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

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

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

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

Representation of a biological network Nodes represent molecules, metabolites, or cells Lines represent relationships between the nodes Fig 21.2 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

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

An example of a complex molecular machine Drawing of a nuclear pore in yeast This complex contains ~ 60 proteins Fig 21.3 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

Example of a protein network in yeast This network contains ~2500 proteins and 7000 linkages Fig 21.4 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

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

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

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

Gene regulatory network for development of the gut in sea urchins Fig 21.7 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

Larval development of the sea urchin Fig 21.8 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

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

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

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

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

Modeling and experimental tests of the galactose utilization system in yeast Fig 21.10 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

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

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

Altered cellular network can lead to disease Nondiseased Diseased Fig 21.12 Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21

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