[virtual] cells
c o u r s e
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l a y o u t
introduction
molecular biology
biotechnology
bioMEMS
bioinformatics
bio-modeling
cells and e-cells
transcription and regulation
cell communication
neural networks
dna computing
fractals and patterns
the birds and the bees ….. and ants
i n t r o d u c t i o n
size
size
size
cells
Humans
60 trillion cells
320 cell types
the
cell
theory
 The Cell is the fundamental structural and functional unit
of living organisms
 The activity of an organism is dependent on both the
individual and collective activities of its cells
 Cell actions are determined and made possible by
specific subcellular structures
 Cells come from cells
the cellular basis of life
 The cell is the unit of life: it contains everything needed
to survive.
 Complex
 carbon, hydrogen, oxygen, nitrogen and traces of others
 organized into multiple structures = organelles
 each type needed for survival
 Many different shapes and sizes
 Neurons
 Blood cells
 ……
neuron
red blood cells
cell
components
3 basic parts
 Nucleus
 Cytoplasm
 all cellular contents between plasma membrane and
nucleus
 organelles = specialized internal structures
 Plasma membrane
cell
components
c e l l
c o m p o n e n t s
 lysosomes forms spindle fibres to separate chromosomes
during cell division
 golgi apparatus final packaging location for proteins
and lipids and distribution
 centriole modifies chemicals to make them functional;
secretes chemicals in tiny vesicles; stores chemicals;
may produce endoplasmic reticulum
 endoplasmic reticulum transports chemicals between
cells and within cells; - provides a large surface area for
the organization of chemical reactions and synthesis
http://www.tvdsb.on.ca/westmin/science/sbi3a1/Cells/cells.htm
lipid
layer
lipid
layer
lipid
layer
cytoplasm
Organelles to know
 Mitochondria
 Ribosomes
 Rough endoplasmic reticulum = Rough ER
 Smooth endoplasmic reticulum = Smooth ER
 Golgi apparatus
 Lysosomes
 Peroxisomes
 Nucleus
 Nucleoli
nucleus
 cell’s control center
 usually visible
 nuclear envelope
 double membrane
 nuclear pores in membrane allow passage of substances
between cytoplasm and nucleus
 contains the hereditary material = DNA
 carries instructions for making proteins
 determines cell structure, coordinates activities of the cell
nucleus
nucleolus
Nucleoli
 Darker staining, oval/spherical bodies within the nucleus
 Clusters of DNA, RNA, and protein (not membranebound)
 Site of ribosome assembly
cells
prokaryotic vs eukaryotic
 Single cells but can be
filamentous
 Small streamlined genomes
 No complex organelles
 Fast cell cycle
 No membrane defined nucleus
 No large visible chromosomes
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Membrane defined nucleus
Complex cytoplasmic organelles
Slow cell cycle
Complex development
Large genome with introns
Multiple chromosomes
prokaryotic cell
prokaryotic cell
eukaryotic
cell
eukaryotic
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cell
Rough endoplasmic reticulum-site of secreted protein synthesis
Smooth ER-site of fatty acid synthesis
Ribosomes-site of protein synthesis
Golgi apparatus- site of modification and sorting of secreted
proteins
Lysosomes-recycling of polymers and organelles
Nucleus-double
membrane
structure
confining
the
chromosomes
Nucleolus-site of ribosomal RNA synthesis and assembly of
ribosomes
Peroxisome-site of fatty acid and amino acid degradation
Flagella/Cilia- involved in motility
Mitochondria-site of oxidative phosphorylation
Chloroplast-site of photosynthesis
Intermediate filaments- involved in cytoskeleton structure
eukaryotic
cell
eukaryotic
cell
plant
vs
animal
 Plant cells have chloroplasts
and perform photosynthesis
 Outermost barrier in plant cells is
the cell wall
 Outermost barrier in animal cells
is the plasma membrane
cells
p l a n t
5 µm
c e l l
p l a n t
1 µm
c e l l
p l a n t
c e l l
chloroplast
chloroplast
20 µm
mitochondria
mitochondria
 break large molecules into small molecules by inserting a
molecule of water into the chemical bond
 produces energy
mitochondria
cell evolution
cell evolution
cell evolution
T4
bacteriophage
tobacco mosaic virus
50 nm
adenovirus
50 nm
transport
plasma membrane: structure
plasma membrane
 Membrane Chemistry and Anatomy
 50-50 split in weight ratio: lipid/protein
 More lipid molecules than protein molecules because of
proteins’ larger sizes
 Membrane lipids
 Phospholipids 75%
 Glycolipids
 Cholesterol
5%
20%
structure
plasma membrane
 Determine the functions a cell can perform
 Composition varies widely among cell types
 Integral proteins – located within the membrane
 channels
 transporters
 receptors
 intracellular junctions
 enzymes
 cytoskeleton anchors
 cell identity markers
 peripheral proteins - located on either face of the
membrane
 A similar list of many functions
proteins
fluid
mosaic
model
 Dynamic “fluid” structure
 Constantly changing components
 molecular positions
 less often its composition
 individual molecules recycled
 The kinds and numbers of membrane molecules,
especially proteins, determine membrane function.
