Digital Cells  Foothill College Nanotechnology  Image by John Alsop
 
Outline <ul><li>Concept of a gene (extended) </li></ul><ul><li>Gene Regulatory Networks (GRN) </li></ul><ul><li>GRN and ce...
Central Dogma in Biology <ul><li>DNA (sequence, expression) </li></ul><ul><li>RNA (sequence, structure) </li></ul><ul><li>...
Concept of a Gene <ul><li>Why do we separate proteins from (DNA) in our definition of genes? </li></ul><ul><li>One is seen...
Fundamental Interactions There are three ‘semi-distinct’ layers of process and information space inside a cell – connected...
The (Really) Big Picture Ion Channels Receptors Transcription Factors Ligands ELECTROPHYSIOLOGY Extracellular space Cytopl...
Proteins and Pathways
Cellular Operating System <ul><li>Genes are interchangeable parts, but must be ‘tuned’ for synchronization, collaboration,...
Genomic Decision Networks Simplified version of the phage decision network that determines whether an infected  E. coli  c...
Oscillating Networks <ul><li>Need to think about oscillating reactions </li></ul><ul><ul><li>(protein formation / life-tim...
Strategy of Genes <ul><li>When? </li></ul><ul><li>Where? </li></ul><ul><li>How much? </li></ul><ul><li>Who with? </li></ul...
Mechanics of Transcription Genes rely on several molecular signals and processes to manifest a solution, which is part of ...
Genes are  just  Solutions <ul><li>Successful molecular solutions involving aminoacyls required templates to execute </li>...
Genetic Algorithms
Genetic Algorithms
Self-Assembled Algorithms ---------------------------  1010110001011010 ATGCCAGTACTGG TACGGTCATGACC 0101001110100101 -----...
Information vs. Processing Just as in a computer, data bits and processing bits are made from the same material, 0 or 1, o...
Basic GRN Circuits Gross anatomy of a minimal gene regulatory network (GRN) embedded in a regulatory network. A regulatory...
http://doegenomestolife.org/ Gene regulatory networks ‘interface’ with cellular processes
Goal of Digital Cells <ul><li>Simulate a Gene Regulatory Network </li></ul><ul><ul><li>Goal of e-cell, CellML, and SBML pr...
Digital Cells <ul><li>Bio-logic gates </li></ul><ul><li>Inverters, oscillators </li></ul><ul><li>Creating genomic circuitr...
Digital Cells http://www.ee.princeton.edu/people/Weiss.php
Digital Cell Circuit (1) INVERSE LOGIC. A digital inverter that consists of a gene encoding the instructions for protein B...
Digital Cell Circuit (2) In this biological AND gate, the input proteins X and Y bind to and deactivate different copies o...
Gene Regulatory Network
Goals of Network Modelling <ul><li>Representation </li></ul><ul><li>Analysis </li></ul><ul><li>Communication </li></ul>Mol...
Different Network Types <ul><li>Gene regulation networks (gene networks) </li></ul><ul><ul><li>Describing transcriptional ...
Advantages of Graphical Representation <ul><li>Graphical representation of biochemical networks is two dimensional </li></...
Diagram Proposal by A.Funashi & H.Kitano ERK ERK Ras PDK-1 ERK ERK ERK RSK RSK RSK RSK RSK CREB c-Myc c-Myc Raf Ras Raf Ra...
Process Diagram <ul><li>Is essentially a state transition diagram  </li></ul><ul><ul><li>like in engineering or software d...
Notation of the Process Diagram A State transition –  changes the state of modification    rather than activation Activati...
Gene Regulatory Networks <ul><li>Post transcriptional interactions should be invisible </li></ul><ul><li>Only gene regulat...
Molecular Interaction Maps (M.Aladjem, K.Kohn) <ul><li>Features: </li></ul><ul><ul><li>MIM depict biochemical components o...
Molecular Interaction Maps (MIM) <ul><li>Characteristics: </li></ul><ul><ul><li>Each molecule shown only in one location <...
