Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Frontier


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“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological

(David Baltimore, Nobel Laureate)

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  • An overview of the eukaryotic transcriptome through examples of its products. Transcription by RNA polymerase I of ribosomal RNA precursors is followed by removal of 5', 3' extensions and intervening sequences (blue thin lines) to generate ribosomal RNAs (blue rectangles), which are further modified by pseudouridynilation () and ribose methylation (CH3) at specific residues and assemble onto ribosomal subunits (blue circle and ellipse), which are then exported to the cytoplasm, where they mediate protein synthesis. Transcription by RNA polymerase II of mRNA precursors follows the processing pathway depicted in Figure. Exonic sequences can also be removed as part of an intron (green rectangle, in the example) to generate alternative mRNAs that can direct the synthesis of distinct protein isoforms. One class of transcripts that do not have extensive coding capacity (ncRNAs) are similarly generated and may play important cellular functions (in the example, by acting as a scaffold for the assembly of functionally connected protein factors, represented as polygons of various shapes). Some introns can generate additional transcripts with important functions in gene regulation. One example are snoRNAs, which assemble onto snoRNP RNP complexes and direct pseudouridynilation and ribose methylation of rRNA and other transcripts. Another example are precursors of miRNAs, which after cleavage by Drosha-type RNases in the nucleus and by Dicer-type enzymes in the cytoplasm, generate 20–28 dsmiRNAs that assemble onto RNP complexes that repress translation by binding to 3' UTRs of mRNAs and/or cause mRNA degradation, depending on the degree of complementarity with their target sequence. Other dsRNAs (e.g. siRNAs) can also trigger mRNA decay through the same mechanism. snoRNAs and miRNA precursors can also be generated from exonic sequences of dedicated transcripts. Small dsRNA fragments generated through bidirectional transcription—shRNAs (often from repetitive DNA—rasiRNAs) and cleavage can also induce transcriptional silencing through histone and DNA methylation.
  • Integromics / Omics Matrix are established terms, it is not my creation.
  • Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Frontier

    1. 1.  “ Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological systems” (David Baltimore, Nobel Laureate)
    2. 2. Biology Biomolecules RNA Chemistry Medicine DNA Cells Networks Proteins Engineering Computers Mathematics
    3. 3. Variomics Transcriptomics Genomics Phenomics Proteomics Metagenomics Metatranscriptomics
    4. 4. biome, CHOmics, cellome, cellomics, chronomics, clinomics, complexome, crystallomics, cytomics, cytoskeleton, degradomics, diagnomicsTM, enzymome, epigenome, expressome, fluxome, foldome, secretome, functome, functomics, genomics, glycomics, immunome, transcriptomics, integromics, interactome, kinome, ligandomics, lipoproteomics, localizome, phenomics, metabolome, pharmacometabonomics, methylome, microbiome, morphome, neurogenomics, nucleome, secretome, oncogenomics, operome, transcriptomics, ORFeome, parasitome, pathome, peptidome, pharmacogenome, pharmacomethylomics, phenomics, phylome, physiogenomics, postgenomics, predictome, promoterome, proteomics, pseudogenome, secretome, regulome, resistome, ribonome, ribonomics, riboproteomics, saccharomics, secretome, somatonome, systeome, toxicomics, transcriptome, transcriptomics, translatome, secretome, unknome, vaccinome, variomics............. (
    5. 5. Any FOOD for thoughts………..
    6. 6. Complicated!!…….Why? How can we make sense of this complexity? Can we convey our understanding of this complexity?
    7. 7. Molecular biologybiomolecule structure and function is studied at the molecular level Systems biologyspecific interactions of components in the biological system are studied – cells, tissues, organs, and ecological webs ◦ Integrative approach in which scientists study pathways and networks, will touch all areas of biology, including drug discovery Era of Molecular Biology (1953 –2001) Era of Systems Biology (2001 – ??)
