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Modeling and Analysis Techniques in Systems
Biology.
CS 6221 Lecture 2
P.S. Thiagarajan
Acknowledgment
• Many of the PDF images that appear in the slides to follow are taken from the text book “Systems Biology in Practice” by E. Klipp et.al.
The Role of Chemical Reactions
Interacting Bio-Chemical networks
Bio-Chemical reactions
A network of Bio-Chemical reactions
Cell functions
Metabolic pathways
Signaling pathways
Gene regulatory networks
3
The Role of Chemical Reactions
Interacting Bio-Chemical networks
Bio-Chemical reactions
A network of Bio-Chemical reactions
Cell functions
Metabolic pathways
Signaling pathways
Gene regulatory networks
4
Reaction kinetics
Rate Laws
• Rate law:– An equation that relates the concentrations of
the reactants to the rate.
• Differential equations are often used to describe these laws.
• Assumption: The reactants participating in the reactions are abundant.
5
Reaction Kinetics
• Kinetics:– Determine reaction rates
Fix reaction law and determine reaction rate constant Solve the equation capturing the dynamics.
• The reaction rate for a product or reactant in a particular reaction:– the amount (in moles or mass units) per unit
time per unit volume that is formed or removed.
6
Rate Laws
• Mass action law:– The reaction rate is proportional to the
probability of collision of the reactants– Proportional to the concentration of the
reactants to the power of their molecularities.
7
Mass action law
S1 + S2 PV
V = k. [S1] [S2]
[S1] is the concentration (Moles/ litre) of S1
[S2] is the concentration (Moles/ litre) of S
k is the rate constant
V, the rate of the reaction
8
Mass-action Kinetics
E + S ES E + P
k1
k 2
k3
9
10
• Assuming mass law kinetics we can write down a system of ordinary differential equations for the 6 species.
• But we don’t know how to solve systems of ordinary (non-linear) differential equations even for dimension 4!
• We must resort to numerical integration.
11
Given:
12
Initial values chosen “randomly”
13
Michaelis-Menton Kinetics
• Describes the rate of enzyme-mediated reactions in an amalgamated fashion:– Based on mass action law.– Subject to some assumptions
• Enzymes– Protein (bio-)catalysts
• Catalyst:– A substance that accelerates the rate of a
reaction without being used up.– The speed-up can be enormous!
14
Enzymes
• Substrate binds temporarily to the enzyme.– Lowers the activation energy needed for the reaction.
• The rate at which an enzyme works is influenced by:– concentration of the substrate– Temperature
beyond a certain point, the protein can get denatured Its 3 dimensional structure gets disrupted
15
Enzymes
• The rate at which an enzyme works is influenced by:– The presence of inhibitors
molecules that bind to the same site as the substrate (competitive)
prevents the substrate from binding
molecules that bind to some other site of the enzyme but reduces its catalytic power (non-competitive)
– pH (the concentration of hydrogen ions in a solution) affects the 3 dimensional shape
16
Michaelis-Menton Kinetics
i) A reversible formation of the Enzyme-Substrate complex ES
ii) Irreversible release of the product P from the enzyme.
This is for a single substrate; no backward reaction or negligible if we focus on the initial phase of the reaction.
E + S ES E + P
k1
k 2
k3
17
Michaelis-Menten Kinetics
18
Michaelis-Menton Kinetics
Use mass action law to model each reaction.
E + S ES E + P
k1
k 2
k3
19
(1)
Assumption1:
[ES] concentration changes much more slowly than those of [S] and [P] (quasi-steady-state)
We can then write:
This is the rate at which P is being produced.
20
(2)
This simplifies to:
21
Michaelis-Menton Kinetics
(1)
(2)
Define (Michaelis constant)
(3)
22
Assumption1:
[ES] concentration changes much more slowly than those of [S] and [P] (quasi-steady-state)
Assumption2: The total enzyme concentration does not change with time.
[E0] = [E] + [ES]
[E0] - initial concentration
23
Michaelis-Menton Kinetics
][][][ 0 ESEE
][])[]]([[ 0 ESKESES M
]][[][]][[ 0 SESESKES M
][][
]][[ 0 ESKS
ES
M
24
Michaelis-Menton Kinetics
(1)
][][
]][[ 0 ESKS
ES
M
][3 ESkv
MKS
ESkv
][
]][[ 03
25
Michaelis-Menton KineticsVmax is achieved when all of the enzyme (E0) is substrate-bound.
