Computational Systems Biology: An Introduction

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Computational Systems Biology An Introduction

• CS 6280 Lecture 1
• P.S. Thiagarajan

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Basic Info

• P.S. Thiagarajan
• COM1 03 32 Tel Ext. 67998
• thiagu_at_comp.nus.edu.sg
• www.comp.nus.edu.sg/thiagu
• Course web page
• www.comp.nus.edu.sg/cs6280
• We will be using the IVLE system extensively.

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Office Hours

• Send mail first and fix an appointment.

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Course Material

• Selected Parts of the text book
• Systems Biology in Practice E. Klipp, R.
  Herwig, A. Kowald, C. Wierling, H. Lehrach
  (Wiley)
• Selected Survey papers, book chapters.
• Lecture slides.
• Research Articles.

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Assignments

• Lab Assignments
• 2
• tool based (Cell Illustrator, COPASI, SimBio)
• Individual

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Term Papers

• Read a paper or more likely- a bunch of papers
  on a topic.
• Summarize in the form of a term paper.
• First assignment Common
• Second assignment
• More substantial
• Can be aligned to your interests

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Seminar(?)

• Give talk based on the second term paper.
• 25 5 minutes.

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Grading (Tentative)

• Lab assignments    40 (20 20)
• Term papers        50 (25 25)
• Seminar 10

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What is the Course About?

• Computational systems biology
• CS approaches to problems in systems biology.
• Systems biology
• Not just focus on individual components.
• genes, mRNAs, proteins, membranes, ligands .
• But study a system of such components and their
  interactions.

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The CS Approach

• View selected biological processes as dynamical
  systems.
• Model
• Simulate
• Analyze
• Predict
• Many research communities study dynamical systems
  

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What do we need ?

• Biology for computer scientists.
• Knowledge of some basic biological
  sub-systems/processes
• Overview of experimental techniques.
• Modeling, analysis and simulation techniques.
• Biologists as collaborators!

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Need-to-Know

• Modeling techniques.
• Mathematical
• Linear algebra, differential equations,
  probability theory, statistics, Boolean networks,
  Bayesian networks, stochastic processes
• CS-specific
• Automata, Petri nets, Hybrid functional Petri
  nets, hybrid automata,

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Current Status

• Metabolism
• Kinetics laws (models).
• Enzyme kinetics, law of mass action,
  Michelis-Menten kinetics
• Metabolic network models and analysis.

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Current Status

• Signal Transduction
• Receptor-ligand interactions
• Protein actors
• signaling dynamics
• Signaling pathways

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Current Status

• Other biological processes
• biological oscillations
• protein folding kinetics
• cell cycle
• Gene expression, regulation

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Current Status

• Modeling tools
• Cell Illustrator, COPASI, SimBio, ..

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What shall we do?

• Selected Research papers.
• To illustrate what CS-based techniques may have
  to offer.
• To critically examine what is missing.
• To discuss promising lines of research.

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Why Systems Biology?

• Biology has traditionally and extremely
  successfully!- focused on how individual parts
  of a cell work .
• Bio-chemistry of large and small molecules
• The structure of DNA and RNA
• Proteins, ligands,

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Why Systems Biology?

• But functionality of a system is determined
  crucially by the interactions of the parts.
• Many fundamental biological processes are
  dynamic.
• cell replication, differentiation
• Protein folding
• Metabolism
• Development (differentiation)

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Why Systems Biology?

• Advances in experimental technology are producing
  vast amounts of data.
• Which genes get expressed when in controlled
  conditions.
• Iteratively
• model, analyze, predict, validate
• Enter mathematical system models!

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What can CS offer?

• We know how to deal with complex systems.
• Hierarchy
• silicon realization of circuits, digital design,
  micro-architectures, assemble language,
  programming languages, GUIs,
• separation of concerns.
• concepts (models), techniques, tools at each
  layer and for connecting the layers.

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What can CS offer?

• Deal with other disciplines.
• Multi-media
• Control
• Manufacturing
• Communications
• Business!
• Using computing power via algorithms and data
  structures!
• Theory Experimentation Computing

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What can CS offer?