Symbols / Conventions used in eMIMs A B A B C Ph’tase A A X Y Protein  A  and  B  can bind to each other The node represen...
Symbols / Conventions used in eMIMs A A Reactions: Cytosol Nucleus Contingencies: Transport of  A  from cytosol to nucleus...
Molecular Interaction Map (eMIM)
KEGG <ul><li>KEGG – Kyoto Encyclopedia of Genes and Genomes </li></ul><ul><li>From a SWISS-PROT entry find the EC number f...
Pathway Diagram in KEGG
Microarrays And Models Reliable Microarray  Measurements Predictive Models Model Validation Experiments Hypothesis Biology...
Pathway Kinetics
BioSPICE – Open Source http://biospice. lbl .gov/
BioCyc <ul><li>BioCyc  Knowledge Library </li></ul><ul><li>The  EcoCyc  and  MetaCyc  databases are highly curated databas...
 
E-Cell <ul><li>E-Cell System  is an object-oriented software suite for modeling, simulation, and analysis of large scale c...
 
 
CellML <ul><li>CellML.org  The CellML TM  language is an  XML -based markup language being developed by  Physiome Sciences...
CellML < model  name =&quot; bi_egf_pathway_1999 &quot;  cmeta:id =&quot; bi_egf_pathway_1999 &quot;  xmlns =&quot; http:/...
SBML <ul><li>Is  one effort for machine readable representation of “MIN” </li></ul><ul><li>SBML is an XML based modelling ...
 
Bio-Nano-Info <ul><li>Looking at bio through the eyes of nano </li></ul><ul><ul><li>Physical properties of small systems <...
<ul><li>The universe’s nanoscale properties affect the processing of three attributes  </li></ul><ul><ul><li>Energy </li><...
Self Assembly <ul><li>Follows statistical thermodynamics </li></ul><ul><li>Seen in molecular monolayers </li></ul><ul><li>...
Molecular Self Assembly Figure1:  3D diagram of a lipid bilayer membrane - water molecules not represented for clarity htt...
Viral Self-Assembly http://www.virology.net/Big_Virology/BVunassignplant.html
Bio-Nano Convergence
Summary  <ul><li>Cell as an information system </li></ul><ul><li>Genome as a decision network </li></ul><ul><li>Pathways a...
References <ul><li>http://www. ee .princeton.edu/people/Weiss.php </li></ul><ul><li>http://www.dbi.udel.edu/   </li></ul><...
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Digital Cells

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  • Digital Cells

    1. 1. Digital Cells Foothill College Nanotechnology Image by John Alsop
    2. 3. Outline <ul><li>Concept of a gene (extended) </li></ul><ul><li>Gene Regulatory Networks (GRN) </li></ul><ul><li>GRN and cells as an information system </li></ul><ul><li>Creating molecular interaction maps </li></ul><ul><li>The goal and process of digital cells </li></ul><ul><li>Using e-cell / SBML to model a cell </li></ul><ul><li>Bio-nano-info convergence </li></ul>
    3. 4. Central Dogma in Biology <ul><li>DNA (sequence, expression) </li></ul><ul><li>RNA (sequence, structure) </li></ul><ul><li>Protein (sequence, structure) </li></ul>Protein feedback Transcription control Transcription Translation
    4. 5. Concept of a Gene <ul><li>Why do we separate proteins from (DNA) in our definition of genes? </li></ul><ul><li>One is seen as “distinct” from the other </li></ul><ul><ul><li>As if they had separate lives </li></ul></ul><ul><li>Proteins are the tactical or “execution” side of a gene – the field of Proteomics </li></ul><ul><li>Nucleotides are the strategic or “planning” side of a gene - Genomics </li></ul>
    5. 6. Fundamental Interactions There are three ‘semi-distinct’ layers of process and information space inside a cell – connected through molecular networks
    6. 7. The (Really) Big Picture Ion Channels Receptors Transcription Factors Ligands ELECTROPHYSIOLOGY Extracellular space Cytoplasm Nucleus Translation + processing cis sites Intracellular Signaling Genetic Regulatory Network mRNA
    7. 8. Proteins and Pathways
    8. 9. Cellular Operating System <ul><li>Genes are interchangeable parts, but must be ‘tuned’ for synchronization, collaboration, workflow, messaging, etc. </li></ul><ul><li>They are ‘metabolic.dlls’ – part of a cellular operating system. They are the most basal autonomous code in the cellular OS. </li></ul><ul><li>Protein services must also ‘boot’ with the OS, and regulate how OS interacts with the metabolome, and other signaling proteins. </li></ul>
    9. 10. Genomic Decision Networks Simplified version of the phage decision network that determines whether an infected E. coli cell follows the lytic or lysogenic pathway. Dashed arrows indicate the direction of transcription, and bold arrows indicate regulatory interactions between a gene product and particular DNA region.
