computational biology: an overview
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Computational Biology: An overview. Shrish Tiwari CCMB, Hyderabad. Mathematics, Computers & Biology. “The book of nature is written in the language of mathematics…” - Galileo What about biology? Changing scenario due to the development of Biological sequence data Chaos theory - PowerPoint PPT PresentationTRANSCRIPT
Computational Biology: An overview
Shrish Tiwari
CCMB, Hyderabad
Mathematics, Computers & Biology
“The book of nature is written in the language of mathematics…”
- Galileo What about biology?
Changing scenario due to the development of Biological sequence data Chaos theory Game theory
Computer Applications in Biology
Pattern recognition
Pattern formation and characterisation
Structural modeling of bio-molecules
Modeling of macro-systems
Image processing
Data management and warehousing
Statistical analysis Next
Pattern recognition
Predicting protein-coding genes (GenScan)
Motif search (MotifScan, promoter search) Finding repeats (TRF, Reputer) Predicting secondary structure (PHDsec,
nnpredict) Classification of proteins (SCOP) Prediction of active/functional sites in
proteins (PDBsitescan)Back
Patterns in nature
Simulated Patterns
Back
Structural modeling
Protein folding: homology modeling, threading, ab initio methods
Protein interaction networks, biochemical pathways
Cellular membrane dynamics
Back
Macro-system modeling
Modeling of dynamics of organs like brain and heart
Modeling of environmental dynamics, interacting species
Modeling of population growth and expansion
Back
Image processing
Gridding of spots in the image Removing background intensity
(usually not uniform across the array)
Computing the ratio of intensities in case of two colour probes
Comparison of slides from different arrays Back
Computational Tools
Dynamic programming algorithm
Markov Model, Hidden Markov Model, Artificial Neural Network, Fourier Transform
Molecular dynamics, Monte Carlo, Genetic Algorithm simulations
Cellular Automata
Game theory
Statistical tools
Dynamic Programming
An optimisation tool that works on problems which can be broken down to sub-problems
Used widely in sequence alignment algorithms in bioinformatics
Other applications: speech, vocabulary, grammar recognition
Back
Pattern recognition tools
Markov model: state of system at time t depends on its state at time t-1, transition probabilities between states are defined. Example: gene finding
Artificial neural networks: attempt to simulate the learning process of real neural network system
Fourier transform: measure correlations between states at different time/space points Back
Optimisation tools
Molecular dynamics: apply Newton’s equation of motion to follow the dynamics of a system
Monte Carlo simulation: randomly hop from one state to another until you find the optimal state Back
Genetic algorithm: attempt to simulate evolutionary mechanism of mutations and recombination to find the optimal solution
Cellular Automata
Components: 1) a lattice, 2) finite number of states at each node, 3) rule defining the evolution of a state in time
Example: game of life _ 1) on a 2-d lattice each cell represents an individual, 2) states 0 (dead) or 1 (live), 3) a cell dies if it has less than 2 or more than 3 live neighbours, a dead cell becomes live if 3 of its neighbours are live
Simple “life” patterns
Still lives
Oscillator
Glider
Back
Game theory Game: 1) involves 2 or more players, 2)
one or more outcomes, 3) outcome depends on strategy adopted by each player
Components: 1) 2 or more players, 2) set of all possible actions, 3) information available to players before deciding on an action, 4) payoff consequences, 5) description of player’s preference over payoffs
Game theory: an example
Traffic as a game: The commuters are players Traffic rules define the set of possible actions
(including disobeying traffic rules) Payoff consequences: fined if you violate
traffic rules, you may suffer injury in accidents or die
Information available: Players preferences: safe driving, dangerous
driving etc. Back
Statistical tools
Expectation value computation to assess the significance of alignment
Clustering methods: UPGMA, WPGMA, k-means etc.
Assessing significance of genotype-phenotype association: chi-square test, Fisher’s exact test etc.
Chaos Theory: An Introduction
One of the behaviours of a non-linear dynamical system
Deterministic yet unpredictable!!
