Computational tools for whole-cell simulation

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Computational tools for whole-cell
simulation Cara Haney (Plant Science) E-CELL
software environment for whole-cell
simulation Tomita et al. 1999. Bioinformatics
15(1) 72-84 Mathematical simulation and
analysis of cellular metabolism and
regulation Goryanin et al. 1999. Bioinformatics
15(9) 749-758
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Questions addressed in E-CELL

• Can gene expression, signaling and metabolism be
  simulated in a manner that will allow one to make
  predications about a cell?
• In simplifying a cell, what functions can be
  sacrificed?
• What is the minimal gene set?

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Overview

• Simple cell based on Mycoplasma genitalium
• User can define interactions between proteins,
  DNA and RNA within the cell, etc. as sets of
  (first order) reaction rules
• User can observe changes in proteins, etc.

M. Genitalium www.nature.come/nsu/010222/010222-17
.html
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Running the Program

• Lists loaded at runtime
• Substances
• Rule list
• System List
• Calculates change in concentration of substrates
  over a user-specified time interval
• User can select either first-order Euler error
  is O(?t2) or fourth-order Runge-Kutta O(?t5)
  integration methods for each compartment

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Cell Model

• Hypothetical minimal cell from M. genitalium
• Only genes essential for metabolism
• Cell can take up glucose from environment and
  generates ATP by turning glucose into lactate via
  glycolysis and fermentation. Lactate is exported
  from the cell
• Transcription and translation modeled by
  including transcription factors, rRNA, tRNA
• Cell takes up glycerol and fatty acids in order
  to maintain membrane structure
• Cell does not replicate

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• Metabolism in the model cell
• Includes glycolysis, phospholipid biosynthesis,
  and transcription and translation metabolisms
• Does not include machinery for replication (DNA
  replication, cell cycle), amino acid/nucleotide
  synthesis

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Classes of Objects

• Substance
• all molecular species within the cell
• Genes
• Modeled as class GenomicElements with coding
  sequences, protein binding sites and intergenic
  spacers
• Gene class includes transcribed GenomicElements
• 120 (out of 507) M. genetalium. 7 from other
  organisms.
• includes enzymes to recycle nucleotides and amino
  acids

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Genes in the cell
Gene type M. Gen Other Total
Glycolysis Lactate fermentation Phospholipid biosynthesis Phosophotransferase system Glycerol uptake RNA polymerase Amino Acid metabolism Ribosomal L. subunit Ribosomal S. subunit rRNA tRNA tRNA ligase Initiation factor Elongation factor 9 1 4 2 1 6 2 30 19 2 20 19 4 1 0 0 4 0 0 2 0 0 0 0 0 1 0 0 9 1 8 2 1 8 2 30 19 2 20 20 4 1
Proteins coding genes RNA coding genes Total 98 22 120 7 0 7 105 22 127
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Classes of Objects cont.

• Reaction Rules
• One substance turned into another via an enzyme
• D-fructose 6 phosphate D-fructose 1-6
  bisphosphate
• Can also represent formation of complexes and
  movement of substances within the cell
• No repressors/enhancers (genes are never turned
  on or off) although user can specify gene
  regulation
• Each protein and mRNA contain equal proportions
  of aas and nucleotides

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Reaction Kinetics

• Reactions are modeled from EcoCyc and KEGG
• Non-enzymatic reactions
• v k ? Sivi
• Enzymatic Reactions (Mechaelis-Menton)
• Vmax S
• S Km
• Also works for a number of substrates and
  products or formation/degredation of molecular
  complexes

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Virtual Experiments
ATP initially increases
Starve cell by decreasing glucose
Level of ATP plummets cell dies
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Changes in mRNA levels upon drop of ATP due to
Glucose Deprivation
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Applications

• Optimization of culture systems
• Minimal gene set
• Discover new gene functions
• Model more complex organisms
• Genetic engineering
• Drugs

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The good and the bad

• As is, can it tell us anything about the cell?
• No repressors/enhancers (genes are not turned on
  or off)
• Cell cannot replicate
• No aa/nucleotide biosynthesis
• Even modified, can it really tell us anything new?

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• Mathematical simulation and analysis of cellular
  metabolism and regulation
• Interface for dealing with systems of
  differential equations.
• Enter a matrix of equations, has ODE (ordinary
  differential equation) solver
• In order to use this for biological applications
• Assumes genome has been sequenced, have gene
  networks and differential equations of how one
  gene influences another over time.
• Need array of equations specifying how gene A
  changes with respect to gene B

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Features

• Evaluates over long period of time until steady
  state is reached within the cell
• Determine relative levels of proteins within a
  cell
• Explicit solver
• If it is known how much energy is being consumed
  from these genes undergoing given reactions
• Implicit solver
• If gene X doubles expression, how are all other
  genes affected?
• Can plot change in GeneY as GeneX changes

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More Features

• Bifurcation Analysis
• Chaos, multiple steady states may exist.
• Bifurcation pointspoints where a slight shift in
  one substance may cause drastic change in steady
  state
• Experimental data
• Fit your model to experimental data to try and
  find the best steady state.

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Problems

• It is now feasible to generate a complete
  metabolic model where complete genome data are
  available
• hmm
• Data available is not there at whole cell level.
• Even if all data is available, can we solve a
  6,000 x 6,000 matrix?
• Just using isolated pathways is this useful?

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Comparison between two systems

• Similarities
• Both use similar approaches to looking at the
  dynamics of a cell.
• Both make it possible to knock out genes
• Can make plots to observe changes
• Differences
• E-CELL starts from the ground up builds cell as
  things are discovered. Math. Sim. Assumes
  information is there
• E-CELL only useful for M. genetalium Can use
  Math. Sim for any organism and adjust based on
  experimental data.