    10. 11. Oscillating Networks <ul><li>Need to think about oscillating reactions </li></ul><ul><ul><li>(protein formation / life-time) inside a cell. </li></ul></ul><ul><li>Gene regulatory networks create inverters (digital inverter networks) </li></ul><ul><li>Inverters create ‘joined’ oscillating reactions with a lag time </li></ul><ul><ul><li>Timing from transcription to translation is critical, as is the half-life of the protein </li></ul></ul>
    11. 12. Strategy of Genes <ul><li>When? </li></ul><ul><li>Where? </li></ul><ul><li>How much? </li></ul><ul><li>Who with? </li></ul><ul><li>Gene circuits </li></ul><ul><ul><li>Regulatory / inhibition </li></ul></ul><ul><ul><li>Promoters </li></ul></ul><ul><ul><li>Co-expression </li></ul></ul>
    12. 13. Mechanics of Transcription Genes rely on several molecular signals and processes to manifest a solution, which is part of a larger decision network
    13. 14. Genes are just Solutions <ul><li>Successful molecular solutions involving aminoacyls required templates to execute </li></ul><ul><ul><li>When, orchestrated (how), and how much </li></ul></ul><ul><ul><li>Executed in time, space, and abundance </li></ul></ul><ul><li>Genes today are complex solutions </li></ul><ul><ul><li>Most “genes” code for complex proteins </li></ul></ul><ul><li>Entire genomes orchestrate a symphony </li></ul><ul><ul><li>Organisms are autonomous collectives </li></ul></ul>
    14. 15. Genetic Algorithms
    15. 16. Genetic Algorithms
    16. 17. Self-Assembled Algorithms --------------------------- 1010110001011010 ATGCCAGTACTGG TACGGTCATGACC 0101001110100101 ---------------------------
    17. 18. Information vs. Processing Just as in a computer, data bits and processing bits are made from the same material, 0 or 1, or A, T, C, G, or U in biology
    18. 19. Basic GRN Circuits Gross anatomy of a minimal gene regulatory network (GRN) embedded in a regulatory network. A regulatory network can be viewed as a cellular input-output device. http://doegenomestolife.org/
    19. 20. http://doegenomestolife.org/ Gene regulatory networks ‘interface’ with cellular processes
    20. 21. Goal of Digital Cells <ul><li>Simulate a Gene Regulatory Network </li></ul><ul><ul><li>Goal of e-cell, CellML, and SBML projects </li></ul></ul><ul><li>Test microarray data for biological model </li></ul><ul><ul><li>Run expression data through GRN functions </li></ul></ul><ul><li>Create biological cells with new functions </li></ul><ul><ul><li>Splice in promoters to control expression </li></ul></ul><ul><ul><li>Create oscillating networks using operons </li></ul></ul>
    21. 22. Digital Cells <ul><li>Bio-logic gates </li></ul><ul><li>Inverters, oscillators </li></ul><ul><li>Creating genomic circuitry </li></ul><ul><li>Promoters, operons and genes </li></ul><ul><li>Multi-genic oscillating solutions </li></ul>
    22. 23. Digital Cells http://www.ee.princeton.edu/people/Weiss.php
    23. 24. Digital Cell Circuit (1) INVERSE LOGIC. A digital inverter that consists of a gene encoding the instructions for protein B and containing a region (P) to which protein A binds. When A is absent (left)—a situation representing the input bit 0—the gene is active. and B is formed—corresponding to an output bit 1. When A is produced (right)—making the input bit 1—it binds to P and blocks the action of the gene—preventing B from being formed and making the output bit 0. Weiss http://www.ee.princeton.edu/people/Weiss.php
