Computational Systems Biology 1 6106, 5 cr

Computational Systems Biology 1
6106, 5 cr
Olli Yli-Harja
Tampere University of Technology
Institute of Signal Processing
Topics covered on CSB1
Introduction
Systems biology
Role of modeling in biosciences
Quantitative models
Discrete models
Measurement systems in biosciences
Array techniques
Models of measurement systems
Microscopy and image analysis
Data analysis
Microarray data
Supervised / Unsupervised learning
Biological databases and software
CSB1 – and other courses
Course homepage
SGN-6106 COMPUTATIONAL SYSTEMS BIOLOGY I.htm
Lecture and exercise schedule lectures.txt
Prerequisites
Basic vocabulary in biosciences ICSP_Program_2006-07.pdf
Basic skills in computation and signal processing SGN-6106.html
Continuation
CSB2 – Genomic data, models and data analysis in depth
CSB1 required
Some more computational skills required (e.g. statistics etc.)
Other course offerings at TUT/ISP
Computational biosciences
Genomic signal processing, SGN-6206 5cr
Computational models in complex systems, SGN-6467 5cr
Complex systems 1, SGN-6307
Graduate seminars
Seminar on Signal Processing for Systems Biology
Signal Processing Graduate Seminar IV
Other useful courses
Image processing
Pattern recognition
Statistical signal processing
Knowledge mining
First lecture, 6.2.2007
Introduction to the research environment:
TUT / Institute of Signal Processing
Introduction to the CSB research team at TUT/ISP:
Signal Processing for Systems Biology
Teaching provided by the research team
Curriculum in biotechnology
Curriculum in computer science
Research topics, examples
Signal Processing for Systems Biology
Olli Yli-Harja
Tampere University of Technology
Institute of Signal Processing
Research environment
Institute of Signal Processing (TUT/ISP)
research personnel 170, 11 professors
30-40% from abroad - also in faculty
topics:
audio, transforms, image processing, multimedia, medical imaging,
systems biology, cell phones, cellular signals, …
Academy of Finland Centre of Excellence, 1996-2000-2006-2011
Tampere International Centre of Signal Processing
Invites researchers to engage in research at the TUT/ISP
40 visits yearly since 1997
Catalyst of research and international connections
A truly international environment for research
What is systems biology?
Based on the availability of large scale measurement data
Knowledge of DNA & genes, microarray technology, single cell manipulation and
measurement …
Advancing the use of regular scientific research approaches in biology:
Mathematical models, Model-based prediction, …, control, …, design
Biology becoming part of physics! General laws in biology?
Interplay between models and experiments
Why is it necessary?
Amount of measurement data increases
Biological knowledge increases
Interpretation without tools is impossible
Problems
Prohibitive complexity
cell, tissue, organism, environment
Repeatability
Controllability of the experimental setup
How to obtain a comparable set of cells
for a repeat experiment
Huge engineering effort needed in data
integration
What has signal processing to offer
for systems biology?
