Systems Biology Overview Dr. Shaila C. Rssle 1 Topics to be - - PowerPoint PPT Presentation

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Systems Biology Overview Dr. Shaila C. Rssle 1 Topics to be - - PowerPoint PPT Presentation

Systems Biology Overview Dr. Shaila C. Rssle 1 Topics to be discussed What is Systems Biology? History the officially start point Impact and Potential of Systems Biology Properties of Systems Biology


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Systems Biology

  • Overview
  • Dr. Shaila C. Rössle

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Topics to be discussed

  • “What is Systems Biology?”
  • History – the officially start point
  • Impact and Potential of Systems Biology
  • Properties of Systems Biology
  • Methodologies and Techniques to understand Systems Biology

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Signal transduction pathways

Molecular cell biology. Lodish, Harvey 5 ed

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What is Systems Biology?

Means different things to different people:

  • Logical continuation of functional genomics
  • carrying out experiments on the genome scale
  • As a branch of mathematical biology (Hiroaki Kitano)
  • study of small systems for which sufficient parameters have

been measured to allow simulations of how the molecules function together to achieve a particular outcome.

  • Both of these things
  • Molecular biology is no longer dominated by studies of single

macromolecules – pathways, complexes or even entire

  • rganisms is now the norm

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What is “Systems Biology”?

The study of the mechanisms underlying complex biological processes as integrated systems of many interacting

  • components. Systems biology involves (1) collection of

large sets of experimental data (2) proposal of mathematical models that might account for at least some significant aspects of this data set, (3) accurate computer solution of the mathematical equations to

  • btain numerical predictions, and (4) assessment of the

quality of the model by comparing numerical simulations with the experimental data.

  • (Leroy Hood, 1999)

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ISB website in 2003

Systems Biology is an integration of data & approaches

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Organizational and Descriptional Levels

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So how can we meaningfully integrate the data?

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Disciplines associated with systems biology

  • Phenomics: phenotype – changes during its life span
  • Genomics: DNA
  • Epigenomics/Epigenetics: factors not empirically coded in the genomic

sequence (i.e. DNA methylation, Histone Acetelation etc.)

  • Transcriptomics: cell gene expression (microarrays)
  • Translatomics/Proteomics: proteins and peptides
  • Metabolomics: metabolites
  • Glycomics: carbohydrates
  • Lipidomics: lipids
  • Interactomics: interactions between molecules
  • Biomics: systems analysis of the ecosystem
  • Structural Biology – protein structure

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History

The term “Systems Biology” was introduced by an engineer at the Case Institute of Technology (now Case Western Reserve University), Michaelo Mesarovic, some forty years ago.

Proceedings of the International Syposium on Systems Theory and Biology (1968)

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Genomics, Post-genome & Systems Biology

1990 1995 2000 2005 2010 2015 2020 Genomics Post-genomic projects Systems Biology

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2000

  • Completion of the Human Genome Project
  • Occurrence of the First International Conference on Systems

Biology in Tokyo

  • Founding of the Institute for Systems Biology in Seattle (headed

by Leroy Hood)

  • Initiation of activities for SBML (Systems Biology Mark-up

Language) mainly led by John Doiyle at Caltech The year 2000 was significant:

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Institute for Systems Biology

ISB was co-founded in 2000 in Seattle, Washington by Dr. Leroy Hood, an immunologist and technologist; Dr. Alan Aderem, an immunologist and Dr. Ruedi Aebersold, a protein chemist. It has since grown to more than 300 staff members, including 13 faculty members and laboratory groups. www.systemsbiology.org

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http://sbml.org/Main_Page

SBML is a machine-readable format for representing models. It's oriented towards describing systems where biological entities are involved in, and modified by, processes that occur over time. An example of this is a network of biochemical reactions. SBML's framework is suitable for representing models commonly found in research on a number of topics, including cell signaling pathways, metabolic pathways, biochemical reactions, gene regulation, and many others.

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SBML

Tasks

  • Arrays
  • Connections
  • Database Interoperability
  • Geometry
  • Submodels
  • Component Identification
  • References
  • Diagrams
  • A description language for simulations

in systems biology

  • Meant to support non-spatial

biochemical models and the kinds of

  • perations that are possible in existing

analysis/simulation tools

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Impact and Potential of Systems Biology

  • Predictive and Personalized Medicine
  • Synthetic Biology
  • Physics and Chemistry
  • Computer Science

ISB website in 2003

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Impact and Potential of Systems Biology

  • Toward predictive and Personalized Medicine

– New P4 Medicine (Leroy Hood)

  • Predictive, preventive, personaliyed and participatory
  • A personaliyed medicine that will revolutionize health care

– Drug companies will have the opportunity for more effective means of drug discovery

  • Guided by diagnostics
  • Smaller patient populations but higher therapeutic

effectiveness

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  • Synthetic Biology

– Growing influence of enginnering approaches in biology “Synthetic biology is concerned with applying the engineering paradigm of systems design to biological systems in order to produce predictable and robust systems with novel functionalities in nature” ( NEST 2008).