    24. 25. Digital Cell Circuit (2) In this biological AND gate, the input proteins X and Y bind to and deactivate different copies of the gene that encodes protein R. This protein, in turn, deactivates the gene for protein Z, the output protein. If X and Y are both present, making both input bits 1, then R is not built but Z is, making the output bit 1. In the absence of X or Y or both, at least one of the genes on the left actively builds R, which goes on to block the construction of Z, making the output bit 0. Weiss http://www.ee.princeton.edu/people/Weiss.php
    25. 26. Gene Regulatory Network
    26. 27. Goals of Network Modelling <ul><li>Representation </li></ul><ul><li>Analysis </li></ul><ul><li>Communication </li></ul>Molecular interaction networks Molecular interaction networks Molecular interaction networks
    27. 28. Different Network Types <ul><li>Gene regulation networks (gene networks) </li></ul><ul><ul><li>Describing transcriptional relationchips </li></ul></ul><ul><li>Biochemical networks </li></ul><ul><ul><li>Describing interaction between proteins, enzymes and other participants in cellular functions </li></ul></ul><ul><ul><li>e.g. cell cycel regulation and signal transduction </li></ul></ul><ul><li>Metabolic networks </li></ul><ul><ul><li>Describing interactions of metabolites </li></ul></ul>
    28. 29. Advantages of Graphical Representation <ul><li>Graphical representation of biochemical networks is two dimensional </li></ul><ul><li>Therefore greater flexibility in describing biochemical networks than in verbal description </li></ul><ul><ul><li>e.g. imagine, describing a street-map </li></ul></ul>
    29. 30. Diagram Proposal by A.Funashi & H.Kitano ERK ERK Ras PDK-1 ERK ERK ERK RSK RSK RSK RSK RSK CREB c-Myc c-Myc Raf Ras Raf Raf MEK MEK ERK ERK CREB P P P P * * P P P P P P P P P P P P P P P P P Process Diagram
    30. 31. Process Diagram <ul><li>Is essentially a state transition diagram </li></ul><ul><ul><li>like in engineering or software developing </li></ul></ul><ul><li>Following states can be represented: </li></ul><ul><ul><li>phosphorylation </li></ul></ul><ul><ul><li>acetylation </li></ul></ul><ul><ul><li>ubiquitination </li></ul></ul><ul><ul><li>allosteric change </li></ul></ul><ul><li>Increasing need to use these diagrams to extract gene regulatory relationships to overlay with gene expression micro-array data </li></ul>
    31. 32. Notation of the Process Diagram A State transition – changes the state of modification rather than activation Activation Inhibition Translocation of module Dashes line indicates active state of a molecule Specific state of molecular species A
    32. 33. Gene Regulatory Networks <ul><li>Post transcriptional interactions should be invisible </li></ul><ul><li>Only gene regulatory network shall be extracted </li></ul>activation or inhibition (instead of state transition & indicates ‘AND’ - relationship
    33. 34. Molecular Interaction Maps (M.Aladjem, K.Kohn) <ul><li>Features: </li></ul><ul><ul><li>MIM depict biochemical components of bioregulatory networks in a standard graphical notation (like “wiring diagrams” in electronics) </li></ul></ul><ul><ul><li>More detailed and explicit than commonly used graphical representations </li></ul></ul><ul><ul><li>Unambiguous </li></ul></ul><ul><ul><li>Ability to view all interactions a molecule can be involved </li></ul></ul><ul><ul><li>Depicts competing interactions as well </li></ul></ul><ul><ul><li>Ready access to annotations </li></ul></ul><ul><ul><li>Retrieval of further information from external resources </li></ul></ul><ul><ul><li>Represents consequences of interactions (e.g. enzyme modifies another enzyme) </li></ul></ul><ul><ul><ul><li>Allows tracing of pathways within the network </li></ul></ul></ul><ul><ul><ul><li>Increases the utility of MIMs as aids to computer simulation </li></ul></ul></ul>
