Transcript file1 - Department of Computer Science

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
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?
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/010222/01022217.html
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(Δt2)] or
fourth-order Runge-Kutta [O(Δt5)] integration methods for
each compartment
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
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
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
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
Classes of Objects cont.
• Reaction Rules
– One substance turned into another via an enzyme
6-phosphofructasokinase
D-fructose 6 phosphate
D-fructose 1-6 bisphosphate
ATP
ADP + H+
C0085 + C00002
C00354 + C00008 + C00080
[EC 2.7.1.11]
– 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 aa’s
and nucleotides
Reaction Kinetics
Reactions are modeled from EcoCyc and KEGG
Non-enzymatic reactions:
J-1
v = k • Π [Si]vi
i
Enzymatic Reactions (Mechaelis-Menton):
Vmax • [S]
v=
[S] + Km
Also works for a number of substrates and products or
formation/degredation of molecular complexes
Virtual Experiments
ATP initially
increases
‘Starve’ cell by
decreasing glucose
Level of ATP
plummets: cell dies
Changes in mRNA levels upon drop of ATP due to
Glucose Deprivation
Applications
•
•
•
•
•
•
Optimization of culture systems
Minimal gene set
Discover new gene functions
Model more complex organisms
Genetic engineering
Drugs
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?
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
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
More Features
• Bifurcation Analysis
– Chaos, multiple steady states may exist.
– Bifurcation points—points 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.
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?
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.