Impact and Potential of Systems Biology

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Impact and Potential of Systems Biology

  • On Computer Science

– Concurrency theory methods to biological systems

  • Encouraged the community to propose a distict “algorithmic” or “executable”

approach to Systems Biology – Evolutionary computing

  • Network inference and estimation of parameters (canonical ODE models)

(Chou and Voit 2009) – Information mining approaches

  • data and text mining

– Information systems supporting various forms of collaboratories (Olson et al 2008)

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Impact and Potential of Systems Biology

  • On Biology, Physics and Chemistry

– Bionanotechnology (Biomimetics or Bionik)

  • Where bio-inspired methods are used in effecting

nanotechnological advances – Nanobiotechnology

  • Uses advances in nanoscience and nanotechnology to study

biological processes – Bioimaging (microscopy and spectroscopy)

  • Producing data on dynamics so essential for modelling in

systems biology

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Impact and Potential of Systems Biology

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Systems Biology Research

  • Experimental data are essential for modeling and

understanding biological processes and systems.

  • Without models and hypotheses, accumulated

experimental data are generally unstructured and uninformative

  • Systems biology research integrates experimental data of

diverse types with coherent models, with the goal of understanding the biological processes and systems being investigated

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Technologies which support the research activities

  • Data generation

– Collect data on the organism under study. Ongoing technologies development aims to increase throughput and efficiency, improve accuracy, and decrease the cost of this work

  • Data management

– Provide us with the means to automate portions of collecting, processing, annotating, and integrating experimental data

  • Data visualization and analysis

– Bioinformatic tools and databases – Modeling software to simulate the dynamics of biological processes or systems

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Data Generation

  • Probing genetic frameworks: What is the genomic parts list of an
  • rganism? What genes interact in concert to regulate or create a

molecular interaction network? How does genetic variation influence gene expression and protein function? – Representative technologies: DNA sequencing, genotyping, large- scale gene deletion constructs; RNAi knockouts

  • Probing gene expression patterns: What genes are up-regulated or

down-regulated in response to a genetic or environmental perturbation? What genes are expressed in what tissues under what conditions? – Representative technologies: microarrays and DNA tagging procedures

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  • Probing DNA-protein interactions: What genes does a particular

transcription factor regulate under defined experimental conditions? – Representative technology: chromatin-immunoprecipitation and gene chips to localize binding sites (ChIP-chip)

  • Probing protein-protein interactions: What proteins are present in

enzyme complexes, nuclear pore complexes, the cytoskeleton? Which proteins modify other proteins in signaling cascades? – Representative technologies: two-hybrid-based interactions; affinity purification; mass spectrometry; quantitative proteomics

  • Probing subcellular protein localization: When during development

is a protein made and where in the cell does it go? – Representative technologies: cell sorting, molecular imaging based

  • n reporter genes or antibody staining

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Data management

  • Bioinformatics pipelines (BioPerl – http://bio.perl.org)

– Collect, extract, store, and interpret data at several different levels of analysis

  • Database frameworks (MySQL)

– Store data, allow data access by query, and facilitate data curation

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Example:

SBEAMS (Systems Biology Experiment Analysis Management System) Platform for managing data derived primarily from microarray and proteomics experiments www.sbeams.org/project_description.ph

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Data visualization and analysis

  • Sources

– Literature and curated databases

  • Biochemical pathways, annotated genomes, known protein

complexes, or gene ontology tables – Large-scale computational tools

  • Gene prediction, binding sit prediction, location of genome-

wide repetitive elements, protein structure predictions – Large-scale wet lab data collection – Tables based on microarrays, proteomics, genome sequencing, protein structures

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Data visualization and analysis

  • Research is closely linked with the development of new technology

at the Institute for Systems Biology (ISB). For example, Leroy Hood and Alan Aderem co-founded the Nanosystems Biology Alliance (www.caltechcancer.org) in conjunction with faculty at Caltech and the University of California Los Angeles (UCLA) to advance research in the areas of cancer and the immune system. The Alliance´s goal is to integrate newly emerging nanotechnology and microfluidics tools for ultra-rapid disease diagnostics. These technologies have the potential to perform thousands of multi- parameter measurements on a single cell, and to perform such measurements simultaneously on a large number of cells.

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Data visualization and analysis

  • In the course of developing new technologies for research projects,

the faculty and senior scientists at the ISB have also helped spin

  • ff companies to commercialize some of the technologies

pioneered at ISB. Examples include Nanostring Technologies (www.nanostring.com), which is developing a barcode approach to molecular profiling, and Cytopeia (www.cytopeia.com) which is developing cell-sorting technologies based on Ger. Van den Engh ´s research. Commercialization represents one way that knowledge is transferred to society, eventually to be used for the improvement

  • f general medical care.

