Computational Systems Biology:

An Introduction

Eytan Ruppin, 2012

השראה: קוראים לי ריי קורצווייל ואני •אחיה לנצח

• הוא לימד מחשב להלחין מוזיקה, 17בגיל המציא את הסורק, ובעשורים 27בגיל

הבאים הפך למיליונר בזכות מאות פטנטים וחזה את מהפכות האינטרנט

, נביא ההייטק 60והסלולר. עכשיו, בגיל ריי קורצווייל גילה שאנחנו בדרך לחיי נצח

1 .Molecular biology – a (very) quick recap..

The Cell

• Basic unit of life.• Carries complete characteristics of the species.• All cells store hereditary information in DNA.• All cells transform DNA to proteins, which determine cell’s structure and function.• Two classes: eukaryotes (with nucleus) and prokaryotes (without).

http://regentsprep.org/Regents/biology/units/organization/cell.gif

DNA RNA protein

transcription translation

The hard disk

One program

Its output

:// . . / / / / .http www ornl gov hgmis publicat tko index htm

DNA Pre-mRNA

protein

transcription translation

Mature

mRNA

splicing

Gene expression

DNA Pre-mRNA

protein

transcription translation

Mature

mRNA

splicing

Gene expression

Gene

Transcription factors (TFs) control transcription by binding to specific DNA sequence motifs.

The Human Genome: numbers

• 23 pairs of chromosomes• ~3,200,000,000 bases• ~25,000 genes• Gene length: 1000-3000 bases,

spanning 30-40,000 bases• ~1,000,000 protein variants

Model Organisms

• Eukaryotes; increasing complexity• Easy to store, manipulate.

Budding yeast• 1 cell• 6K genes

Nematode worm• 959 cells• 19K genes

Fruit fly• vertebrate• 14K genes

mouse• mammal• 30K genes

High-throughput measurement

DNA RNA proteinGenome: Sequencing technologies

Transcriptome: Microarrays

Proteome: Various assays

Protein-protein interaction (PPI): yeast two-hybrid

Protein-DNA (transcriptional) interactions: chip-on-chip

Genetic interactions

2 .Systems Biology

The Reductionist Approach to Biological Research

• Explanations of things ought to be continually reduced to the very simplest entities

• Identifying individual genes, proteins and cells, and studying their specific functions

The Reductionist Approach to Biological Research (20th century

biology)Explanations of things ought to be continually reduced to the very simplest entities

Can this approach explain the behavior of a complex

system?

Building models from parts lists

•High throughput technologies signal the end of reductionism in biology

Why Build Models?(Jay Bailey, 1998)

• 1. To organize disparate information into a coherent whole

• 2. To think (and calculate) logically about what components and interactions are important in a complex system.

• 3. To discover new strategies• 4. To make important corrections to the

conventional wisdom• 5. To understand the essential qualitative

features

• "One is neither too scrupulous and sincere, nor too subjected to nature; but one is more or less master of his model, and especially of his means of expression"

When one is a master of his own model..

So what is Systems Biology?

• The study of the mechanisms underlying complex biological processes as integrated systems of many interacting components. – collection of large sets of experimental data– proposal of mathematical models that might account

for at least some significant aspects of this data set– accurate computer solution of the mathematical

equations to obtain numerical predictions,– assessment of the quality of the model by comparing

numerical simulations with the experimental data.

• First described in 1999 by Leroy Hood – Director of the Institute for Systems Biology

What’s it good for?• Basic Science/”Understanding Life”• Predicting Phenotype from Genotype• Understanding/Predicting

– Metabolism– Cellular signal trasduction– Cell-Cell Communication– Pathogenicity/Toxicity

• Biology in silico..

Virtual life..

PubMed abstracts indicate a growing interest in Systems

Biology

Human genome completed

3 .Biological Networks

From genomics to genetic circuits

• The coordinated action of multiple gene products can be viewed as a network

Transcriptional Regulatory Network

• Nodes – transcription factors (TFs) and genes;• Edges – directed from transcription factor to the

genes it regulates • Reflect the cell’s genetic regulatory circuitry• Derived through:

1062 TFs, X genes 1149 interactions

S. cerevisiae

▲ Chromatin IP ▲ Microarrays

Protein-Protein Interaction (PPI) Networks

• Nodes – proteins; • Edges – interactions• Reflect the cell’s machinery and signlaing

pathways.• High-throughput experiments:▲ Protein coIP

▲ Yeast two-hybrid

4389 proteins 14319 interactions

S. cerevisiae

Metabolic Networks• Nodes – metabolites; Edges – biochemical

reactions• Reflect the cell’s metabolic circuitry• Derived through:

1062 metabolites 1149 reactions

S. cerevisiae

▲ Biochemistry knowledge▲ Metabolic flux measurements

Systems Biology: Network States

There are many sources of information

about biological networks

Biological networks operate in thecrowded intra‐cellular environment

4. How do we model the complex biological processes encoded in these networks?

30

Modeling the Network Function

Kinetic models

Approx. kinetics

•A dynamic system with differential equations•Requires unknown data on kinetic constants and concentrations

Topological analysis

•Topological analysis•Degree distribution, motifs, functional modules

Constraint-based analysis

•Constraint-based modeling

•Boolean and discrete models, bayesian models, linear models, etc

Conventional functional models

Metabolic

PPI

Signaling

Regulatory

Abstraction level

Types of models

• Data models – reconstruction• Topological – structure of networks• Steady state – linear algebra• Dynamic states – ODEs• Thermal fluctuations – noise,

stochastic ODEs• Sensitivity – MCA, etc

Interim Summary

• The genotype‐phenotype relationship is fundamental in biology

• Systems biology promises to make this relationship mechanistic

• The core paradigm is a four step process – Components‐>networks‐>in silico models‐>phenotype

• Network reconstruction is foundational to the field and a common denominator

• Models are built to describe steady states (capabilities) and dynamics states

• And now to Monty Python something completely different..

• The Future as seen at Present

New upcoming Data

• The revolution in genome sequencing technologies

• Which leads also to new gene expression technologies

• microRNA chips• Large scale protein abundance data• Large scale metabolomics data• Completing the identification of cellular

networks• Large scale individual cell measurements in

high temporal resolution

Research Questions & Challenges – I. Basic Science

• The riddle of embryonic development• How are cells regulated?• The riddle of `junk’ DNA and the hidden world

of mRNA• How and to what extent does the normal

cellular genotype determine the phenotype? – Epigenetics..

• The emergent properties of tissues and organs• Evolutionary systems biology – the search for

LUCA, the origins of multi-cellularity, the ascent of man..

Research Questions & Challenges – II. Applications

• Charting the pathophysiology of human diseases

• Identifying new drugs and combinations of drugs

• Stems cell research and tissue and organ replacement

• Whats can microrganisms do for you?• Metagenomics and the art of sailing..• Personalized Medicine

Some potential computational avenues

• Genome association & CN studies• New approaches for modeling

integrated cellular functions – in silico cellular biology

• Models of tissues and systems • “Interfacing” with the community

and the literature..

My lab: Don’t ask what Sysbio can do for you, ask what you can do for

Sysbio • Studying cancer metabolism and predicting

and testing new anti-cancer therapies• Computational methods for predicting

biomarkers and disease diagnosis• Searching for new antibiotics that are resistant

to resistance…• Building and studying the gut metabolome• The evolution of human brains..• Metabolism of stem cells, Alzheimer’s disease,

diabetes.• Fighting aging and extending human lifespan