1. Genetics: From Genes to Genomes
PowerPoint to accompany
Genetics: From Genes to Genomes
Fourth Edition
Leland H. Hartwell, Leroy Hood, Michael L. Goldberg, Ann E. Reynolds, and Lee M. Silver
Prepared by Mary A. Bedell
University of Georgia
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2. 21 Systems Biology and the Future of Medicine CHAPTER OUTLINE PART VI
Beyond the Individual Gene and Genome
CHAPTER
Systems Biology and the Future of Medicine
CHAPTER OUTLINE
21.1 What Is Systems Biology?
21.2 Biology as an Informational Science
21.3 The Practice of Systems Biology
21.4 A Systems Approach to Disease
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3. 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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4. Four questions to guide thinking about 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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5. Representation of a biological network
Nodes represent molecules, metabolites, or cells Lines represent relationships between the nodes
Fig 21.2
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6. Biology 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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7. An example of a complex molecular machine
Drawing of a nuclear pore in yeast This complex contains ~ 60 proteins
Fig 21.3
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8. Example of a protein network in yeast
This network contains ~2500 proteins and linkages
Fig 21.4
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9. Gene regulatory networks control information 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 (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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10. Multiple transcription factors regulate gene 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
Fig 21.5
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11. Gene regulatory network involving three layers of genes
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
Fig 21.6
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12. Gene regulatory network for development of the gut in sea urchins
Fig 21.7
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13. Larval development of the sea urchin
Fig 21.8
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14. The 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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15. An 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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16. Systems approach to reveal the process of galactose utilization in yeast
Fig 21.9
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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17. Observations on systems approach to galactose 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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18. Modeling and experimental tests of the galactose utilization system in yeast
Fig 21.10
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19. Interactions between networks
Genetic perturbations of the galactose-utilizing system in yeast affect the network of interactions with other metabolic and functional systems
Fig 21.11
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20. A 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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21. Altered cellular network can lead to disease
Nondiseased
Diseased
Fig 21.12
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22. The systems approach leads to predictive, preventive, 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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