    8. 8. Genomics Transcriptomics Proteomics Systems Biology 1990 1995 2000 2005 2010 2015 2020 (
    9. 9. study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. Looking at the whole system rather than at components, such as sugar metabolism or a cell nucleus Completeness is a recent aspect Mathematics/modelling-essential Whole>Sum of parts: Give Rise Emerging properties Properties ‘arise’ from components interaction (Interactomics!!)
    10. 10.  Details are now clearer…….
    11. 11. Top-down and bottom-up approach (Bruggeman and Westerhoff, 2006)
    12. 12. Challenged Vs. Control B O T T O M T O P D O W N U P Deductive: From known properties of components, system functions deduced. Properties emerge from interaction of the components. Inductive: From how the system reacts to the perturbations. One infers which components are critical and how the system may function
    13. 13. Omics (the bottom-up approach) focuses on the identification and global measurement of molecular components. Modeling (the top-down approach) attempts to form integrative (across scales) models of animal physiology and disease, although with current technologies, such modeling focuses on relatively specific questions at particular scales, e.g., at the pathway or organ levels. An intermediate approach, with the potential to bridge the two, is to generate profiling data from high-throughput assays for biological complexity, interacting active pathways, intercommunicating cell types and different environments at multiple levels (Butcher et al., 2004)
    14. 14. Basic Science/”Understanding Life” Predicting Phenotype from Genotype Understanding/Predicting Metabolism Understanding Cellular Networks Understanding Cell-Cell Communication Understanding Pathogenicity/Toxicity “Raising the Bar” for Biologists “Making Biology a Predictive Science”
    15. 15. 100’s of completed genomes 1000’s of known reactions 10,000’s of known 3D structures 100,000’s of protein-ligand interactions 1,000,000’s of known proteins & enzymes Decades of biological/chemical know-how Computational & Mathematical resources “The Push to Systems Biology”
    16. 16. Genomics Proteomics Metabolomics Phenomics Bioinformatics Genometrics Proteometrics Metabometrics Phenometrics Biosimulation
    17. 17. Genomics (HT-DNA sequencing) Mutation detection (SNP methods) Transcriptomics (Gene/Transcript measurement, SAGE, gene chips, microarrays) Proteomics (MS, 2D-PAGE, protein chips, Yeast-2hybrid, X-ray, NMR) Metabolomics (NMR, X-ray, capillary electrophoresis)
    18. 18. study of organisms in terms of their DNA sequences, or 'genomes'
    19. 19. Study of total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type
    20. 20. (Soares and Valcárcel, 2006)
    21. 21. Large-scale study of proteins, particularly their structures and functions
    22. 22. Bioinformatics Data Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those to acquire, store, organize, archive, ana lyze, or visualize such data
    23. 23. systematic study of the unique chemical fingerprints that specific cellular processes leave behind" specifically, the study of their smallmolecule metabolite profiles.
    24. 24. ( Bionaz and Loor, 2008)
    25. 25. Genome-wide reconstruction of the regulatory and metabolic network in a sequenced organism….. Gives only static information (Weckwerth, 2011)
    26. 26. Sequencing capacity ↑ Number of sequenced genomes↑ Number of SNPs identified ↑ SNP typing capacity ↑ Microarray data ↑ Proteomics data ↑ Metabolomics data ↑ Data in databases ↑ Sequencing costs ↓ SNP genotyping costs ↓ Profiling costs ↓
    27. 27. Integromics
    28. 28. Information Omics classes Genomics Transcriptomics Proteomics Metabolomics Phenomics Sequence Pathways (Reactome) Structure APIaPIAPI Data / DBMS Expression Pathways Bioinformatics Portal/Publication
    29. 29. Integromics approach in the Systems Roadmap (Sauer et al., 2007)
    30. 30. Accessing information using ontologies and web databases that contain models encoded in ML The ML ensure that models are encoded in a consistent form and allow simulation packages to import the models in a standard format
    31. 31. CGH
    32. 32. The functional sequelae of SNPs and the consequences of alterations in transcript levels can not be routinely predicted in a comprehensive manner The amount of protein does not necessarily correlate with enzyme activity or protein function due to multiple posttranslational modifications and compartmentation Examination of whole cell or tissue extracts are not necessarily indicative of the physical interactions between moieties
    33. 33. Data management: Clear indication of the source and context of the data Meaningful identifiers (everybody’s proud of their clever system that nobody else uses) Accessible data sources Models / Methods to interpret the data An honest assessment of the benefits and limits of various modeling approaches A realistic assessment of the near-term capabilities of current modeling approaches.
