javier campos, jorge julvez´ university of...
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelos Formales en Bioinformatica
Javier Campos, Jorge JulvezUniversity of Zaragoza
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Outline
1 Systems Biology
2 Population Dynamics Example
3 Formal Models
4 Stochasticity
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Outline
1 Systems Biology
2 Population Dynamics Example
3 Formal Models
4 Stochasticity
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
What is systems biology?
• Systems biology is the study of all the elements in abiological system (all genes, mRNAs, proteins, etc) andtheir relationships one to another in response toperturbations.
• Systems approaches attempt to study the behaviour of allthe elements in a system and relate these behaviours tothe systems or emergent properties.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
Bioinformatics Basics
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
Systems Biology: Interaction in Networks
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Systems Biology: Interaction in Networks
34516.+*71
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
What is systems biology?
• ...systematic study of complex interactions in biologicalsystems, thus using a new perspective (integration insteadof reduction) to study them... one of the goals of systemsbiology is to discover new emergent properties (Wikipedia)
• Systems biology is the study of an organism, viewed as anintegrated and interacting network of genes, proteins andbiochemical reactions... systems biologists focus on all thecomponents and the interactions among them, all as partof one system (Institute for Systems Biology, Washington)
• To understand complex biological systems requires theintegration of experimental and computational research –in other words a systems biology approach (Kitano, 2002)
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
NetworksGene regulation
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Metabolic Pathway
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Signalling
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Protein-protein interaction
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
Metabolic Pathway
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Systems Biology is a multidisciplinar field
Which is the volume of a cow?
An informatics perspective: for cow=0; cow+1 end
Where is the cow???
A physicist perspective: Consider a spherical cow with negligiblemass…
Even E. coli is not that
spherical !!
• A chemist perspective: Dissolve the cow in H2SO4, weightthe result and measure the volume.
• Sometimes it is important not to destroy the cow.
• An engineering perspective: Immerse the cow in a tank ofwater and measure the volume.
• Some cows cannot swim. Water pressure might changevolume.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Systems Biology is a multidisciplinar field
Which is the volume of a cow?
An informatics perspective: for cow=0; cow+1 end
Where is the cow???
A physicist perspective: Consider a spherical cow with negligiblemass…
Even E. coli is not that
spherical !!
• A chemist perspective: Dissolve the cow in H2SO4, weightthe result and measure the volume.
• Sometimes it is important not to destroy the cow.
• An engineering perspective: Immerse the cow in a tank ofwater and measure the volume.
• Some cows cannot swim. Water pressure might changevolume.
11 / 51
![Page 13: Javier Campos, Jorge Julvez´ University of Zaragozawebdiis.unizar.es/asignaturas/SPN/material/1intro.pdfA physicist perspective : Consider a spherical cowwith negligible massÉ Even](https://reader034.vdocuments.mx/reader034/viewer/2022042804/5f51eca4dff5a81d4800cfe2/html5/thumbnails/13.jpg)
Systems Biology Population Dynamics Example Formal Models Stochasticity
Systems Biology is a multidisciplinar field
Which is the volume of a cow?
An informatics perspective: for cow=0; cow+1 end
Where is the cow???
A physicist perspective: Consider a spherical cow with negligiblemass…
Even E. coli is not that
spherical !!
• A mathematician perspective: Cut the cow into pieces andsum up the pieces.
• The total might not be equal to the sum of its parts→emergent properties.
• A physicist perspective: Consider a spherical cow withnegligible mass...
• Even E. coli is not that spherical
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Systems Biology is a multidisciplinar field
Which is the volume of a cow?
An informatics perspective: for cow=0; cow+1 end
Where is the cow???
A physicist perspective: Consider a spherical cow with negligiblemass…
Even E. coli is not that
spherical !!
• A mathematician perspective: Cut the cow into pieces andsum up the pieces.
• The total might not be equal to the sum of its parts→emergent properties.
• A physicist perspective: Consider a spherical cow withnegligible mass...
• Even E. coli is not that spherical
12 / 51
![Page 15: Javier Campos, Jorge Julvez´ University of Zaragozawebdiis.unizar.es/asignaturas/SPN/material/1intro.pdfA physicist perspective : Consider a spherical cowwith negligible massÉ Even](https://reader034.vdocuments.mx/reader034/viewer/2022042804/5f51eca4dff5a81d4800cfe2/html5/thumbnails/15.jpg)
Systems Biology Population Dynamics Example Formal Models Stochasticity
Outline
1 Systems Biology
2 Population Dynamics Example
3 Formal Models
4 Stochasticity
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
Mathematics to model the time evolution of a population
Theoretical immunology
Modelling a population
Deterministic birth process
Mathematics to model the time evolution of a population
Figure: A lot of penguins!