(assumption: [S] >> [E0])
at maximum rate,
Thus,
][][ 0EES
][][ 033max EkESkv
26
][3 ESkv
Michaelis-Menton Kinetics
This is the Michaelis-Menten equation!
MKS
ESkv
][
]][[ 03
][ 03max Ekv
MKS
Svv
][
][max
27
Michaelis-Menton Kinetics
This is the Michaelis-Menten equation!
MKS
ESkv
][
]][[ 03
][ 03max Ekv
MKS
Svv
][
][max
So what?
28
Michaelis-Menton Kinetics
Consider the case:
The KM of an enzyme is therefore the substrate concentration at which the reaction occurs at half of the maximum rate.
MKS
Svv
][
][max
MKS
Svv
][
][
2maxmax
][2][ SKS M ][SKM
2maxv
v
29
Michaelis-Menton Kinetics
30
Michaelis-Menton Kinetics
31
Michaelis-Menton Kinetics
• KM is an indicator of the affinity that an enzyme has for a given substrate, and hence the stability of the enzyme-substrate complex.
• At low [S], it is the availability of substrate that is the limiting factor.
• As more substrate is added there is a rapid increase in the initial rate of the reaction.
32
Curve Plotting
• This is not relevant anymore
• Good non-linear regression techniques and LARGE amounts of computing power are available.
Variations
• Reversible form of Michaelis-Menten.
More complicated equation but similar form.
E + S ES E + P
k
k
k
k
Variations
• Enzymes don’t merely accelerate reactions.
• They regulate metabolism:– Their production and degradation adapted to
current requirements of the cell.
• Enzyme’s effectiveness targeted by inhibitors and activators (effectors).
Variations
• Regulatory interactions between an enzyme and an inhibitor are characterized by:– How the enzyme binds the inhibitor I
EI, ESI or both
– Which complexes can release the product ES alone or ESI or both ES and ESI
General Inhibitory Scheme
Competitive Inhibition
Competitive Inhibition
S and I compete for the binding place
High S may out-compete I
Uncompetitive Inhibition
Inhibitor binds only to the ES complex.
Does not compete but inhibits by binding elsewhere and inhibiting.
S can’t out-compete I.
Other forms Inhibitions
• Non-competitive inhibition
• Mixed inhibition
• Partial inhibition
Hill Coefficients
• Suppose a dimeric (two identical sub-units linked together) protein has two identical binding sites.
• The binding to the first ligand (at the first site) can facilitate binding to the second ligand.– Cooperative binding.
• In general, the binding of a ligand to a macromolecule is often enhanced if there are already other ligands present on the same macromolecule
• The degree of cooperation is indicated by the Hill coefficient.
Hill Coefficients
• A Hill coefficient of 1 indicates completely independent binding.– Independent of whether or not additional
ligands are already bound.
• A coefficient > 1 indicates cooperative binding.– Oxygen binding to hemoglobin:
Hill coefficient of 2.8 – 3.0
Hill’s equationHill equation
θ - fraction of ligand binding sites filled
[L] - ligand concentration
KM - ligand concentration producing half occupation (ligand concentration occupying half of the binding sites)
n - Hill coefficient, describing cooperativity
Sigmoidal Plots
Summary
• A bio-chemical reaction is governed by a kinetic law.– Mass law, Michalis-Menten, Hill equation,…
• Different laws apply under different regimes.
• Each law leads to an ODE model of the reaction kinetics.– Often, with an unknown constant of
proportionality. (rate constant)
Metabolic networks: Stoichiometric network analysis
Biopathways
Metabolic Pathways
• Cells require energy and material:– To grow and reproduce– Many other processes
• Metabolism:– Acquire energy and use it to grow and build
new cells
• Highly organized process• Involves thousands of reactions catalyzed
by enzymes.
Metabolic Pathways
• Two types of reactions:– Catabolic: break down complex molecules to
acquire energy and produce building blocks. breakdown of food in cellular respiration
– Anabolic: construct complex compounds from simpler building blocks by expending energy.
The Glycolysis Metabolic Pathway
The Glycolysis Metabolic Pathway
• The individual nodes are the molecule types.
• Arrows depict chemical reaction. They are labeled with the enzymes that catalyze them.
• ATP and ADP play important roles.
