Academic Speakers
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Prof. Brian Ingalls:
“Control Theoretic Approaches to Systems Biology“
Department of Applied Mathematics,
University of Waterloo,
Canada
( View Abstract )
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Prof. Elling W. Jacobsen:
“The role of feedback in intracellular networks“
Automatic Control Lab,
KTH Stockholm,
Sweden
( View Abstract )
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Prof. Roy Kishony:
“From epistasis information to biological function “
Bauer Center for Genomics Research,
Harvard University,
Cambridge, MA, USA
( View Abstract )
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Prof. Lucila Ohno-Machado:
“Predictive Models in the Diagnosis and Prognosis of
Disease“
Harvard Medical School,
Cambridge, MA
Brigham and Women's Hospital,
Boston, MA, USA
( View Abstract )
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Prof. Ilya Shmulevich:
“Modeling and Inference of Genetic Networks: Computational and
Experimental Approaches“
Institute for Systems Biology,
Seattle, WA, USA
Department of Bioengineering,
University of Washington,
Seattle, WA, USA
( View Abstract )
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Prof. Hamid Bolouri:
“Computational recontruction of transcriptional regulatory networks:
theory & biological examples“
Institute for Systems Biology, Seattle, WA, USA
( View Abstract )
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Dr. Michael Stumpf:
“Statistical and Evolutionary Inferences from Biological Networks“
Centre for Bioinformatics,
Imperial College London, UK
( View Abstract )
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Prof. Hans Westerhoff:
“Bottom-up Systems Biology“
Institute of Molecular Cell Biology, Vrije Universiteit, Amsterdam, The Netherlands
Manchester Centre for Integrative Systems Biology, UK
( View Abstract )
Industry Speakers
Abstracts
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Prof. Brian Ingalls:
“Control Theoretic Approaches to Systems Biology“
This presentation will introduce aspects of feedback control theory
which have proved relevant in the investigation of biochemical
networks. These analytic tools, which were developed to aid in the
design and analysis of engineered self-regulating systems, provide a
valuable framework for the reverse-engineering of networks of regulatory
interactions within the cell. Topics covered will include the roles of
negative and positive feedback, the special features of integral
control, and the analytic framework provided by linearization and the
frequency response. These theoretical foundations will be illustrated
by specific biological applications.
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Prof. Elling W. Jacobsen:
“The role of feedback in intracellular networks“
The presentation will give a control theoretic perspective on the role of
feedback in intracellular networks. In the first part I will provide an
overview of some general properties of feedback systems, and discuss how
these are employed in the cell to generate functions and provide properties
such as robustness. In the second part I will introduce tools that can be
used to identify and analyze key feedback mechanisms embedded in complex
biochemical networks. Relevant biological applications will be used
for illustration throughout.
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Prof. Roy Kishony:
“From epistasis information to biological function“
Complex biological functions are encoded by networks of interacting genetic components.
Epistasis, describing the way multiple perturbations (mutations or drugs) in such networks
affect each other's phenotypic consequences, provides essential information for elucidating
the network functional architectures. I will describe a combined experimental-theoretical
approach to quantify epistatic interactions in bacteria and yeast and for using epistasis
information to identify functional gene modules and their system-level organization.
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Prof. Lucila Ohno-Machado:
“Predictive Models in the Diagnosis and Prognosis of Disease“
This presentation will focus on key considerations in building diagnostic
and prognostic prediction systems using biomarkers and clinical data. A
review of the most commonly used methods for model construction drawn from
the statistical and machine learning communities will be followed by a
comprehensive exposition on evaluation methods and an overview of deployment
strategies. Special emphasis will be given to understanding differences in
discrimination and calibration indices and how they impact the use of
predictive models.
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Prof. Ilya Shmulevich:
“Modeling and Inference of Genetic Networks: Computational and Experimental Approaches“
Cellular function and interaction with its microenvironment is governed by the
integrated behavior of regulatory networks of interacting biomolecules inside the cell.
Revealing the structure of these networks is of paramount importance for
understanding the nature of cellular behavior in health and disease. New technologies
are now making it possible to measure the activities of thousands of molecular species
inside the cell. From these measurements, taken under various conditions and time points, we
can make inferences about the structure of the underlying regulatory networks. Combining
diverse types of measurements and other data can significantly improve our ability to correctly
infer this structure. I will discuss computational and statistical approaches to model,
simulate, and infer genetic networks from measurement data. I will present several modeling
approaches, along with examples, and discuss the relationships among them.
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Prof. Hamid Bolouri:
“Computational recontruction of transcriptional regulatory networks“
In the first talk, I will review the theoretical and computational
framework for model building and analysis with emphasis on tools and
techniques developed by my group. In the second talk, I will take the
audience through examples of networks we have constructed in yeast,
sea urchin embryos, and mouse macrophages, emphasizing the different
needs of different biological systems.
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Dr. Michael Stumpf:
“Statistical and Evolutionary Inferences from Biological Networks“
Current protein interaction network data are notoriously noisy and incomplete: for most
species -- apart from S.cerevisiae -- only the interactions among relatively small sets of
proteins are known. As a result present network datasets only offer us partial insights
into the structure and functional organization of molecular interaction networks in most
species. Here we discuss the extent to which such missing information may affect functional
and evolutionary inferences in systems biology and statistical tools that allow us to assess
the properties of the true network. In particular we will show that it is possible to estimate
the size of the true whole-organism interactome from the present partial datasets. We present a powerful,
efficient and very reliable estimator and illustrate its performance using several published S.cerevisae
datasets. We obtain consistent and similar estimates (and associated confidence intervals) for these
datasets and estimate that the compelte yeast interactome will have approximately 24000-30000 interactions.
We then apply the same formalism to different datasets from P.falciparum, D.melanogaster, C.elegans and
H.sapiens to see if predicted interactome size reflects the complexity of these organisms. We obtain
interactome size estimates of approximately 19000, 70000,230000 and 680000 interactions, respectively,
and find good agreement between different datasets for the same species. Thus the human protein
interaction network appears to be approximately an order of magnitude larger or more complex than the
fruitfly protein interaction network. We show that our approach will yield reliable estimates for most
systematic high-throughput experiments. We conclude with a discussion of the implications of these
results for comparative and functional analyses in systems biology.
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Prof. Hans Westerhoff:
“Bottom-up Systems Biology“
A number of approaches of bottom-up Systems Biology will be demonstrated. One of these will be the silicon
cell approach of making computer replica of pathways in living cells. Drug targets will be identified by calculating
steps that control vital flux more in parasites than in their host. Then Systems Biology laws and principles will
be derived that address the control of fluxes and concentrations, both under steady state and under dynamic conditions.
Examples of their applications to signal transduction pathways will be given. Systems Biology should not only look at
transcriptome or metabolome but integrate these with proteome, genome, and function. Hierarchical Regulation Analysis
does this and enables one to quantify how much regulation proceeds at t each of these levels, adding up to a total
regulation of 100 %. Applying this to yeast starving for carbon or nitrogen, we found that limiting these studies to
transcriptomics would have made us miss most of the regulation and much of the excitement: Yeast here exhibits a
fascinating diversity in regulatory strategies, which we shall try to rationalize.
For a more comprehensive abstract click here.