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
Mathematics to model the time evolution of a population
From penguins to rabbits..
Theoretical immunology
Modelling a population
First attemt at combining mathematics and biology
From penguins to rabbits
Figure: A lot of rabbits!
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Early attempts to make use of mathematics in biologyLeonardo de Pisa or Fibonacci (1202)• At month 0 there is a pair of rabbits (one female and one
male).• Every pair of rabbits (one female and one male) can mate
at the age of one month.• The female rabbit always produces a new pair of rabbits
(one female and one male) every month from the secondmonth on.
• As there is no death, all rabbits survive.What is the number of pairs of rabbits in month n?
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Rabbits population
Theoretical immunology
Modelling a population
First attemt at combining mathematics and biology
From penguins to rabbits
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Leonardo de Pisa or Fibonacci (1202)
• At month 0 there is a pair of rabbits (one female and one male).
• Every pair of rabbits (one female and one male) can mate at the age ofone month.
• The female rabbit always produces a new pair of rabbits (one femaleand one male) every month from the second month on.
• As there is no death, all rabbits survive.
The number of pairs in month n, Rn, satisfies:
Rn+1 = Rn + Rn−1
R0 = 1R1 = 1R2 = 1 + 1 = 2R3 = 2 + 1 = 3R4 = 3 + 2 = 5R5 = 5 + 3 = 8 18 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic modelling
• Let N(t) be the population of those penguins at time t .• N(t) is the number of individuals in the population at time t .• The change in the number of penguins in a small time
interval, from t to t + ∆t , is given by:
N(t + ∆) = N(t) + births − deaths + migration
• This equation is a conservation equation for the number ofindividuals of the population.
• The form of the various terms on the right-hand-siderequires essential feedback from biologists.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth processLet us assume that:• There are no death events in the population.• There are only birth events in the population.• The birth rate (number of births per unit of time), b, is the
same for all individuals of the population.• We have:
N(t + ∆t) = N(t) + births
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth process• Change in the population is due to birth events.• The births in the time interval [t , t + ∆t ] due to a single
individual is b∆t .
• The births in the time interval [t , t + ∆t ] due to allindividuals is N(t)b∆t .
• N(t+∆t) = N(t)+N(t)b∆t =⇒ N(t + ∆t)− N(t)∆t
= bN(t)
• For a very small time interval, ∆t → 0,
lim∆t→0
N(t + ∆t)− N(t)∆t
=dN(t)
dt= bN(t)
• This equation can be easily solved by integration.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth process• Change in the population is due to birth events.• The births in the time interval [t , t + ∆t ] due to a single
individual is b∆t .• The births in the time interval [t , t + ∆t ] due to all
individuals is N(t)b∆t .
• N(t+∆t) = N(t)+N(t)b∆t =⇒ N(t + ∆t)− N(t)∆t
= bN(t)
• For a very small time interval, ∆t → 0,
lim∆t→0
N(t + ∆t)− N(t)∆t
=dN(t)
dt= bN(t)
• This equation can be easily solved by integration.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth process• Change in the population is due to birth events.• The births in the time interval [t , t + ∆t ] due to a single
individual is b∆t .• The births in the time interval [t , t + ∆t ] due to all
individuals is N(t)b∆t .
• N(t+∆t) = N(t)+N(t)b∆t =⇒ N(t + ∆t)− N(t)∆t
= bN(t)
• For a very small time interval, ∆t → 0,
lim∆t→0
N(t + ∆t)− N(t)∆t
=dN(t)
dt= bN(t)
• This equation can be easily solved by integration.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth process• Change in the population is due to birth events.• The births in the time interval [t , t + ∆t ] due to a single
individual is b∆t .• The births in the time interval [t , t + ∆t ] due to all
individuals is N(t)b∆t .
• N(t+∆t) = N(t)+N(t)b∆t =⇒ N(t + ∆t)− N(t)∆t
= bN(t)
• For a very small time interval, ∆t → 0,
lim∆t→0
N(t + ∆t)− N(t)∆t
=dN(t)
dt= bN(t)
• This equation can be easily solved by integration.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth processN(t): Number of individuals at time tdN(t)
dt= bN(t)
• If the population at time t = t0 is given by N0, we have:
N(t) = N0eb(t−t0)
• In a deterministic birth process the population size ispredicted at time t with absolute certainty, once the initialsize N0 and birth rate b are given.