structure
fluid
mosaic
model
 Communication with other cells and tissues
 selective permeability - allows passage
substances, limits others
of
some
 dependent on:
 molecular size
 lipid solubility
 charge
 membranes impermeable to all charged
molecules
 Ions only move through membrane through
channels
 The presence of channels & transporters is very
specific
functions
passive
transport
 Moves materials across cell and organelle membranes
without expending cellular energy
 Simple Diffusion
 kinetic energy is everywhere - allows mixing or diffusion
 diffusion requires a concentration gradient
 high concentration in one area, lower
concentration in another
 if areas continuous (connected) particles move
with (down) the concentration gradient
 eventually
reaches
equal
concentration
everywhere - equilibrium
passive
transport
simple
diffusion
Diffusion through the plasma membrane
 selective permeability
 water and lipid-soluble molecules move freely through
membrane
 small non-lipid-soluble substances may move through
specific channels
simple
diffusion
factors affecting diffusion
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increased temperature increases diffusion rate
greater concentration gradients increase diffusion rate
larger surface area increases diffusion rate
smaller particle sizes increase diffusion rate
time - diffusion decreases as concentrations equalize
osmosis
 Osmosis is the movement of water from an area of
higher [H2O] to an area of lower [H2O] concentration
 water moves with (down) its concentration gradient
 osmotic pressure – the net pressure effect of individual
particles in solution
 hydrostatic pressure = fluid pressure is created by osmosis
osmosis
solution tonicity
 isotonic solution
 cells in a solution where [ ] of solutes is same inside/outside
cells
 no net movement of water, e.g., “normal” saline solution
 hypertonic solution
 cells in a solution with an increased [ ] of solutes
 water moves out of cells, cells shrink, crenation
 hypotonic solution
 cells in a solution with a decreased [ ] of solutes
 water moves into cells, cells swell, may rupture
facilitated diffusion
some substances too large to diffuse need help crossing
 integral proteins move substances into cells – with (down)
the concentration gradient
 passive - no cellular energy required
 may be regulated by hormones
example insulin will increase cellular glucose uptake
facilitated diffusion
active
transport
 some substances cannot move passively
 they may be too big
 they may have the wrong charge
 they must be moved against concentration gradient
 energy must be expended for active processes (they
require the energy derived from splitting ATP = energy of
hydrolysis)
active
transport
 uses ATP hydrolysis to power transport
 many substances move by primary active transport: Na+,
K+, H+, Ca++, I-, Cl-, amino acids, monosaccharides, etc.