Signal processing:
In the intersection of computer science, statistics, dynamic systems theory
Manipulation, storing, retrieval, analysis of measurement data
Systematic modeling of observations
Design of optimal algorithms based on mathematical model
Develop novel signal processing methods that can be applied in modern
systems biology
Building on fundamental research of signal processing
Inter-disciplinary collaboration with research partners
Main directions
Tool development
Quantitative models of biological systems
Modelling of novel biosystem measurement technologies
Image processing and analysis in an important role
Applied research in medicine
Data analysis
Basic research in theoretical biology
Discrete network models
Research team
(started in 2001)
Data Analysis (1+1)
Professor Jaakko Astola, Reija Autio
Computational Neuroscience (1+5)
Academy research fellow *Marja-Leena Linne, Tiina Manninen, Antti
Pettinen, Katri Hituri, Eeva Mäkiraatikka, Kalle Leinonen
Computational systems biology (1+2+9+6)
Professor Olli Yli-Harja, Dr. Harri Lähdesmäki, Miika Ahdesmäki,
Tommi Aho, Juha Kesseli, Antti Larjo, Antti Niemistö, Nikhil, Matti
Nykter, Antti Saarinen, *Jenni Seppälä, Olli-Pekka Smolander, KaisaLeena Taattola, Tomi Korpelainen, Antti Ylipää, Sakari Palokangas,
Virpi Kivinen, Timo Erkkilä
Image processing (1+4)
Lecturer Heikki Huttunen, Petri Hirvonen, Antti Lehmussola, Pekka
Ruusuvuori, Jyrki Selinummi
Teaching in computational bioscience
New major in Bio- and environmental engineering
in 2004:
Computational systems biology
for Biotechnology students
New major in Institute of Signal Processing
in 2005:
Computational systems biology
for Computer Science students
Joint projects and joint guidance of students
within Tampere area
Courses offered by the CSB research group
Old courses
Lecture course, Signal processing for systems biology, 40 students,
2003-2004
Seminars in microscopy arranged, 15-20 students, 2001-2005
Weekly research seminar, 20-30 students, since 2001
New courses in 2005-2006:
Introduction to computational systems biology, SGN-6056 5cr
Computational systems biology 1, SGN-6106 5cr
Computational systems biology 2, SGN-6156 5cr
Computational models in complex systems, SGN-6467 5cr
Complex systems 1, SGN-6307 5cr
Graduate seminars
Seminar on Signal Processing for Systems Biology, SGN-6906, 2-3 cr
Signal Processing Graduate Seminar IV, SGN-9406, 3-8 cr
International collaboration
Research visits in 2002-2007
(~ 178 person-months ~ 15 person-years)
Institute of Systems Biology, Seattle: Dr. Harri Lähdesmäki, Dr. Antti Niemistö, Dr. Matti Nykter, Antti Larjo, Jyrki
Selinummi, Miika Ahdesmäki (74m)
University of Texas, M. D. Anderson Cancer Center, Houston: Dr. Harri Lähdesmäki, Dr. Antti Niemistö, Dr. Matti
Nykter, Prof. Olli Yli-Harja, Dr. Daniel Nicorici (27m)
Institute for Biocomplexity and Informatics, University of Calgary: Dr. Pauli Rämö, Juha Kesseli, Tiina Manninen,
Antti Lehmussola, Andre Ribeiro (12m)
NHGRI, Washington & MIT Boston: Dr. Sampsa Hautaniemi (36m)
University of Jena, Germany: Tommi Aho (3m)
Texas A&M University: Pekka Ruusuvuori (3m)
University of Uppsala: Jenni Seppälä (2m)
University of Antwerp: Katri Hituri (3m)
University of York, UK: Kathryn Williams, X (6m)
*Visiting Tampere from University of California in Santa Barbara : John Berger (12m)
TICSP Workshop on Computational Systems
Biology held annually 2003-2006 WCSB06.htm
IEEE Workshop on Genomic Signal Processing in
June 2007 Gensips 2007.htm
Xochicalco, Mexico, 2006
Research directions
Discrete
models
Simulation
(quantitative
models)
Models and tools
for measurement
systems
Image processing
and analysis
Data analysis
and fusion
Simulation
Quantitative models of signalling and metabolic networks
Bioelectrical neuron models:
Linne et al., (2004) A Model Integrating the
Cerebellar Granule Neuron Excitability and Calcium
Signaling Pathways, Neurocomputing
Using stochastic differentian equations
Saarinen et al., (2006) Modeling single neuron
behavior using stochastic differential equations,
Neurocomputing
Manninen et al., (2006) Developing Itô stochastic
differential equation models for neuronal signal