    34. 35. Molecular Interaction Maps (MIM) <ul><li>Characteristics: </li></ul><ul><ul><li>Each molecule shown only in one location </li></ul></ul><ul><ul><ul><li>All interactions and modifications can be traced from one point </li></ul></ul></ul><ul><ul><ul><li>Molecules can be located from an index of map coordinates </li></ul></ul></ul><ul><ul><li>In “Cell Cycle eMIMs” (interactive MIMs) molecules serve as links to additional sources of information (PubMed, Gene Cards, MedMiner) </li></ul></ul>
    35. 36. Symbols / Conventions used in eMIMs A B A B C Ph’tase A A X Y Protein A and B can bind to each other The node represents the A:B complex Multimolecular complex: x is A:B; y is (A:B):C Endless extendable Reactions: P P A B Covalent modification of protein A. A can exist in a phosphorylated state. Cleavage of a covalent bond: dephosphorylation of A by a phosphatase. Stoichiometric conversion of A to B .
    36. 37. Symbols / Conventions used in eMIMs A A Reactions: Cytosol Nucleus Contingencies: Transport of A from cytosol to nucleus. The dot represents A after transport to the nucleus. Formation of homodimer. Dot on the right represents copy of A . Dot on line represents the homodimer A:A Enzymatic stimulation of a reaction Enzymatic of a reaction in trans. Stimulation of a process. Bar indicates necessity. Inhibition Transcriptional activation Transcriptional inhibition
    37. 38. Molecular Interaction Map (eMIM)
    38. 39. KEGG <ul><li>KEGG – Kyoto Encyclopedia of Genes and Genomes </li></ul><ul><li>From a SWISS-PROT entry find the EC number for COMT (EC: 2.1.1.6 - but this doesn’t link into KEGG) </li></ul><ul><li>Search H.sapiens database using DBGET (KEGG) </li></ul><ul><ul><li>Catechol O-methyltransferase , membrane-bound form (EC 2.1.1.6) (MB-COMT) </li></ul></ul><ul><ul><li>Metabolism; Amino Acid Metabolism; Tyrosine metabolism [PATH: hsa00350 ] </li></ul></ul><ul><li>In the pathway maps (see next slide) click on the EC number or the substrate image for details. </li></ul>
    39. 40. Pathway Diagram in KEGG
    40. 41. Microarrays And Models Reliable Microarray Measurements Predictive Models Model Validation Experiments Hypothesis Biology Engineering Delaware Biotech Institute
    41. 42. Pathway Kinetics
    42. 43. BioSPICE – Open Source http://biospice. lbl .gov/
    43. 44. BioCyc <ul><li>BioCyc Knowledge Library </li></ul><ul><li>The EcoCyc and MetaCyc databases are highly curated databases whose content is derived principally from the biomedical literature </li></ul><ul><li>PathoLogic - Computationally-Derived BioCyc Databases </li></ul><ul><ul><li>The majority of databases in the BioCyc collection were created by a program called PathoLogic </li></ul></ul>
    44. 46. E-Cell <ul><li>E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells. The version 3 allows many components driven by multiple algorithms with different timescales to coexist </li></ul>
    45. 49. CellML <ul><li>CellML.org The CellML TM language is an XML -based markup language being developed by Physiome Sciences Inc. in Princeton, New Jersey, in conjunction with the Bioengineering Institute at the University of Auckland and affiliated research groups. </li></ul><ul><li>The purpose of CellML is to store and exchange computer-based biological models. CellML allows scientists to share models even if they are using different model-building software. It also enables them to reuse components from one model in another, thus accelerating model building. </li></ul>
