Weak Bond. It can be

easily opened to take a

copy.


Cell as Complex Machine

Cell : Complex Machine of :

Chromosome

DNA & RNA

Genes & Protein

Membrane & Receptor

Membrane

Signal Receiver

Cell-Cell ModelInput Signal Vector

Output Signal Vector

Signaling

Links Signaling

Links

Control

Links

Structural

links

Building Structure

Blood

Heart

Circulatory system

ProcessVein/arteries/capillaries

Organs

Tissue

Biological Processes

1. Digestive Process

2. Blood Circulation Process

3. Respiratory Process

4. Immune System

5. Nervous system

6. Skeleton system

7. Skin system

8. Hormone system

Cell

Tissue

Organ

Signal Transport

device/Media

Skeletal muscle

Network of Biological Process

Fully Connected Graph

Nervous system

Hormone system

Respiratory system system

Blood Circulation system

Immune system

Digestive system

Reproductive System

Cell Networking Relationship

Genome Proteome

Metabolome

PT Process

Structure

& Control

Process

mRNA

Polypeptide

Enzymes & Ribosome

Replication &

Transcription

factors

Energy &

Amino Acids

Energy &

Nucleotide

Outside the cell

Signal-outSignal-in

Dynamics of Change

Time (hrs)

External Cell Vectors (hrs)

Cells

Time (~M years)

Evolutionary Change Vectors (~M years)

Species

Biological Process

Evolution Process

Modeling Complexity in Biology

Time

Space

Concentration

A0

meter

Fraction of nsecday

nM

mM

1010

1013

106

Modeling will require

1. Intelligent aggregation

2. different level of modeling

3. Integration of different levels

4. Flexibility

Homolog

Orthologs: Two separate species with two separate genes

originated from same gene.

Paralogs: The new species have two separate genes those

are modifications of a single gene.

Homolog: All modifications of gene G are homologsRef: Molecular Biology of the cell, Alberts et al

Modeling biological System

Electron

Atom

Molecule

Macro Molecule

Quantum Mechanics

Force Field

Molecular Dynamics

10-200 atoms

20 A0

femto-seconds

1 Million atoms

300 A0

10 ns

Biological Process

Biological Process

1. Biological process is driven by the Macromolecule

2. Usage of the macromolecules drive the process

3. Usage is determined by functionality

4. Structure of the macromolecules define functionality

Functionality

UsageStructure

Macromolecule

QM/MD

Models

Process Modeling

Reaction Kinetics

Stochastic Model

Metabolic Network

Boolean Network

Hybrid

Petri net

Discrete Event

Chromosome


Chromosome


Documents

Modeling Biological Networks Introductionorca.st.usm.edu/~pghosh/1.SystemBiology.Intro.pdf · 1. What is a Biological Network 2. What is a Cell and how it works as a signaling network