Genetics: From genes to genomes - Chapter 21: Beyond the individual gene and genome

PowerPoint to accompanyGenetics: From Genes to GenomesFourth EditionLeland H. Hartwell, Leroy Hood, Michael L. Goldberg, Ann E. Reynolds, and Lee M. SilverPrepared by Mary A. BedellUniversity of Georgia*Copyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th editionBeyond the Individual Gene and Genome*PART VICopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21Systems Biology and the Future of Medicine21.1 What Is Systems Biology?21.2 Biology as an Informational Science21.3 The Practice of Systems Biology21.4 A Systems Approach to DiseaseCHAPTER OUTLINECHAPTERWhat is systems biology?Biological system – collection of interacting elements that carry out a specific biological taskCan be interacting molecules; i.e. proteins, mRNAs, metabolites, or control elements of genesCan be interacting cells; i.e. immune system cells, hormonal network cells, or neuronal network cellsSystems biology – seeks to describe and analyze the complex interactions of components within the system and in relation to components of other systemsRequires 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 systemsWhat are the elements of the system?Use data sets generated by genomic and proteomic toolsWhat 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 perturbationsWhat gives rise to a system's emergent properties?Can sometimes be greater than the sum of individual componentsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Representation of a biological networkNodes represent molecules, metabolites, or cellsLines represent relationships between the nodesCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.2Biology as an informational scienceBiological information is hierarchicalIn systems biology, information from as many different hierarchical levels must be captured and integratedDigital genomic information has two types of sequences:Genes that encode protein and untranslated RNAsDNA sequences that are cis-control elementsAll networks are dynamic – able to respond to conditions when activatedCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*An example of a complex molecular machineDrawing of a nuclear pore in yeastThis complex contains ~ 60 proteinsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.3Example of a protein network in yeastThis network contains ~2500 proteins and 7000 linkagesCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.4Gene regulatory networks control information transmissionGene regulatory networks receive diverse inputs of information, integrate and modify the inputs, then transmit the altered information to protein networksEach gene has 3 - 30 (or more) cis-control elementsSome transcription factors control expression of two or more genes that encode other transcription factorsMay generate complex feed-forward and feedback regulatory loopsComplexity of a gene regulatory network is specified by the number of layers in each network and the number of genes in each layerCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Multiple transcription factors regulate gene expressionIn this example, six transcription factors bind to six cis-control elements to regulate when, where, and how much mRNA from this gene is transcribedCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.5Gene regulatory network involving three layers of genesCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.6Transcription factor interactions may be positive or negative and can interact with other transcription factors in a lower layer or can feedback to another layerGene regulatory network for development of the gut in sea urchinsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.7Larval development of the sea urchinCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.8The practice of systems biologyHigh throughput platforms for genomics and proteomics (Chapter 10)Powerful computational toolsStudies of simple model organisms; e.g. E. coli and yeastComparative genomicsEmploys both discovery science and hypothesis-driven scienceAcquisition of global data sets and integration of different types of dataCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*An algorithmic approach to systems biologyScan the biological literature and databases to discover all genes, mRNAs, and proteins in a cell or organismDevelop a preliminary model (descriptive, graphic, or mathematical)Formulate a hypothesis-driven query and test through genetic or environmental manipulationsIntegrate different types of graphical or mathematical dataPerform iterative perturbations with a second round of genetic and environmental manipulationsEvaluate whether the refined model can predict the behavior of the systemCopyright © 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 yeastGAL 1, GAL 5, GAL 7, and GAL 10 genes encode four enzymesOne transporter molecule carries galactose into cellFour transcription factors that turn the system on and offNine genetically perturbed yeast strains, each has a single gene knocked out, and a wild type strainGlobal 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*Fig 21.9Observations on systems approach to galactose utilization in yeastMore than 8 unexpected gene expression patterns were notedExpression patterns of 997 could be clustered into 16 groupsEach group had a similar pattern of changes in gene expression, some of which were known to be involved in other pathwaysSuggested that these other pathways were directly or indirectly connect to galactose-utilization pathwaySecond round of analyses of protein-protein and protein-DNA interactions confirmed the interactionsFor 15 genes, found evidence for posttranscriptional regulationCopyright © 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 yeastCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.10Interactions between networksGenetic perturbations of the galactose-utilizing system in yeast affect the network of interactions with other metabolic and functional systemsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.11A systems approach to diseaseDisruptions 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 stepMolecular footprints - patterns of mRNAs and proteins in disease vs normal tissues/cellsDisease stratification may be identifiedMany diseases have different subtypes within the same general phenotypeImproved diagnostic and treatment potential for different subtypesKnowledge of protein interactions can identify drug targetsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Altered cellular network can lead to diseaseCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*Fig 21.12NondiseasedDiseasedThe systems approach leads to predictive, preventive, personalized medicinePredictionIndividual genome sequence can be used to determine chance of developing a particular diseaseBlood fingerprints will allow early detection and stratification of disease typesNew prevention strategiesBetter understanding of networks will lead to more effective therapeutic agents and drugs to prevent diseasePersonalizationApply power of predictive and preventive medicine to individual needsCopyright © The McGraw-Hill Companies, Inc. Permission required to reproduce or display Hartwell et al., 4th edition, Chapter 21*