PowerPoint to accompany Genetics: From Genes to Genomes Fourth Edition Leland H. Hartwell, Leroy Hood, Michael L. Goldberg, Ann E. Reynolds, and Lee M.

PowerPoint to accompany

Genetics: From Genes to GenomesFourth Edition

Leland H. Hartwell, Leroy Hood, Michael L. Goldberg, Ann E. Reynolds, and Lee M. Silver

Prepared by Mary A. BedellUniversity of Georgia

1Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display

Hartwell et al., 4th edition

Beyond the Individual Gene and GenomeBeyond the Individual Gene and Genome

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PART PART VIVI

Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

Systems Biology and the Future of Medicine

21.1 What Is Systems Biology?21.2 Biology as an Informational Science21.3 The Practice of Systems Biology21.4 A Systems Approach to Disease

CHAPTER OUTLINECHAPTER OUTLINE

CHAPTERCHAPTERCHAPTERCHAPTER

What is systems biology?What is systems biology?

Biological system – collection of interacting elements that carry out a specific biological task

• Can be interacting molecules; i.e. proteins, mRNAs, metabolites, or control elements of genes

• Can be interacting cells; i.e. immune system cells, hormonal network cells, or neuronal network cells

Systems biology – seeks to describe and analyze the complex interactions of components within the system and in relation to components of other systems

• Requires a cross-disciplinary approach – teams of biologists, computer scientists, chemists, engineers, mathematicians, and physicists


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Four questions to guide thinking Four questions to guide thinking about biological systemsabout biological systems

What are the elements of the system?

• Use data sets generated by genomic and proteomic tools

What physical associations occur between the elements?

• e.g. Protein-protein, protein-DNA, cell-cell, etc.

What happens when the system is perturbed?

• Genetic or environmental perturbations

What gives rise to a system's emergent properties?

• Can sometimes be greater than the sum of individual components


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Representation of a biological networkRepresentation of a biological network

Nodes represent molecules, metabolites, or cells

Lines represent relationships between the nodes


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Fig 21.2

Biology as an informational scienceBiology as an informational science

Biological information is hierarchical

In systems biology, information from as many different hierarchical levels must be captured and integrated

Digital genomic information has two types of sequences:

• Genes that encode protein and untranslated RNAs

• DNA sequences that are cis-control elements

All networks are dynamic – able to respond to conditions when activated


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An example of a complex molecular machineAn example of a complex molecular machine

Drawing of a nuclear pore in yeast

This complex contains ~ 60 proteins


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Fig 21.3

Example of a protein network in yeastExample of a protein network in yeast

This network contains ~2500 proteins and 7000 linkages


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Fig 21.4

Gene regulatory networks control Gene regulatory networks control information transmissioninformation transmission

Gene regulatory networks receive diverse inputs of information, integrate and modify the inputs, then transmit the altered information to protein networks

Each gene has 3 - 30 (or more) cis-control elements

Some transcription factors control expression of two or more genes that encode other transcription factors

• May generate complex feed-forward and feedback regulatory loops

Complexity of a gene regulatory network is specified by the number of layers in each network and the number of genes in each layer


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Multiple transcription factors regulate Multiple transcription factors regulate gene expressiongene expression

In this example, six transcription factors bind to six cis-control elements to regulate when, where, and how much mRNA from this gene is transcribed


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Fig 21.5

Gene regulatory network involving Gene regulatory network involving three layers of genesthree layers of genes


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Fig 21.6

Transcription factor interactions may be positive or negative and can interact with other transcription factors in a lower layer or can feedback to another layer

Gene regulatory network for development Gene regulatory network for development of the gut in sea urchinsof the gut in sea urchins


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Fig 21.7

Larval development of the sea urchinLarval development of the sea urchin


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Fig 21.8

The practice of systems biologyThe practice of systems biology

High throughput platforms for genomics and proteomics (Chapter 10)

Powerful computational tools

Studies of simple model organisms; e.g. E. coli and yeast

Comparative genomics

Employs both discovery science and hypothesis-driven science

Acquisition of global data sets and integration of different types of data


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An algorithmic approach to systems biologyAn algorithmic approach to systems biology

Scan the biological literature and databases to discover all genes, mRNAs, and proteins in a cell or organism

Develop a preliminary model (descriptive, graphic, or mathematical)

Formulate a hypothesis-driven query and test through genetic or environmental manipulations

Integrate different types of graphical or mathematical data

Perform iterative perturbations with a second round of genetic and environmental manipulations

Evaluate whether the refined model can predict the behavior of the system


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Systems approach to reveal the process of Systems approach to reveal the process of galactose utilization in yeastgalactose utilization in yeast

GAL 1, GAL 5, GAL 7, and GAL 10 genes encode four enzymes

One transporter molecule carries galactose into cell

Four transcription factors that turn the system on and off

Nine genetically perturbed yeast strains, each has a single gene knocked out, and a wild type strain

Global microarrays from cells grown in the presence and absence of galactose (all 6000 yeast genes)


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Fig 21.9

Observations on systems approach to Observations on systems approach to galactose utilization in yeastgalactose utilization in yeast

More than 8 unexpected gene expression patterns were noted

Expression patterns of 997 could be clustered into 16 groups

• Each group had a similar pattern of changes in gene expression, some of which were known to be involved in other pathways

• Suggested that these other pathways were directly or indirectly connect to galactose-utilization pathway

Second round of analyses of protein-protein and protein-DNA interactions confirmed the interactions

For 15 genes, found evidence for posttranscriptional regulation


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Modeling and experimental tests of the Modeling and experimental tests of the galactose utilization system in yeastgalactose utilization system in yeast


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Fig 21.10

Interactions Interactions between between networksnetworks

Genetic perturbations of the galactose-utilizing system in yeast affect the network of interactions with other metabolic and functional systems


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Fig 21.11

A systems approach to diseaseA systems approach to disease

Disruptions that result in disease may arise from mutated genes (e.g. cancer), or from infection by foreign agents (e.g. AIDS, smallpox, the flu)

Identification of biomarkers is a first step

• Molecular footprints - patterns of mRNAs and proteins in disease vs normal tissues/cells

Disease stratification may be identified

• Many diseases have different subtypes within the same general phenotype

• Improved diagnostic and treatment potential for different subtypes

Knowledge of protein interactions can identify drug targets


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Altered cellular network can lead to diseaseAltered cellular network can lead to disease


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Fig 21.12Nondiseased Diseased

The systems approach leads to predictive, The systems approach leads to predictive, preventive, personalized medicinepreventive, personalized medicine

Prediction

• Individual genome sequence can be used to determine chance of developing a particular disease

• Blood fingerprints will allow early detection and stratification of disease types

New prevention strategies

• Better understanding of networks will lead to more effective therapeutic agents and drugs to prevent disease

Personalization

• Apply power of predictive and preventive medicine to individual needs


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PowerPoint to accompany Genetics: From Genes to Genomes Fourth Edition Leland H. Hartwell, Leroy Hood, Michael L. Goldberg, Ann E. Reynolds, and Lee M.

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