• Find the right level abstractions.
• approximations
• Handle distributed dynamics
• Deal with hybrid behaviors
• Develop efficient algorithmic techniques
• Build tools.

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What the Course is NOT about.

• We will not deal with
• Traditional Bio-Informatics topics
• data mining, sequence analysis,
• Computational aspects of structural biology
• Proteins structure, folding

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Contents

• Basic Biology
• You know this?!
• Bio-chemical networks
• The basics of chemical kinetics
• The three types of bio-chemical networks
• Gene networks
• Metabolic networks
• Signaling pathways

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Bio-Chemical Networks

• Many studies of biological sub-systems boil down
  to studying
• bio-pathways
• A network of bio-chemical reactions.

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The Role of Chemical Reactions
Bio-Chemical reactions
Metabolic pathways Signaling pathways Gene
regulatory networks
A network of Bio-Chemical reactions
Interacting networks of Bio-Chemical reactions
Cell functions
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Biopathways
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Gene Regulatory networks

• Boolean models
• Differential equations
• Bayesian networks.

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Metabolic pathways

• Petri nets
• Linear algebra
• Flux analysis

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Signaling Pathways

• Differential equations.
• Hybrid functional Petri nets
• Hybrid automata
• Stochastic models.
• Gillespies algorithm.

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Guest Lectures

• Mainly biologists
• Some of them will be our collaborators
• Members of my lab

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Our Research

• Signaling pathways
• AKT/MAPK pathway
• DNA damage/repair pathways
• Complementary pathway
• Liver fibrosis
• Parameter estimation techniques
• Stochastic approximations of dynamics.
• Two faculty members, 1 Post-doc, 4 PhD students
• Collaboration with faculty members in FoM, DBS,
  RCE on Mechanobiology
• www.comp.nus.edu.sg/rpsysbio

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Expected Outcomes

• Have a sound grasp of
• current modeling and simulation techniques
  (Signaling pathways)
• Reaction kinetics
• stochastic models and simulations

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Expected Outcomes

• Be aware of the limitations of current techniques
  and state of knowledge
• Be ready to undertake modeling and simulation
  work.

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Reaction Kinetics
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Basic Biology Sources

• Chapter 2 (Biology in a Nutshell) of the book
  Systems Biology in Practice by E. Klipp et.al.
• Chapter 1 (Molecular Biology for Computer
  Scientists) of the book Artificial Intelligence
  and Molecular Biology by Lawrence Hunter.
• Available online!
• Many other sources

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(Sub-)Goal

• Understand the molecular biology of eukaryotic
  cells.
• Cell the basic building block.
• Two major families Prokaryotes and Eukaryotes.
• Eukaryotes
• More evolved
• WE are made up of these types of cells.

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Cells

• In multi-cellular organisms
• Cells are differentiated.
• Different types of cells have different functions
  (and composition).
• Groups of cells for specific functionalities
• tissues.
• we have 14 different types of tissues.

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Major Classes of Bio-Molecules

• Carbohydrates
• Lipids
• Proteins
• Nucleic acids

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Proteins

• Many functions!
• Build up the cytoskeletal structure of the cell
  (the scaffolding)
• Responsible for cell movements (motility)
• Catalytic enzymes for bio-chemical reactions.
• Crucial for signal transductions.
• Control transcriptions and translation of genes
• Control degradation of other proteins.

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Proteins

• Proteins consist of one or more polypeptides.
• Peptide- a chain (sequence of amino acids).
• Polypeptide - a LONG chain of amino acids.
• The order of amino acids constituting a peptide
  is fundamental.
• Primary structure
• coded by genetic information

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Proteins

• 20 different amino acids
• A protein can have 50 4000 amino acids
  sequence. (50 1000 is the typical range)
• 201000 possible proteins!
• Actually, only a tiny fraction is found in nature.

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Nucleic Acids

• DNA (Deoxyribonucleic acid) molecules store
  genetic information.
• Present in all living organisms
• RNA (Ribonucleic acid) takes part in a large
  number of processes.
• Transferring hereditary information in the DNA to
  synthesize proteins.
• .