 two substances may be coupled for secondary active
transport = co-transport = facilitated transport
 Symport/Symporter – transports substances in the same
direction
 Antiport/Antiporter – transports substances in the opposite
direction
primary active transport
 uses ATP hydrolysis energy
directly to move substances
 ATP
is hydrolyzed to move
specific ions
 without ATP, the pump does
not work
 Na+/ K+ ATPase
 pumps 3 Na+ out / 2 K+ in each
cycle
 because Na+ & K+ always leaks
across the membrane, the
pump is always working
secondary active transport
 uses ATP energy indirectly to move substances
 uses the concentration gradient from a primary active
transporter for transport (like a water mill grinding corn)
 as Na+ leaks back into cell, bound to the symport, the
symport binds and drags the glucose inside with Na+
secondary active transport
resting membrane potential
 Generating/maintaining a resting membrane potential
 all cells are polarized
 negatively charged inside
 positively charged outside
 Na+/K+ ATPase creates the unequal charge distribution
 Na+ tends to diffuse in on its own
 K+ tends to diffuse out on its own
 the sodium-potassium pump transports 3 Na+ out
& 2 K+ in each cycle
 this creates the charge differential
 Electrochemical gradient
 the net effect of all charged ions on either side
of the membrane
resting membrane potential
cell-environment interactions
Membrane Receptors
 contact signaling - identifying
neighbor cells
 electrical signaling - channels
responding to voltage changes
(concentrations of charged ions)
 chemical signaling – various
signal
compounds:
neurotransmitters,
hormones,
and local hormones
cell
division
cell cycle
b u d di ng S . c e re vis ia e
cell cycle
control of the cell cycle
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19 to 24 hrs in mammalian cells
DNA replication
Cyclins
Kinases
Phosphorylation
m i t o s i s
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Cell cycle
DNA replication
Somatic cells
2n to 2n
No pairing
m i t o s i s
 Somatic cells
 DNA replication
 2n to 2n
m i t o s i s
m i t o s i s
m i t o s i s
human chromosomes
meiosis
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DNA replication
Homologous pairing
Recombination
Reduction division
Germline cells
meiosis
meiosis
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Germ line cells
DNA replication
Homologous pairing
Recombination
Reduction-division
 2n to n
meiosis
meiosis
meiosis
meiosis
meiosis
10 µm
labeling antibodies
cell
signaling
cell
signaling
cell differentiation
cell differentiation
Mendelian genetics
definitions
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Gene- a unit of heredity
Allele- a form of a gene
Dominance- one allele dominants or masks the other
Recessive- only seen/expressed in the homozygous state
Homozygous- having two of the same allele
Heterozygous- having two different alleles
Punetts square
Aa x Aa
A
A AA
a Aa
a
Aa
aa
monohybrid cross
monohybrid cross
monohybrid cross
dihybrid
cross
Mendel’s
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postulates
Heredity units in pairs
Dominance/recessive
Segregation of unit factors
Independent assortment of factors
virtual
cells
cell
simulation
virtual
cells
virtual cell
http://www.nrcam.uchc.edu/
applications
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Energy-Metabolism in E. coli
E-rice
Human Erythrocyte model
Circadian rhythms
E-neuron
Chemotaxis
the cell as a simulation target
self sustaining cell
motivation
Genome sequencing and functional analysis of complete
gene sets are producing a huge mass of molecular
information for a wide range of model organisms. Previous
work in genetic simulation has isolated well-characterized
pathways for detailed analysis, but methods for building
integrative models of the cell that incorporate gene
regulation, metabolism and signaling have not been
established until a few years ago.
motivation
 By attempting to understand the dynamics in living cells,
we should be able to predict consequences of changes
introduced into the cell.
 Possible consequences of such intervention include
changes in cell death, growth rate, and an increase or
decrease in the expression of specific genes.
 The development of sufficiently refined cell models
which allow predictions of such behavior would
complement the experimental efforts now being made
systematically to modify and engineer entire genomes.
motivation
 So, there was need in a software environment for
building integrative models based on gene sets, and
running simulations to conduct experiments in silico.
 Different approaches are used:
1. Ordinary differential equations (ODE),
2. π-calculus formal language,
3. Hybrid functional Petri nets (HFPN),
etc.
E-CELL
 E-CELL is a modeling and simulation environment for
simulation with GUI, based on ODE.