transduction pathways, Computational Biology
and Chemistry
Evaluation of existing simulation tools:
***Pettinen et al., (2005) Simulation Tools for
Biochemical Networks: Evaluation of Performance
and Usability, Bioinformatics
PKC signalling network in mammal neurons
Ordinary and stochastic differential equations
Case studies in mammal neurons, yeast,
bacteria
Main collaborators
Ari Huovila, Institute of Medical
Technology, University of Tampere
Jaakko Puhakka, Institute of Environmental
Engineering and Biotechnology, Tampere
University of Technology
Microbial hydrogen production
Joint research project with Jaakko
Puhakka,
TUT
Bioand
environmental
engineering
Reactor simulation
Performing
experiments
r/y
Experimental
research cycle
Analyzing results
r/min
Computational
research cycle
Designing
experiments
r/min
Proposing new
hypotheses
Models of biosystem measurement systems
Modeling and inversion of the effects
of the measurement system:
Lähdesmäki et al., (2005) In
silico microdissection of
microarray data from
heterogeneous cell populations,
BMC Bioinformatics
***Lähdesmäki et al., (2003)
Estimation and Inversion of the
Effects of Cell Population
Asynchrony in Gene Expression
Time-Series, Signal Processing
Microarray model
Nykter et al., (2005) Simulation
of microarray data with realistic
characteristics, BMC
Bioinformatics
Main collaborators:
Wei Zhang, University of Texas
M. D. Anderson Cancer Center
Stuart Kauffman, Institute for
Biocomplexity and Informatics,
University of Calgary
Signal processing approach:
Systematic modeling of observations
Design of optimal algorithms based on
mathematical models
Validation of models and analysis tools
Integrated image analysis
Effects of Cell Population Asynchrony in Gene
Expression Time-Series
Gene expression is measured from a cell population
Cell population gradually loses its synchrony
This corresponds to a time-varying low-pass filtering
of the “true” gene expression time-series
Problem: Not all genes are synchronized
Lähdesmäki et al., (2003) Estimation and inversion
of the effects of cell population asynchrony in gene
expression time-series. Signal Processing
Subcellular Image Analysis
Analysis of dispersion of the Golgi apparatus after different treatments
(SH-SY5Y neuroblastoma cells)
Jyrki Selinummi, Antti Lehmussola, Jertta-Riina Sarkanen, Jonna Nykky, Tuula O. Jalonen, Olli Yli-Harja
“Automated Analysis of Golgi Apparatus Dispersion in Neuronal Cell Images”
Proc. 4th TICSP Workshop on Computational Systems Biology (WCSB 2006), Tampere, 2006.
Image processing and analysis for biomeasurements
Measurements based on imaging:
Niemistö et al., (2005) Robust quantification of
in vitro angiogenesis through image analysis,
IEEE Transactions on Medical Imaging
Niemistö et al., (2005) Analysis of angiogenesis
using in vitro experiments and stochastic
growth models, Physical Review E
***Selinummi et al.,(2005) Software for
quantification of labeled bacteria from digital
microscope images by automated image
analysis, Biotechniques
CellC-software, freely available
Quantitative measurements based on
microscope images of cell populations
Analysis and synthesis of images
Simulated of microscope images!?
Main collaborators
Wei Zhang, University of Texas,
M. D. Anderson Cancer Center
Jaakko Puhakka, Institute of Environmental
Engineering and Biotechnology, Tampere
University of Technology
Olli-Pekka Kallioniemi, Technical Research Centre
and University of Turku, Finland
v
Motivation for the Use of
Simulation in Validation
Xiaobo Zhou, Stephen T.C. Wong, ”Informatics challenges
of high-throughput microscopy” IEEE Signal Processing
Magazine, May 2006:
”..when we detect or track lots of cells, proteins, and neuron spines, how
can we validate the extracted results of automated analysis since it is
almost impossible to perform similar manual analysis in high-throughput
scale?”
”Manual methods can only be used to tens or at most up to hundreds of
images. They would make lots of counting errors when analyzing hundreds
or thousands of images… Thus, simulation of complex biological processes
and images becomes an urgent issue.”