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The Central Dogma

• First enunciated by Francis Crick in 19581
• re-stated in a Nature paper published in 19702
• Three major classes of information-carrying
  biopolymers
• DNA, RNA, proteins
• Information encoded as sequences of molecules.

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The Central Dogma

• In principle there can be 9 types of transfers

Proteins
DNA
RNA
Proteins
DNA
RNA
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The Central Dogma

• The simple form of central dogma states

DNA
RNA
Proteins
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The Central Dogma

• Information cannot be transferred back from
  protein to either protein or nucleic acid.
• 'once information gets into protein, it can't
  flow back to nucleic acid.'

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Current Known Information Flows
Special flows occur in viruses (or labs!)
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Information Flows
(Replication)
DNA
DNA
(Transcription)
DNA
mRNA
(Translation)
mRNA
Proteins
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Mechanism of Cellular Functions

• Replication (of DNA)
• Transcription of RNA and Processing by splicing-
  to yield mRNA which migrates to the cytoplasm.
• Translation (by ribosomes) of the code carried by
  mRNA into proteins.

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                                      Legend
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Molecular Modifications

• A key aspect of implementing cellular functions
• Phosphorylation (Activation of proteins)
• Dephosphorylation (Deactivation of proteins)
• Methylation and acetylation (Gene silencing.
  Plays a role in cell differentiation)
• Cleavage (Cutting of genes and proteins. For
  degradation and apoptosis)
• Ubiquitination (Marking of proteins for further
  processing)

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Interaction roles of proteins

• Proteins have specific roles in the form of
  chemical interactions.
• Kinase (Catalyzes phosphorylation, thereby
  activating other proteins)
• Phosphatase (Catalyzes dephosphorylation)
• Transcriptional Co-factors

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Role of Bio-pathways

• Apoptosis
• The process of cell death
• Differentiation
• Cells getting specialized for specific functions
• Cell-cycle
• Growth and replication of cells
• Many others!

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Wnt Signaling Pathway

• Most studies on each of the two types of pathways
  (Signaling and GRN) done in isolation
• Wnt canonical pathway, starts with the binding of
  the Wnt ligand to Frz receptor
• Chain of chemical reactions occur, which results
  in the transcription factor ß-Catenin being
  translocated to the nucleus
• Cofactor with TCF/LEF to up-regulate the
  transcription of several genes

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Wnt Signaling Pathway (Canonical)
Degradation Complex form when GSK-3B binds and
phosphorylates APC and Axin
Cytoplasmic B-catenin will be phosphorylated by
the complex and gets marked for degradation
When Wnt ligand binds to Frz, Dsh is recruited to
the plasma membrane and gets activated
It will inhibit the formation of the degradation
complex
Cytoplasmic B-catenin can then translocate to
nucleus where it binds to co-factors Tcf and Lef
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Reaction Kinetics
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Sources

• Chapter 5 (Metabolism) of the book Systems
  Biology in Practice by E. Klipp et.al.
• Other related material to be uploaded.

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Bio-Chemical Reactions

• Bio-Chemical reaction
• A basic unit of biological processes.
• Convert molecules of one type into another
• Can be modeled at different levels of abstraction
  (time scales).
• Microscopic single molecules and their
  interactions
• Macroscopic Concentrations and rates (changes of
  concentration per time unit).

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Reactions

• Bio-chemical reaction
• Involves bio-molecules.
• Proteins, carbohydrates, lipids,
• Creation and transformation of bio-molecules.
• Control the flow of energy and materials through
  the cell.

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Kinetic Models of Reactions

• Reaction
• A chemical process resulting in inter-conversion
  of the reactants.
• motion of electrons cause chemical bonds to
  break and form.
• Reaction types
• Isomerization
• structural rearrangement (transform one isomer
  to another)
• no change in net atomic composition

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Reaction Types

• Direct combination or synthesis
• two or more chemical elements or compounds unite
  to form a more complex product.
• 2H2 O2 ? 2H2O
• Chemical decomposition
• a compound is decomposed into smaller compounds
• 2H2O ? 2H2 O2

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Reaction Types

• Single displacement or substitution
• an element being displaced out of a compound by a
  more reactive element
• 2Na 2HCl ? 2NaCl H2
• Double displacement
• two compounds in aqueous solution exchange
  elements or ions to form different compounds.
• NaCl AgNO3 ? NaNO3 AgCl

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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.