Sensitive to initial conditions/small perturbations
First discovered by Lorenz when he was simulating the weather dynamics using simplified hydro-dynamics model
The Lorenz attractor
Simplified model of convections in the atmosphere
dx / dt = a (y - x)
dy / dt = x (b - z) - y
dz / dt = xy - c z
a = 10, b = 28, c = 8/3
The Bernoulli shift
Map: f:x (2x mod 1), 0 ≤ x ≤ 1.
t = 0 1 2 3 4 5 6 7 8
x = .2 .4 .8 .6 .2 .4 .8 .6 .2
.21 .42 .84 .68 .36 .72 .44 .88 .76 Binary representation:
0.2: 0.001100110011…
0.21: 0.001101011100…
Chaotic dynamics: An example
Simplest system exhibiting chaos, the logistic map: xn+1 = rxn(1 – xn ), 0 < xn < 1
This simple equation exhibits a rich dynamical behaviour, ranging from stationary state to chaotic dynamics, as the parameter r varies from 0-4
This system models the population dynamics of a species whose generations do not overlap
Stationary state
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1 13 25 37 49 61 73 85 97
Time
Xn Series1
Acrobat Document
2-periodicity
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0.2
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1 13 25 37 49 61 73 85 97Time
Xn Series1
4-periodicity
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1 13 25 37 49 61 73 85 97
Time
Xn Series1
Chaos
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1 14 27 40 53 66 79 92
Time
Xn Series1
Logistic map bifurcation diagram
First return map
Plot of xn+1 against xn for discrete systems, and xt+T against xt for continuous dynamics, where T is some fixed interval
Return map of a periodic orbit is a finite set of points
Return map of a stochastic system a scatter of infinite number of points
Return map of a chaotic system an infinite number of points in a structure
Return map: Logistic map
Return map: Lorenz attractor
Controlling chaos
Different kinds of control are possible: Suppression of chaos, I.e. bring the system out
of chaotic behaviour into some regular dynamics: e.g. adaptive control
Remain in the chaotic dynamics, but force the system to remain in one of the unstable periodic orbits: e.g. OGY (Ott, Grebogi & Yorke) method
Sustain or enhance chaos: desirable for example in combustion where homogeneous mixing of gas and air improves the combustion
Synchronisation: confidential communication
Control of cardiac chaos
A. Garfinkel et al. applied the OGY method of control to arrest arrhythmia in a rabbit’s heart (Science 257, 1230-35 (1992) )
Arrhythmia was induced in the rabbit heart by injecting the animal with the drug ouabain
The first return map In-1 vs. In, the interbeat interval, identified periodic orbits with saddle instability
When the heart dynamics approached one of these points, small electrical pulses were used to force the system on the unstable periodic orbit
Prey-Predator Model
Simplest description of prey-predator interactions is given by the Lotka-Volterra equations:
dH/dt = rH – aHPdP/dt = bHP – mP
H: density of prey P: denstiy of predatorsr: intrinsic prey growth rate a: predation
rateb: reproduction rate of predator per prey
eatenm: predator mortality rate
Game theory Deals with situations involving:
2 or more players Choice of action depends on some strategy One or more outcomes Outcome depends on strategy adopted by all
players: strategic interaction Elements of a game:
Players Set of all possible actions Information available to players The payoff consequences A description of players’ preferences over
payoffs
Prisoners’ dilemma: An example
Players: 2 prisoners A and B Two possible actions for each prisoner:
Prisoner A: Confess, Don’t confess Prisoner B: Confess, Don’t confess
Prisoners choose simultaneously, without knowing what the other choses
Payoff quantified by years in prison: fewer years greater payoff
Outcomes: 1) both don’t confess: 1 year in prison for both, 2) 1 confesses other does not: the one who confesses is free, other gets 15 years, 3) both confess: both get 5 years
Prey-predator model with predators using hawk and dove tactics
P. Auger et al. recently studied a prey-predator model with the predators using a mix of hawk and dove strategies (Mathematical Sciences 177&178, 185-200 (2002) )
A classical Lotka-Volterra model was used to describe the prey-predator interaction
Predators use two behavioural tactics when they contest a prey with another predator: hawk or dove
Prey-predator model with predators using hawk and dove tactics
Assumptions: Gain depends on the prey density, which
modifies predator behaviour The prey-predator interaction acts at a
slow time scale The behavioural change of predator works
on fast time scale
Aim: effects of individual predator behaviour on the dynamics of the prey-predator system
Study carried out for different prey densities
Prey-predator model with predators using hawk and dove tactics
Conclusions: There is a relationship between
behaviour and prey density Aggressive (or hawk) behaviour prevails
in high prey density A mix of hawk and dove strategy
observed for low prey density A change of view: aggressive
behaviour is not advantageous when prey (resources) are rare and collaboration should be favoured
This is just the beginning …
Mathematics and computers are playing an increasingly important role in biology
We have just begun to scratch the surface of biological discoveries
The field is vast and largely untapped so we need young minds to be fascinated by these problems
References
A. Garfinkel, M.L. Spano, W.L. Ditto and J.N. Weiss “Controlling cardiac chaos” Science 257, 1230-1235 (1992).
P. Auger, R.B. de la Parra, S. Morand and E. Sanchez “A prey-predator model with predators using a hawk and dove tactics” Math. Biosci. 177&178, 185-200 (2002)