• The population size N(t) and time t are both continuousvariables (both take real values) and not discrete (takeinteger values).
• Is this a good mathematical population growth model?
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Deterministic birth processN(t): Number of individuals at time tdN(t)
dt= bN(t)
• If the population at time t = t0 is given by N0, we have:
N(t) = N0eb(t−t0)
• In a deterministic birth process the population size ispredicted at time t with absolute certainty, once the initialsize N0 and birth rate b are given.
• The population size N(t) and time t are both continuousvariables (both take real values) and not discrete (takeinteger values).
• Is this a good mathematical population growth model?22 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Stochastic birth process• Let Xt be the dicrete random variable that describes the
number of individuals of the population at time t .• The stochastic process that describes the population
satisfies:Xt ∈ {1,2, . . .} and t ∈ [0,+∞)
• Denote by pn(t) the probability that at time t the size of thepopulation is n, i.e., the probability that at time t there are nindividuals in the population:
pn(t) = Prob(Xt = n)
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Stochastic birth process
• Consider a small time interval [t , t + ∆t ]• How is Xt+∆t related to X• We have the following rules:
• There are no death events in the population.• There are birth events in the population: the probability that
a birth takes place in ∆t is b∆t .• The probability of more than one birth in a time interval ∆t
is negligible (no twin births allowed).
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Stochastic birth process
Theoretical immunology
Modelling a population
Stochastic birth process
Stochastic birth process II
1 2 3
· · · · · ·n ! 1 n n + 1
! The probability that a population of size n ! 1 increases to n in the timeinterval (t, t + !t) is b "!t " (n ! 1).
! The probability that a population of size n increases to n + 1 in the timeinterval (t, t + !t) is b "!t " n.
! If at time t the population has n individuals, the probability that no birthevent takes place in the time interval (t, t + !t) is 1 ! b "!t " n.
! Evolution equation for pn(t):
pn(t + !t) = (n ! 1) b !t pn!1(t) + (1 ! n b !t) pn(t) .
• The probability that a population of size n − 1 increases ton in the time interval (t , t + ∆t) is (n − 1)b∆t .
• The probability that a population of size n increases ton + 1 in the time interval (t , t + ∆t) is nb∆t .
• If at time t the population has n individuals, the probabilitythat no birth event takes place in the time interval(t , t + ∆t) is 1− nb∆t .
• Evolution equation for pn(t):
pn(t + ∆t) = (n − 1)b∆tpn−1(t) + (1− nb∆t)pn(t)
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Stochastic birth process
Theoretical immunology
Modelling a population
Stochastic birth process
Stochastic birth process II
1 2 3
· · · · · ·n ! 1 n n + 1
! The probability that a population of size n ! 1 increases to n in the timeinterval (t, t + !t) is b "!t " (n ! 1).
! The probability that a population of size n increases to n + 1 in the timeinterval (t, t + !t) is b "!t " n.
! If at time t the population has n individuals, the probability that no birthevent takes place in the time interval (t, t + !t) is 1 ! b "!t " n.
! Evolution equation for pn(t):
pn(t + !t) = (n ! 1) b !t pn!1(t) + (1 ! n b !t) pn(t) .
• The probability that a population of size n − 1 increases ton in the time interval (t , t + ∆t) is (n − 1)b∆t .
• The probability that a population of size n increases ton + 1 in the time interval (t , t + ∆t) is nb∆t .
• If at time t the population has n individuals, the probabilitythat no birth event takes place in the time interval(t , t + ∆t) is 1− nb∆t .
• Evolution equation for pn(t):
pn(t + ∆t) = (n − 1)b∆tpn−1(t) + (1− nb∆t)pn(t)25 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
T cell
Theoretical immunology
Immunology
T cell
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Cell division
Theoretical immunology
Immunology
Cell division
Birth event
! At time t there are n cells.
! During the time interval !t there is a single birth event.
! At time t + !t there are n + 1 cells.
Birth event:• At time t there are n cells.• During the time interval ∆t there is a single birth event.• At time t + ∆t there are n + 1 cells.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Cell death
Theoretical immunology
Immunology
Cell death
Death event
! At time t there are n cells.
! During the time interval !t there is a single death event.
! At time t + !t there are n ! 1 cells.