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Reaction Rates

• Influenced by
• Temperature
• Concentration
• Pressure
• Light
• Order (zero, first, second)
• catalyst

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Rate Laws

• Rate law
• An equation that relates the concentrations of
  the reactants to the rate.

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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.

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Mass action law
V1
S1 S2
2P
V2
V (V1) - (V2) k1. S1 S2 k2 P2
S1 (S2 is the concentration (Moles/litre)
of S1 (S2) k1 and k2 are the rate constants V1,
the rate of the forward reaction V2, the rate of
the backward reaction V, the net
rate Molecularity is 1 for each substrate
(reactant) of the forward reaction and 2 for the
backward reaction
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Mass-action Kinetics
k1
k2
E S
ES
E P
k -1
dS/dt -k1 ES k-1 (ES)
dES/dt k1 E.S (k-1 k2) ES
dE/dt -k1ES (k-1 k2) ES
dP/dt k2 ES
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Initial values chosen randomly
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Solving (simultaneous) differential equations
Remember the single (ordinary) linear
differential equation in one dimension dx / dt
2t has the solution x(t) t2 x(0) But
simultaneous ordinary differential equations in n
dimensions (large n!) are impossible to solve.
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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!

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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

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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

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Michaelis-Menton Kinetics

• A reversible formation of the Enzyme-Substrate
  complex ES
• Irreversible release of the product P from the
  enzyme.

This is for a single substrate no backward
reaction at least negligable if we focus on the
initial phase of the reaction. .
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Michaelis-Menten Kinetics
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Michaelis-Menton Kinetics
k1
k2
E S
ES
E P
k -1
Use mass action law to model each reaction.
dS/dt -k1 ES k-1 (ES)
dES/dt k1 E.S (k-1 k2) ES
dE/dt -k1ES (k-1 k2) ES
dP/dt k2 ES
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This is the rate at which P is being produced.
(1)
Assumption1 ES concentration changes much
more slowly than those of S and P
(quasi-steady-state) We can then write dES/dt
0.
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But, dES/dt k1 (E.S) (k-1 k2) ES From
dES/dt 0 it follows
This simplifies to
(2)

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Michaelis-Menton Kinetics
(1)
(2)
Define
(Michaelis constant)
(3)
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Assumption1 ES concentration changes much
more slowly than those of S and P
(quasi-steady-state) We can then write dES/dt
0. Assumption2 The total enzyme concentration
does not change with time. E0 E ES

E0 - initial concentration
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Michaelis-Menton Kinetics
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Michaelis-Menton Kinetics
(1)
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Michaelis-Menton Kinetics
Vmax is achieved when all of the enzyme (E0) is
substrate-bound. To achieve maximum rate, S gtgt
E0 at maximum rate,
E0 ES Thus, Vmax k2 ES k2E0

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Michaelis-Menton Kinetics
This is the Michaelis-Menten equation!
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Michaelis-Menton Kinetics
This is the Michaelis-Menten equation!
So what?
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Michaelis-Menton Kinetics
Consider the case v Vmax / 2
The KM of an enzyme is therefore the substrate
concentration at which the reaction occurs at
half of the maximum rate. 
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Michaelis-Menton Kinetics
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Michaelis-Menton Kinetics
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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.

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Michaelis-Menton Kinetics

• At KM, 50 of active sites are substrate bound. 
• At higher S a point is reached (at least
  theoretically) where all of the enzyme has
  substrate bound and is working flat out. 
• Adding more substrate will not increase the rate
  of the reaction, hence the levelling out observed
  in the graph. 

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Modeling Bio-Chemical networks

• Enzyme catalyzed reaction