    46. 50. CellML < model name =&quot; bi_egf_pathway_1999 &quot; cmeta:id =&quot; bi_egf_pathway_1999 &quot; xmlns =&quot; http://www.cellml.org/cellml/1.0# &quot; xmlns:cellml =&quot; http://www.cellml.org/cellml/1.0# &quot; xmlns:cmeta =&quot; http://www.cellml.org/metadata/1.0# &quot; xmlns:mathml =&quot; http://www.w3.org/1998/Math/MathML &quot;> < rdf:Description rdf:about =&quot;&quot;> <!-- The Human Readable Name metadata. --> < dc:title > Epidermal growth factor stimulation of mitogen-associated protein kinase and activation of Ras </ dc:title >
    47. 51. SBML <ul><li>Is one effort for machine readable representation of “MIN” </li></ul><ul><li>SBML is an XML based modelling language that represents biochemical networks </li></ul><ul><li>It enables exchange of biochemical network models between software-apps (e.g. CellDesigner) </li></ul><ul><li>http:// sbml .org </li></ul>
    48. 53. Bio-Nano-Info <ul><li>Looking at bio through the eyes of nano </li></ul><ul><ul><li>Physical properties of small systems </li></ul></ul><ul><li>Looking at nano through the eyes of bio </li></ul><ul><ul><li>Self-assembly of nano-structures </li></ul></ul><ul><li>Interaction of information and molecules </li></ul><ul><ul><li>Molecular assemblies as information and operating systems - nano execution of IT </li></ul></ul>
    49. 54. <ul><li>The universe’s nanoscale properties affect the processing of three attributes </li></ul><ul><ul><li>Energy </li></ul></ul><ul><ul><li>Mass </li></ul></ul><ul><ul><li>Information </li></ul></ul><ul><li>Biology leverages these to produce a cellular operating system, metabolism, and complex self-assembled structures </li></ul>Three Dimensions of Nano
    50. 55. Self Assembly <ul><li>Follows statistical thermodynamics </li></ul><ul><li>Seen in molecular monolayers </li></ul><ul><li>Building process for viral caspids </li></ul><ul><li>Use nature to guide manufacturing </li></ul><ul><ul><li>Control and guide novel structures </li></ul></ul>
    51. 56. Molecular Self Assembly Figure1: 3D diagram of a lipid bilayer membrane - water molecules not represented for clarity http://www.shu.ac.uk/schools/research/mri/model/micelles/micelles.htm <ul><li>Figure 2: Different lipid model </li></ul><ul><li>top : multi-particles lipid molecule </li></ul><ul><li>bottom: single-particle lipid molecule </li></ul>
    52. 57. Viral Self-Assembly http://www.virology.net/Big_Virology/BVunassignplant.html
    53. 58. Bio-Nano Convergence
    54. 59. Summary <ul><li>Cell as an information system </li></ul><ul><li>Genome as a decision network </li></ul><ul><li>Pathways and process diagrams </li></ul><ul><li>Digital cells - insilico biology </li></ul><ul><li>Bio-nano-info convergence </li></ul><ul><ul><li>Biology as an ‘instance’ of nanotechnology </li></ul></ul><ul><ul><li>Nature as an information (processing) system </li></ul></ul>
    55. 60. References <ul><li>http://www. ee .princeton.edu/people/Weiss.php </li></ul><ul><li>http://www.dbi.udel.edu/ </li></ul><ul><li>http://biospice.lbl.gov/ </li></ul><ul><li>http://www.systems-biology.org/ </li></ul><ul><li>http://www.e-cell.org/ </li></ul><ul><li>http://sbml.org/ </li></ul><ul><li>http://biocyc.org/ </li></ul><ul><li>http://www.sbi.uni-rostock.de/teaching/research/ </li></ul><ul><li>http://www.ipt.arc.nasa.gov/ </li></ul>

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