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Digital Cells Nano-Bio-Info Technology

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Digital Cells Nano-Bio-Info Technology Introduction to Nanotechnology Image by John Alsop Outline Concept of a gene (extended) Gene Regulatory Networks (GRN) GRN and ... – PowerPoint PPT presentation

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Title: Digital Cells Nano-Bio-Info Technology


1
Digital Cells Nano-Bio-Info Technology
  • Introduction to
  • Nanotechnology

Image by John Alsop
2
(No Transcript)
3
Outline
  • Concept of a gene (extended)
  • Gene Regulatory Networks (GRN)
  • GRN and cells as an information system
  • Creating molecular interaction maps
  • The goal and process of digital cells
  • Using e-cell / SBML to model a cell
  • Bio-nano-info convergence

4
Central Dogma in Biology
  • DNA (sequence, expression)
  • RNA (sequence, structure)
  • Protein (sequence, structure)

Transcription
Transcription control
Translation
Protein feedback
5
Concept of a Gene
  • Why do we separate proteins from (DNA) in our
    definition of genes?
  • One is seen as distinct from the other
  • As if they had separate lives
  • Proteins are the tactical or execution side of
    a gene the field of Proteomics
  • Nucleotides are the strategic or planning side
    of a gene - Genomics

6
Fundamental Interactions
There are three semi-distinct layers of process
and information space inside a cell connected
through molecular networks
7
The (Really) Big Picture
8
Proteins and Pathways
9
Cellular Operating System
  • Genes are interchangeable parts, but must be
    tuned for synchronization, collaboration,
    workflow, messaging, etc.
  • They are metabolic.dlls part of a cellular
    operating system. They are the most basal
    autonomous code in the cellular OS.
  • Protein services must also boot with the OS,
    and regulate how OS interacts with the
    metabolome, and other signaling proteins.

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.
11
Oscillating Networks
  • Need to think about oscillating reactions
  • (protein formation / life-time) inside a cell.
  • Gene regulatory networks create inverters
    (digital inverter networks)
  • Inverters create joined oscillating reactions
    with a lag time
  • Timing from transcription to translation is
    critical, as is the half-life of the protein

12
Strategy of Genes
  • When?
  • Where?
  • How much?
  • Who with?
  • Gene circuits
  • Regulatory / inhibition
  • Promoters
  • Co-expression

13
Mechanics of Transcription
Genes rely on several molecular signals and
processes to manifest a solution, which is part
of a larger decision network
14
Genes are just Solutions
  • Successful molecular solutions involving
    aminoacyls required templates to execute
  • When, orchestrated (how), and how much
  • Executed in time, space, and abundance
  • Genes today are complex solutions
  • Most genes code for complex proteins
  • Entire genomes orchestrate a symphony
  • Organisms are autonomous collectives

15
Genetic Algorithms
16
Genetic Algorithms
17
Self-Assembled Algorithms
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
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/

20
Gene regulatory networks interface with
cellular processes
http//doegenomestolife.org/
21
Goal of Digital Cells
  • Simulate a Gene Regulatory Network
  • Goal of e-cell, CellML, and SBML projects
  • Test microarray data for biological model
  • Run expression data through GRN functions
  • Create biological cells with new functions
  • Splice in promoters to control expression
  • Create oscillating networks using operons

22
Digital Cells
  • Bio-logic gates
  • Inverters, oscillators
  • Creating genomic circuitry
  • Promoters, operons and genes
  • Multi-genic oscillating solutions

23
Digital Cells
http//www.ee.princeton.edu/people/Weiss.php
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 0the gene is active.
and B is formedcorresponding to an output bit 1.
When A is produced (right)making the input bit
1it binds to P and blocks the action of the
genepreventing B from being formed and making
the output bit 0. Weiss http//www.ee.princeton.ed
u/people/Weiss.php
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

26
Gene Regulatory Network
27
Goals of Network Modelling
  • Representation
  • Analysis
  • Communication

28
Different Network Types
  • Gene regulation networks (gene networks)
  • Describing transcriptional relationchips
  • Biochemical networks
  • Describing interaction between proteins, enzymes
    and other participants in cellular functions
  • e.g. cell cycel regulation and signal
    transduction
  • Metabolic networks
  • Describing interactions of metabolites

29
Advantages of Graphical Representation
  • Graphical representation of biochemical networks
    is two dimensional
  • Therefore greater flexibility in describing
    biochemical networks than in verbal description
  • e.g. imagine, describing a street-map

30
Diagram Proposal by A.Funashi H.Kitano
31
Process Diagram
  • Is essentially a state transition diagram
  • like in engineering or software developing
  • Following states can be represented
  • phosphorylation
  • acetylation
  • ubiquitination
  • allosteric change
  • Increasing need to use these diagrams to extract
    gene regulatory relationships to overlay with
    gene expression micro-array data

32
Notation of the Process Diagram
State transition changes the state of
modification rather than activation
Activation
Inhibition
Translocation of module
Dashes line indicates active state of a molecule
A
Specific state of molecular species
33
Gene Regulatory Networks
  • Post transcriptional interactions should be
    invisible
  • Only gene regulatory network shall be extracted

activation or inhibition (instead of state
transition

indicates AND - relationship
34
Molecular Interaction Maps (M.Aladjem, K.Kohn)
  • Features
  • MIM depict biochemical components of
    bioregulatory networks in a standard graphical
    notation (like wiring diagrams in electronics)
  • More detailed and explicit than commonly used
    graphical representations
  • Unambiguous
  • Ability to view all interactions a molecule can
    be involved
  • Depicts competing interactions as well
  • Ready access to annotations
  • Retrieval of further information from external
    resources
  • Represents consequences of interactions (e.g.
    enzyme modifies another enzyme)
  • Allows tracing of pathways within the network
  • Increases the utility of MIMs as aids to computer
    simulation