The Glycolysis Metabolic Pathway
• ADP: Adenosine Diphosphate
• ATP: Adenosine Triphosphate
• Both nucleotides
• ADP ---> ATP – Energy storage (catabolic)
• ATP ---> ADP – Energy release (anabolic)
Metabolic Networks
• Basic constituents:– The substances with their concentrations– The (chain of) reactions and transport
processes. that change these concentrations
– Reactions are usually catalyzed by enzymes– Transport carried out by transport proteins or
pores.
Stoichiometric Network Analysis
• Mainly used for studying metabolic networks. Properties studied:– Network consistency; blocked reactions and
missing network elements– Functional pathways and cycles: “non-
intutive” routes between in inputs and ouputs in complex networks; futile cycles that consume energy; inconsistent cycle consuming no energy
Stoichiometric Network Analysis
• Properties studied:– Optimal pathways, sub-optimal pathways,
maximal yields et. – Very useful for bio-tech applications.– Importance of single reactions for overall
system performance: knockout mutations Enzyme deficiencies
Stoichiometric Network Analysis
• Properties studied:– Correlated reactions: very likely co-regulated
• Sensitivity analysis
Metabolic Networks• Stoichiometric Coefficients:
– Reflect the proportion of substrate and product molecules in a reaction
S1 + S2 2PV1
V2
The stoichiometric coefficients : (-1, -1, 2)
Can also be
Can even be (1, 1, -2) if the reverse reaction
(V’ = V2 – V1) is being considered
V = V1 – V2
Metabolic Networks
• System equations
• n substances and r reactions.
•
– i = 1, 2, ….,n - metabolites– j = 1, 2, …,r - reactions
– cij = The stoichiometric coefficient of substrate (metabolite) i in the reaction j.
– Vj the rates (functions of time!)
Metabolic Networks
• Stoichiometric matrix– N
– N(i, j) = cij
An example
S1 2S2
S3
V1 V2
V4
By convention,
V1 (V2) is positive from left to right
V4 is positive from top to bottom
V3
An example
By convention,
V1 (V2) is positive from left to right
V3 is positive from top to bottom
1 -1 0 -1
S1 2S2
S3
V1
V2
V4
V3
S1
S2
S3
V1
V2 V3 V4
An example
By convention,
V1 (V2) is positive from left to right
V3 is positive from top to bottom
1 -1 0 -1
S1 2S2
S3
V1
V2
V4
V3
S1
S2
S3
V1
V2 V3 V4
?
An example
By convention,
V1 (V2) is positive from left to right
V3 is positive from top to bottom
1 -1 0 -1
S1 2S2
S3
V1
V2
V4
V3
S1
S2
S3
V1
V2 V3 V4
0 2 -1 0
An example
By convention,
V1 (V2) is positive from left to right
V3 is positive from top to bottom
1 -1 0 -1
S1 2S2
S3
V1
V2
V4
V3
S1
S2
S3
V1
V2 V3 V4
0 2 -1 0
P
An example
By convention,
V1 (V2) is positive from left to right
V3 is positive from top to bottom
1 -1 0 -1
S1 2S2
S3
V1
V2
V4
V3
S1
S2
S3
V1
V2 V3 V4
0 2 -1 0
0 0 0 1
Metabolic Networks
• S(t) are the functions we would like to know.– Need to solve simultaneous systems of
differential equations.– Rate constants are often unknown!– Initial values not always known
• Just compute the steady states.
Stoichiometric network analysis
Stoichiometric Matrix Analysis
• Uses only structural information.
• Can compute what are the admissible fluxes possible in steady state.– Flux: The total amount of a reactant passing
through (the pathway; through an enzyme;..) in unit time.
– We are ignoring a good deal of the dynamics.
Stoichiometric network analysis
Basic linear algebra
Basic linear algebra
77
In steady state, the reaction rate v8 will go to 0 !
Elementary Fluxes
• Elementary flux: a minimal set of non-zero-rate reactions – producing a steady state.– Respect the irreversibility (if any) of the
reactions
2
1
1
1
=
1
1
1
1
+
1
0
0
1
v2 is irreversible
Further techniques
• One can do similar analysis on NT
– Conserved quantities.
• Quasi steady state approximations
• Quasi equilibrium approximations
• Replace differential equations by algebraic equations.
• Sensitivity analysis (will deal with this in the context of signaling pathways)