Death event:
• At time t there are n cells.• During the time interval ∆t there is a single death event.• At time t + ∆t there are n − 1 cells.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
Cell-cell interactions lead to events
Theoretical immunology
Immunology
Cell-cell interactions lead to events
Figure: T cell-dendritic cell interaction.29 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling the time evolution of a population
T cell and Tumour cell
Theoretical immunology
Immunology
T cell and tumour cell
Figure: T cell and tumour cell.30 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Outline
1 Systems Biology
2 Population Dynamics Example
3 Formal Models
4 Stochasticity
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Modelling: Design and construction of models of existingbiological systems, which explain observed properties andpredict the response to experimental interventions.
systems biology: modelling as formal knowledge representation
synthetic biology: modelling for system construction
biosystemnatural
biosystemsynthetic
observedbehaviour
predictedbehaviour
model(blueprint)
desiredbehaviour
design construction
verification verification
observedbehaviour
predictedbehaviour
wetlab
model-basedexperiment design
experiments
formalizingunderstanding
wetlab experiments
model(knowledge)
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Model:• Formal representation of the real
world.• Simplified abstract view of the
complex reality
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Why model?
• A model can generate new insights• A model can make testable predictions
• E.g., predict the effect of drugs on an organism• E.g., predict the effect on an inhibitor on a pathway
• A model can test conditions that may be difficult to study inthe laboratory
• A model can rule out particular explanations for anexperimental observation
• A model can help you identify what’s right and wrong withyour hypotheses
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Textbook view of the cell and reality
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Three D EM image of a pancreatic
Beta cell
Campbell, Reece & Mitchell (1998) Biology, 5th
Edition
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
In silico humans - spatial & temporal scalesIn silico humans-spatial & temporal scales
• 1 m person
• 1 mm electrical length scale of cardiac tissue
• 1 mm cardiac sarcomere spacing
• 1 nm pore diameter in a membrane protein
Range = 109
• 109 s (70 yrs) human lifetime
• 106 s (10 days) protein turnover
Requires a hierarchy of inter-related models
pathway
modelsODEs
stochastic
modelsPDEs (continuum models)gene reg.
networks
• 106 s (10 days) protein turnover
• 103 s (1 hour) digest food
• 1 s heart beat
• 1 ms ion channel HH gating
• 1 ms Brownian motion
Range = 1015
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Levels of abstraction!"#"$%&'(&)*%+,-./'0&
12&
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Models must be validated by experimental data: Simulationsmust be accurate representations of the real world.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Formal Models in Bioinformatics
A Framework for Modelling• Define all the components of the system• Systematically perturb and monitor components of the
system• Reconcile the experimentally observed responses with
those predicted by the model• Design and perform new perturbation experiments to
distinguish between multiple or competing modelhypotheses.
(Ideker, Galitski & Hood, 2001)
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Models should be:• Readable.• Unambiguous.• Analysable.• Executable.
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Suggestions:• Occam’s razor: Don’t overcomplicate things.• Einstein: Everything should be made as simple as
possible, but not simpler.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Models should be:• Readable.• Unambiguous.• Analysable.• Executable.
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Suggestions:• Occam’s razor: Don’t overcomplicate things.• Einstein: Everything should be made as simple as
possible, but not simpler.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Modelling Regimes
Continuous
SimulationAlgorithms
MolecularDynamics
OrdinaryDifferentialEquation
StochasticDifferentialEquation
Randomness
StateSpace
Discrete
Deterministic Stochastic
Stochastic
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Modelling Regimes• Discrete and stochastic: Small numbers of molecules.
Exact description via Stochastic Simulation Algorithm(SSA) - Gillespie. Large computational time.
• Continuous and stochastic: A bridge connecting discreteand continuous models. Described by SDEs ChemicalLangevin Equation.
• Continuous and deterministic: Law of Mass Action. TheReaction Rate equations. Described by ordinarydifferential equations. Not valid if molecular populations ofsome critical reactant species are small.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Modelling Regimes• Discrete and stochastic: Small numbers of molecules.
Exact description via Stochastic Simulation Algorithm(SSA) - Gillespie. Large computational time.
• Continuous and stochastic: A bridge connecting discreteand continuous models. Described by SDEs ChemicalLangevin Equation.
• Continuous and deterministic: Law of Mass Action. TheReaction Rate equations. Described by ordinarydifferential equations. Not valid if molecular populations ofsome critical reactant species are small.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Modelling Regimes• Discrete and stochastic: Small numbers of molecules.