    34. 34. The ability to understand the limits of the data and models Complexity of mammalian systems "As the complexity of the variable increases, it becomes more important to have a solid model of what you think you can predict and to then test it explicitly, rather than less important as the machine learning enthusiasts would have it" (Michael Bittner, Tgen)
    35. 35. Atomic Scale 0.1 - 1.0 nm Coordinate data Dynamic data 0.1 - 10 ns Molecular dynamics Molecular Scale 1.0 - 10 nm Interaction data 10 ns - 10 ms Interactions Cellular Scale 10 - 100 nm Concentrations Diffusion rates 10 ms - 1000 s Fluid dynamics
    36. 36. Tissue Scale 0.01m - 1.0 m Metabolic input Metabolic output 1 s – 1 hr Process flow Organism scale 0.01m – 4.0 m Behaviors Habitats 1 hr – 100 yrs Mechanics Ecosystem scale 1 km – 1000 km Environmental impact Nutrient flow 1 yr – 1000 yrs Network Dynamics
    37. 37. If one scale (e.g., protein-protein interactions) behaves deterministically and with isolated components, then we can use plug-n-play approaches  If it behaves chaotically or stochastically, then we cannot  Most biological systems lie between this deterministic order and chaos: Complex systems 
    38. 38.     High level of biological organization. Traits broader than in human medicine: Productivity, product quality, disease resistance, fertility, behaviour, welfare, footprint Divergently selected lines that differ quantitatively in specific traits. Samples from tissues, blood or other body fluids (milk) from a large number of animals with welldocumented management, and performance recordings are available
    39. 39.  Understand underlying mechanisms of complex traits, and genotype environment-phenotype relationships  Fill the gap between genotype and phenotype: ‘Deep’ phenotyping.  “predictive biology”; Biomarkers for product quality or health issues
    40. 40. Technology development Nanotechnology and microfluidic devices High thoroughput and inexpensive genome sequencing tech. Improved computational approaches to modeling and simulation Advances in basic biological concept elucidate a catalogue, or “periodic chart” of modules that cells typically use to perform basic biological processes
    41. 41. Practical Applications: targeted prediction and control P4 Medicine (predictive, preventive, participatory medicine) : goals- personalized and Stratification of diseases and patient populations for specific diagnosis and more effective treatment More rational drug design for improved efficacy and decreased side effects Use of genetic information to determine probable health history and blood biomarker diagnostic tests. “The blood will become a window into health and disease” Restoration of a disease-perturbed network to its normal state by genetic or pharmacological intervention
    42. 42. Understanding of genes and mechanisms involved in estrous behavior, estrus regulation and milk or meat production Can unveil dynamic cellular networks that provide an important framework for drug discovery and design. The future of drug target discovery is going to be understanding the dynamics of disease-perturbed networks
    43. 43. Targeting bacterial protective pathways that are induced to remediate reactive oxygen species damage, and in particular manipulating the DNA damage repair pathways, becomes, therefore, one potential approach to potentiate the effect of antibiotics. small molecules could be produced that would lead to the creation of super-Cipro, super-Gentamicin, or super-Ampicillin Insight into bacterial cell death pathways and protective mechanisms induced by antibiotics. Network-based analyses will lead to the development of novel, more effective antibiotics, as well as ways to enhance existing antibacterial drugs. These efforts will be critical in our ongoing fight against antibiotic resistance
    44. 44. “Solving the puzzle of complex diseases, from obesity to cancer, will require holistic understanding of the interplay between factors such as genetics, diet, infectious agents, environment, behavior, and social structures.” (Elias Zerhouni, The NIH Roadmap, Science2003, 302:63- 64)
    45. 45. THANK YOU “Our brains are wired for narrative, not statistical uncertainty.” (Francis Bacon)