”Should such simulation model exist, it would be invaluable in validating
HTS image analysis algorithms without relying on laborious and costly
manual validation”
Simulation of Images of Cell Populations
Motivation for simulation of measurement systems
Unlimited amounts of free data
Free of financial and time constraints
Total control of the experiments
Hypothesis testing
Education
Ground-truth information
Valuable information for benchmarking various analysis methods
Reduces requirements for preprocessing and quality control
Antti Lehmussola, Pekka Ruusuvuori, Jyrki Selinummi, Heikki Huttunen, Olli YliHarja “Computational framework for simulating fluorescence microscope
images with cell populations”
Manuscript submitted.
Antti Lehmussola, Jyrki Selinummi, Pekka Ruusuvuori, Antti Niemistö, Olli Yli-Harja
“Simulating fluorescent microscope images of cell populations”
Proceedings of the 27th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC'05), Shanghai, China, September 1-4, 2005,
pp. 3153-3156.
Simulation of Images of Cell Populations
Simulated images share properties of real
fluorescence microscope images
Example: real image (A) and synthetic image (B)
Realistic enough for validating e.g. image analysis
algorithms
Comparison of Cell Enumeration Algorithms
Cell enumeration software are compared
Five different clustering conditions
Each image has the same number of cells (1000)
Cells become more clustered Æ more overlapping
Explorative data analysis
Clustering and explorative analysis for biological data:
Lähdesmäki et al., (2004) Distinguishing Key Biological
Pathways Between Primary Breast Cancers and their
Lymph Node Metastases by Gene Function-based
Clustering Analysis, International Journal of
Oncology
D. Nicorici et al., (2006) Finding Large Domains of
Similarly Expressed Genes using MDL Principle, IEEE
Engineering in Medicine and Biology Magazine
Visualization, validation of results
***Nykter et al., (2006) Unsupervised analysis
uncovers changes in histopathologic diagnosis in
supervised genomic studies. Technology in Cancer
Research and Treatment
MDS
of
microarray
data
Labels from
clinical
database
Inter-disciplinary cancer research
Normalization, visualization
Main collaborators:
Wei Zhang, University of Texas,
M. D. Anderson Cancer Center
Olli-Pekka Kallioniemi, Technical Research
Centre and University of Turku, Finland
Jukka Partanen, Finnish blood service
Lauri Aaltonen, University of Helsinki
Discrete large-scale models of biosystems
Annealed Boolean dynamics:
Kesseli et al., (2005) On Spectral Techniques
in Analysis of Boolean Networks, Physica D
Kesseli et al., (2005) Tracking perturbations in
Boolean networks with spectral methods,
Physical Review E
Kesseli et al., (2006) Iterated maps for
annealed Boolean dynamics, Physical
Review E
Criticarity in gene regulatory networks
***Rämö et al., (2006) Perturbation
Avalanches and Criticality in Gene Regulatory
Networks, Journal of Theoretical Biology
Rämö et al., (2005) Stability of Functions in
Boolean Models of Gene Regulatory Networks,
Chaos
x
t +1
Order, chaos and criticality
in genetic regulatory networks
= f (x )
t
Complex systems
General laws in biology
New biological observables
Randomized network ensembles
Main collaborators:
Stuart Kauffman, Institute for Biocomplexity and
Informatics, University of Calgary
Ilya Shmulevich, Institute of Systems Biology, Seattle
Information theoretic approach to biology
Inference of Boolean networks from data:
Lähdesmäki et al., (2003) On Learning Gene
Regulatory Networks Under the Boolean Network
Model, Machine Learning
Lähdesmäki et al., Relationships between
probabilistic Boolean networks and dynamic
Bayesian networks as models of gene regulatory
networks, Signal Processing, accepted.
Information theoretic approach
Rämö et al., Information Propagation in Models of
Gene Regulatory Networks, Physica D,
submitted
***Nykter et al., I. Information flow in complex
networks and evolution: A universal approach,
submitted
Main collaborators:
Ilya Shmulevich, Institute of Systems Biology, Seattle
Stuart Kauffman, Institute for Biocomplexity and
Informatics, University of Calgary