The role of bioinformatics in systems biology

USP – University of Sao Paulo

DCM – Departamento de Computação e Matemática

LabPIB – Laboratório de Processamento de Informação Biológica http://labpib.fmrp.usp.br

Prof. Dr. Vênciorvencio@usp.br

Who are we ?

Who are we ?

Who are we ?

Where do we come from ?

Do you need bioinformatics ?

What is bioinformatics ?

"Computer science Bioinformatics is no more about

computers than astronomy is about telescopes"

-- E. Dijkstra (adapted)

What is bioinformatics ?

"Computer science Bioinformatics is no more about

computers than astronomy is about telescopes"

-- E. Dijkstra (adapted)

"Computer science Bioinformatics is to biology what calculus

is to physics"

-- H. Morowitz (adapted)

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Biological Motivation

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Biological Motivation

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

This presentation: mathematical complexity order

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Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Metabolomics

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Metabolomics

Silverstein, R. M.; Bassler, G. C.; Morrill, T. C.; Spectrometric identification of Organic Compounds, 7th ed., John Wiley & Sons, Inc.:New York, 2005.

Metabolomics

Silverstein, R. M.; Bassler, G. C.; Morrill, T. C.; Spectrometric identification of Organic Compounds, 7th ed., John Wiley & Sons, Inc.:New York, 2005.

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics

R.Silva, PhD candidate

Measured masses must be assigned to empirical formulas e. g. 194.06 Da → C8H10N4O2

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics

R.Silva, PhD candidate

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics

R.Silva, PhD candidate

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate

Rogers et al. (2008) NIPS Machine learning in computational biology workshop

Probabilistic Annotation – Bayesian Statistics

R.Silva, PhD candidate

Bayesian Statistics

P(A and B) = P(A) P(B|A)

probability of A and B happening at the same time... is equal to the probability of A happens … times ...the probability of B happens after we know that A already happened.

Bayesian Statistics

P(A and B) = P(B) P(A|B)

(or vice-versa, after all, they are logically equivalent if there is no natural order)

P(A and B) = P(A) P(B|A)

probability of A and B happening at the same time... is equal to the probability of A happens … times ...the probability of B happens after we know that A already happened.

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Probabilistic Annotation – adding biological information

R.Silva, PhD candidate

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Metabolomics

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Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Genomics

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Genomics

Genomics

D.Almeida-e-Silva, master candidate

Bayesian Networks: dealing with information uncertainty

Classic “wet grass” example (from: http://www.ra.cs.uni-tuebingen.de/software/JCell )

Bayesian Statistics

P(A and B) = P(A) P(B|A)

probability of A and B happening at the same time... is equal to the probability of A happens … times ...the probability of B happens after we know that A already happened.

Bayesian Statistics

P(A and B) = P(A) P(B|A)

P(A1 and A2and … and An) = P(A1 | A2and … and An) · · P(A2 | A3and … and An) · P(A2 | A3and … and An) … …

· P(An-2 | An-1 and An) · P(An-1 | An) · P(An)

Bayesian Networks

P(Z,X,Y,W,Q) = P(Q|Y) P(W|X,Y) P(Y|Z) P(X|Z) P(Z)

Bayesian Networks: dealing with information uncertainty

Classic “wet grass” example (from: http://www.ra.cs.uni-tuebingen.de/software/JCell )

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Genomics

D.Almeida-e-Silva, master candidate

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Genomics

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Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Transcriptomics

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Transcriptomics - microarray

B. Corrêa, master candidate

Transcriptomics - RNAseq

G. Felix, PhD candidate

Signal Segmentation

C. Bare, T Koide, et al BMC Bioinformatics (2010)

Signal Segmentation

D. Martinez, PhD candidate ; F. tenCaten, PhD candidate

Signal Segmentation

D. Martinez, PhD candidate ; F. tenCaten, PhD candidate

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Transcriptomics

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Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Work in Progress …

Joyce & Palsson, Nat Rev Mol Cell Biol (2006)

Work in Progress …

RNA-protein interaction

RNA-RNA interaction

structuromics

ncRNA

2011 !

LaBiSisMi & LabPIB

LaBiSisMi & LabPIB

USP – University of Sao Paulo

DCM – Departamento de Computação e Matemática

LabPIB – Laboratório de Processamento de Informação Biológica http://labpib.fmrp.usp.br

Prof. Dr. Vênciorvencio@usp.br

Thank you for your attention!

USP – University of Sao Paulo

DCM – Departamento de Computação e Matemática

LabPIB – Laboratório de Processamento de Informação Biológica http://labpib.fmrp.usp.br

Prof. Dr. Vênciorvencio@usp.br

“ In the strict formulation of the law of causality -- if we know

the present, we can calculate the future -- it is not the

conclusion that is wrong but the premise. ”

-- W. Heisenberg