Exact description via Stochastic Simulation Algorithm(SSA) - Gillespie. Large computational time.
• Continuous and stochastic: A bridge connecting discreteand continuous models. Described by SDEs ChemicalLangevin Equation.
• Continuous and deterministic: Law of Mass Action. TheReaction Rate equations. Described by ordinarydifferential equations. Not valid if molecular populations ofsome critical reactant species are small.
42 / 51
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Outline
1 Systems Biology
2 Population Dynamics Example
3 Formal Models
4 Stochasticity
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Biological evidence of noise• “Stochasticity is evident in all biological processes the
proliferation of both noise and noise reduction systems is ahallmark of organismal evolution” Federoff et al.(2002).
• “Transcription in higher eukaryotes occurs with a relatively lowfrequency in biologic time and is regulated in a probabilisticmanner” Hume (2000).
• “Gene regulation is a noisy business” Mcadams et al. (1999).
• “Initiation of gene transcription is a discrete process in whichindividual protein-coding genes in an off state can bestochastically switched on, resulting in sporadic pulses of mRNAproduction” Sano 2001.
• “It is essential to study individual cells and to measure the cell tocell variations in biological response, rather than averaging overcell populations” Zatorsky, Rosenfeld et al. 2006.
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling in systems biology
Origin of Stochasticity• Intrinsic noise due to small numbers of molecules (e.g.
mRNA, DNA loci, TFs).• Uncertainty of knowing when a reaction occurs and which
reaction it is.• Relative statistical uncertainty is inversely proportional to
the square root of the number of molecules.• Applies equally well to studying channel behaviour via the
concept of channel molecules.• Extrinsic noise due to (external) environmental effects
(extrinsic factors are: stage in cell cycle, number of RNAPor ribosomes, cellular environment).
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling formalisms
Markov chains• Based on the concept of state of the system• Solution techniques:
• Enumerative• Transient and steady-state analysis• Exact and approximate analysis
• Drawbacks:• Low abstraction level• Model size equals number of states of the system• Only in very particular cases aggregation techniques exist
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling formalisms
Queueing networks• High abstraction level
• The number of states characterizing the system growsexponentially on the model size.
• Solution techniques:• Enumerative (based on Markov chains)• Reduction/transformation-based• Structurally based (product-form solution, exact)• Transient and steady-state analysis• Exact, approximate and bounds
• Drawbacks:• Lack of synchronization primitive• Extensions exist but destroying analysis possibilities
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling formalisms
Stochastic Petri nets• Abstraction level similar to queueing networks• With synchronization primitive
• SPN =Petri nets+ timing interpretation=queueing networks+synchronizations
• Wide range of qualitative (logical properties) analysistechniques:
• Enumerative (based on Markov chains)• Reduction/transformation-based• Structurally based
• Petri nets as a formal modelling paradigm• a conceptual framework to obtain specific formalisms based
on common concepts and principles at different life-cyclephases
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Modelling formalisms
Stochastic Petri nets (cont.)• Analysis techniques:
• Exact: mainly enumerative (based on Markov chains)• Bounding techniques (structurally based)• Approximation techniques (reduction/transformation)
• Drawbacks:• Lack of a product-form solution for efficient exact analysis in
most cases
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Contents of the Course
1 Discrete and Continuous Markov chains2 Birth and Death Processes3 Stochastic Simulation4 Hidden Markov Chains5 Stochastic Petri nets
Course info:http://webdiis.unizar.es/asignaturas/SPN/
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Systems Biology Population Dynamics Example Formal Models Stochasticity
Acknowledgments
Much of the material in the course is based on the followingcourses:• Deterministic models in mathematical biology, Magic 042 - Lecture 1
Carmen Molina-Parıs, Department of Applied Mathematics, School ofMathematics, University of Leeds
• Una Introduccion a la Biologıa de SistemasRaul Guantes (UAM), Juan F. Poyatos (CNB)
• A conceptual framework for BioModel Engineering (Systems Biology,Synthetic Biology)Rainer Breitling, Groningen, NL; David Gilbert, Brunel, UK; MonikaHeiner, Cottbus, DE
• A Petri Net Perspective on Systems and Synthetic BiologyMonika Heiner, Brandenburg University of Technology Cottbus, DEDept. of CS
• Systems Biology: Stochastic models and SimulationKevin Burrage, Institute for Molecular Bioscience, The University ofQueensland, Australia.
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