35
Molecular Interaction Maps (MIM)
  • Characteristics
  • Each molecule shown only in one location
  • All interactions and modifications can be traced
    from one point
  • Molecules can be located from an index of map
    coordinates
  • In Cell Cycle eMIMs (interactive MIMs)
    molecules serve as links to additional sources of
    information (PubMed, Gene Cards, MedMiner)

36
Symbols / Conventions used in eMIMs
Reactions
Protein A and B can bind to each other The node
represents the AB complex
A
B
X
Multimolecular complex x is AB y is
(AB)C Endless extendable
A
B
Y
C
Covalent modification of protein A. A can exist
in a phosphorylated state.
P
A
P
Cleavage of a covalent bond dephosphorylation of
A by a phosphatase.
Phtase
A
Stoichiometric conversion of A to B.
A
B
37
Symbols / Conventions used in eMIMs
Reactions
Transport of A from cytosol to nucleus. The dot
represents A after transport to the nucleus.
Cytosol
Nucleus
A
Formation of homodimer. Dot on the right
represents copy of A. Dot on line represents the
homodimer AA
A
Contingencies
Enzymatic stimulation of a reaction
Enzymatic of a reaction in trans.
Stimulation of a process. Bar indicates necessity.
Inhibition
Transcriptional activation
Transcriptional inhibition
38
Molecular Interaction Map (eMIM)
39
Pathway Diagram in KEGG
40
KEGG
  • KEGG Kyoto Encyclopedia of Genes and Genomes
  • From a SWISS-PROT entry find the EC number for
    COMT (EC 2.1.1.6 - but this doesnt link into
    KEGG)
  • Search H.sapiens database using DBGET (KEGG)
  • Catechol O-methyltransferase, membrane-bound form
    (EC 2.1.1.6) (MB-COMT)
  • Metabolism Amino Acid Metabolism Tyrosine
    metabolism PATHhsa00350
  • In the pathway maps (see next slide) click on the
    EC number or the substrate image for details.

41
(No Transcript)
42
Pathway Kinetics
43
BioSPICE Open Source
http//biospice.lbl.gov/
44
BioCyc
  • BioCyc Knowledge Library
  • The EcoCyc and MetaCyc databases are highly
    curated databases whose content is derived
    principally from the biomedical literature
  • PathoLogic - Computationally-Derived BioCyc
    Databases
  • The majority of databases in the BioCyc
    collection were created by a program called
    PathoLogic

45
(No Transcript)
46
E-Cell
  • 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

47
(No Transcript)
48
CellML
  • CellML.org The CellMLTM 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.
  • 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.

49
CellML
ltmodel name"bi_egf_pathway_1999"
cmetaid"bi_egf_pathway_1999" xmlns"http//www.c
ellml.org/cellml/1.0" xmlnscellml"http//www.ce
llml.org/cellml/1.0" xmlnscmeta"http//www.cell
ml.org/metadata/1.0" xmlnsmathml"http//www.w3.
org/1998/Math/MathML"gt ltrdfDescription
rdfabout""gt lt!-- The Human Readable Name
metadata. --gt ltdctitlegtEpidermal growth factor
stimulation of mitogen-associated protein kinase
and activation of Raslt/dctitlegt
50
SBML
  • Is one effort for machine readable
    representation of MIN
  • SBML is an XML based modelling language that
    represents biochemical networks
  • It enables exchange of biochemical network models
    between software-apps (e.g. CellDesigner)
  • http//sbml.org

51
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52
Nano-Bio-Info
  • Looking at bio through the eyes of nano
  • Properties of small living systems
  • Looking at nano through the eyes of bio
  • Self-assembly of nano-structures (life)
  • Interaction of information and molecules
  • Molecular assemblies as information, and cells as
    operating and information systems
  • Quantum world as an information system

53
Nano-Bio-Info
  • Bio-nano (Nano-bio)
  • Self assembly of life
  • Bio MEMS, GeneChip
  • Bio-info
  • DNA computing and genetic algorithms
  • Bioinformatics, digital cells, insilico biology
  • Nano-info
  • Quantum computing, nanoelectronic devices

54
Nano-Bio-Info
Nano
Quantum computing nanoelectronic devices
Self assembly Microarrays, BioMEMS
Bio
Info
Digital cells DNA computing insilico biology
Concept by Robert Cormia
55
Self Assembly
  • Follows statistical thermodynamics
  • Minimize free energy of the system
  • Seen in molecular monolayers
  • Spontaneous self-assembly
  • Building process for viral caspids
  • Layering proteins and nucleotides
  • Use nature to guide manufacturing
  • Control and guide novel structures

56
Molecular Self Assembly
57
Viral Self-Assembly
http//www.virology.net/Big_Virology/BVunassignpla
nt.html
58
Bio-Nano Convergence
59
Summary
  • Cell as an information system
  • Genome as a decision network
  • Pathways and process diagrams
  • Digital cells - insilico biology
  • Nano-Bio-Info convergence
  • Biology as an instance of nanotechnology
  • Nature as an information processing system

60
References
  • http//www.ee.princeton.edu/people/Weiss.php
  • http//www.dbi.udel.edu/
  • http//biospice.lbl.gov/
  • http//www.systems-biology.org/
  • http//www.e-cell.org/
  • http//sbml.org/
  • http//biocyc.org/
  • http//www.sbi.uni-rostock.de/teaching/research/
  • http//www.ipt.arc.nasa.gov/
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