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  • Organic chemistry shows us that the

structure of the molecules determines their possible reactions.

  • One approach to study proteins is to infer

their function based on their structure, especially for active sites.

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  • Organic chemistry shows us that the

structure of the molecules determines their possible reactions.

  • One approach to study proteins is to infer

their function based on their structure, especially for active sites.

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Systems Biology vs. traditional cell and molecular biology

  • Experimental techniques in systems biology are high

throughput.

  • Intensive computation is involved from the start in

systems biology, in order to organize the data into usable computable databases.

  • Exploration in traditional biology proceeds by

successive cycles of hypothesis formation and testing; data accumulates during these cycles.

  • Systems biology initially gathers data without prior

hypothesis formation; hypothesis formation and testing comes during post-experiment data analysis and modeling.

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An example of a pathway entry in KEGG- Glycolysis

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Protein in living organismsGenerally speaking, proteins do everything in the living cells. All functions of the living

  • rganisms are related with proteins. Each protein or group of proteins are responsible for they own specific function.

That is why in bacterial cells, proteins make about a half of the dry weight of cells.Classification by protein functionsProteins are responsible for many different functions in the living cell. It is possible to classify proteins on the basis of their functions. Very often, proteins can carry few functions and such proteins can be placed into different groups, but despite this, it is possible to assign main group for each protein.Enzymes - proteins that catalyze chemical and biochemical reactions within living cell and outside. This group of proteins probably is the biggest and most important group of the proteins. Enzymes are responsible for all metabolic reactions in the living cells. Well known and very interesting examples are: DNA- and RNA-polymerases, dehydrogenases etc.Hormones - proteins that are responsible for the regulation of many processes in organisms. Hormones are usually quite small and can be classifies as peptides. Most known protein hormones are: insulin, grows factor, lipotropin, prolactin etc. Many protein hormones are predecessor of peptide hormones, such as endorfine, enkephalin etc. It is possible to increase this group of proteins by adding of all protein venoms.Transport proteins - These proteins are transporting or store some other chemical compounds and ions. Some of them are well known: cytochrome C - electron transport; haemoglobin and myoglobin - oxygen transport; albumin - fatty acid transport in the blood stream etc. It is possible to classify trance membrane protein channels as a transport proteins as well.Immunoglobulin or Antibodies - proteins that involved into immune response of the organism to neutralize large foreign molecules, which can be a part of an infection. Sometimes antibodies can act as enzymes. Sometimes this group of proteins is considered as a bigger group of protective proteins with adding such proteins as lymphocyte antigen-recognizing receptors, antivirals agents such as interferon, tumor necrosis factor (TNF). Probably the clotting of blood proteins, such as fibrin and thrombin should be classified as protective proteins as well.Structural proteins - These proteins are maintain structures of other biological components, like cells and tissues. Collagen, elastin, α-keratin, sklerotin, fibroin - these proteins are involved into formation of the whole organism body. Bacterial proteoglycans and virus coating proteins also belongs to this group of

  • proteins. Currently we do not know about other functions of these proteins.Motor proteins. These proteins can convert

chemical energy into mechanical energy. Actin and myosin are responsible for muscular motion. Sometimes it is difficult to make a strict separation between structural and motion proteins.Receptors These proteins are responsible for signal detection and translation into other type of signal. Sometimes these proteins are active only in complex with low molecular weight compounds. Very well known member of this protein family id rhodopsin - light detecting protein. Many receptors are transmembrane proteins.Signalling proteins - This group of proteins is involved into signalling translation process. Usually they significantly change conformation in presence of some signalling molecules. These proteins can act as enzymes. Other proteins, usually small, can interact with receptors. Classical example of this group

  • f proteins is GTPases.Storage proteins. These proteins contain energy, which can be released during metabolism

processes in the organism. Egg ovalbumin and milk casein are such proteins. Almost all proteins can be digested and used as a source of energy and building material by other organisms.Classification of proteins by location in the living cellProtein classification can be based on their appearance in the living cell. According to this, it is possible to classify all proteins into four main groups.Membrane or transmembrane proteins - these proteins are located within cell membrane lipid bi-layer. These proteins can be completely or partially burred in membrane.Internal proteins - these proteins are located within living cell and all functions are related with intercellular needs.External or secret proteins - these proteins are functions outside the cell they produced. Such type of proteins is more common for multicells

  • rganisms.Virus proteins - These proteins are present only in virus organism, usually as a coat for viral

particle.Classification of proteins by posttranslational modificationAfter protein translation some of them are subjected to posttranslational modification. This modification can be related with many different aspects of changes. Again this classification split all proteins into overlapped groups.Native proteins - these proteins are not changed after translation.Glico-proteins - these proteins are modified by covalent binding with linear or branched

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