 Biochemical reactions are represented as a systems of
ODEs.
 For reactions which cannot be represented with ODEs, it
employs ad-hoc user defined C++ programs.
implementation of the E-CELL
 The E-CELL is a rule-based simulation system, written in
C++.
 The model consists of three lists, and is loaded at runtime.
1.The substance list defines all objects which make up
the cell and the culture medium.
2. The rule list defines all of the reactions which can take
place within the cell.
3. The system list defines functional structure of the cell
and its environment.
 The state of the cell is expressed as a list of concentration
values of all substances, pH and temperature.
 In a single time interval, each rule in the rule list is called
upon by the simulator engine to compute the change in
concentration of each substance.
implementation of the E-CELL
 E-CELL allows the assignment of any numerical
integration algorithm for each compartment of the cell
model, as well as definition of different time intervals (Δt).
 The system defaults to 1 ms for Δt and the user can
select between the first-order Euler or fourth-order
Runge-Kutta methods for the numerical integration in
each compartment.
user interfaces of the E-CELL
The E-CELL provides the following graphical interfaces:
1. The tracer interface allows to select substances or
reactions and observe dynamic changes.
2. The substance window shows the exact quantity of a
selected substance.
3. The reactor window displays the activity of a selected
reaction.
4. The gene map window provides the user with a means
of monitoring the expression level of all genes. It also
allows the user to knock out a selected gene or group of
genes.
user interfaces of the E-CELL
modeling the cell
 The main purpose is to develop a framework for
constructing simulatable cell models based on gene sets
derived from completed genomes.
1. A model of a hypothetical, minimal cell, based on the
gene set of Mycoplasma genitalium, the self-replicating
organism having the smallest known genome was
constructed. Its gene set was reduced to only those
genes that are required for what was defined as a
minimal cellular metabolism.
modeling the cell
 This model cell takes up glucose from the culture
medium using a phosphotransferase system, generates
ATP by catabolizing glucose to lactate through glycolysis
and fermentation, and exports lactate out of the cell.
 The model cell is 'self-supporting', but not capable of
proliferating; the cell does not have pathways for DNA
replication or the cell cycle.
 The cell model is basically constructed with three classes
of objects: Substances, Genes and reaction rules. The
reactions rules are internally represented as Reactor
objects.
modeling the cell
structure of the E-CELL system
substances
 All molecular species within the cell are defined as
Substances. The same molecule in different states (e.g.
phosphorylation) is defined as separate molecular
species and each spatial compartment of the model
retains a list of all of the substance objects it may contain.
genes
 DNA sequences in chromosomes are modeled as a
doubly linked list of Genomic Elements.
 The genome of the cell consists of 127 genes including
20 tRNA genes and two rRNA genes.
1. Out of the 127 genes, 120 have been identified in the
genome of M.genitalium.
2. The last of the seven E-CELL genes not found in
M.genitalium is glutamine-tRNA ligase, whose function is
probably substituted for by glutamate-tRNA ligase in
M.genitalium
tables
 The
number
of
genes
in
important pathways of the
hypothetical cell.
 Protein coding genes in the
hypothetical cell.
 Enzymes in the hypothetical cell.
 Small molecules in the
hypothetical cell.
http://web.sfc.keio.ac.jp/~mt/mt-lab/publications/Paper/ecell/graphics/btc007t03.gif
reaction
rules
 A typical reaction in a metabolic pathway is
transformation of one molecular species into another,
catalyzed by an enzyme which remains unaltered. For
example, the enzyme 6-phosphofructasokinase (EC
2.7.1.11) catalyzes the transformation of d-fructose 6phosphate (C00085) into d-fructose 1,6-biphosphate
(C00354), consuming ATP (C00002) and generating ADP
(C00008) and H+ (C00080) (E-CELL Substance IDs shown
in parentheses):
C00085 + C00002 -> C00354 + C00008 + C00080
[EC 2.7.1.11]
 Pathways can then be implemented by defining a series
of reactions which use the products of another reaction
as participating reactants.
transcription and translation
 Complex reactions such as transcription and translation
are modeled in detail as a series of reactions.
 The system does not have any regulatory factors, such as
repressors and enhancers, although they may be added
by the user.
transcription and translation
reaction kinetics
 A generalized chemical reaction:
where Sn is a concentration of the nth substance and
[nu]n is a stoichiometric coefficient for the substance.
 Most non-enzymatic reactions are first-order reactions.
Their velocities directly depend on concentrations of the
substrates and can be expressed as:
reaction kinetics
 Enzymatic reaction with a substrate and a product can
be expressed as the Michaelis-Menten equation:
where Vmax is the maximal velocity of the reaction and
Km is the Michaelis constant.
 For multiple substrates/products computation is a bit
more complicated.
virtual experiments
The E-CELL interfaces provide a means of conducting
experiments in silico. For example, we can 'starve' the cell
by draining glucose from the culture medium. The cell
would eventually 'die', running out of ATP. If glucose is
added back, it may or may not recover, depending on the
duration of starvation. We can also 'kill' the cell by knocking
out an essential gene for, for example, protein synthesis. The
cell would become unable to synthesize proteins, and all
enzymes would eventually disappear due to spontaneous
degradation.
virtual experiments
a trace of the quantity of ATP in the starving cell
virtual experiments
a trace of mRNA levels before and after starvation of the cell
genome
engineering
The main purpose of the E-CELL is to model the real cell of
M.genitalium, the organism having the smallest known
chromosome. Because of the small number of genes (470
proteins, 37 RNAs), M.genitalium is a prime candidate for
exhaustive functional (proteome) analysis. Because there
are still many genes whose functions are not yet known, it
will probably be necessary to hypothesize putative proteins
to complement missing metabolic functions, in order for the
model cell to work in silico.
genome
engineering
E-CELL is applicable for:
 Finding the optimal nutritional environment.
 Deciphering gene regulatory networks.
 Defining the minimal set of genes required for a selfreplicating .
concluding remarks on E-CELL
 Further task: to allow the cell proliferate.
 Further investigation goals:
1. Comparison of living cells to their computer models in
order to refine the system.
2. Defining minimal gene set in order to create such
living cells for future experiments.
B i o P S I
 BioPSI is a computer system, developed for the
representation and simulation of biochemical processes
 BioPSI is based on stochastic π-calculus.
 π-calculus is a formal language originally developed for
specifying concurrent computational systems.
M C e l l
 A General Monte Carlo Simulator of Cellular Microphysiology
… MCell now makes it possible to incorporate
high resolution ultrastructure into models of
ligand diffusion and signaling …
what is MCell ?
what is MCell ?
MCell uses
 Monte Carlo diffusion
 Chemical reaction algorithms in 3D
MCell simulates
 Release of ligands in solution
 Creation/destruction of ligands
 Ligand diffusion within spaces
 Chemical reactions undergone by ligand and
effector
what is MCell ?
what is MCell ?
main biochemical interactions
 3D diffusion of ligand molecules based on Brownian
motion
 the average net flux from one region of space to another
depends on molecules mobility depends on 3D con
centration gradient between the regions
computing
3D gradients
With Voxels
Assume well-mixed condition
Use PDEs for average net changes
PROS
 correct average system behavior
CONS
 too complex for realistic structures
 output has no direct stochastic information
computing
3D gradients
Monte Carlo approach
 Directly approximate the Brownian movements of the in
dividual ligand
 Chemical reaction rates are solution rate const
PROS
 events are considered on a molecule-by-molecule basis
 the simulation results include realistic stochastic noise
CONS
 complexity
how to run MCell ?
Simulate the system behavior
 Running the same computation with different seeds
 Averaging all the instances
Each instance has
 A pre-defined number of time steps
 Input data
how to run MCell ?
Input Data consists of
 one or more MDL scripts
 files describing elements of the simulation
spatial geometry
effector location
chemicals' repartitions
Output files
 resulting stochastic model
 visualization files