Next talks
14 December, 9-11 a.m., Building 25, Room 201 (Carnot Building)
Joseph X. Zhou (Institute for Biocomplexity and Informatics, University of Calgary, Canada)
How to cure cancer without killing the tumour cells? -
Genetic mechanism of bidirectional spontaneous transition and population equilibrium of Breast cancer cells
Recent Talks
Dr. Stephan M. Feller, Biological Systems Architecture Group, Department of Oncology,
Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Oxford University
Complexities of human cancer cell signalling -
Molecular diversity of tumours and signal processing by large protein complexes
Abstract:
The heterogeneity of molecular lesions in tumours is arguably the greatest challenge in developing
more effective drug therapies for solid malignancies. High-throughput sequencing of tumour DNAs is
now a well-established method, but only a small percentage of the resulting data can be turned into
accessible information, so many of the key players that drive tumour development in individual
tumours remain unknown. Many new tumour drugs coming into clinical testing act on signalling proteins,
which are thus of particular interest. Proteomic, biochemical and in particular functional studies
are needed to learn more about these proteins and their behaviours. While this is still all but
impossible with tumour biopsy material, large panels of tumour cell lines derived from a single
tissue type are useful to gain some mechanistic insights into the highly diverse signalling networks
of cancer cells. First results of our studies focused on tyrosine kinases and their signalling
mediators in the world's largest panel of colorectal cancer lines will be presented.
Most intracellular signalling processes occur in large multi-protein complexes within a highly
crowded environment (endogenous protein concentrations are ca. 200 mg/ml protein). Therefore, the
free diffusion of signalling components that are part of these complexes is probably highly restricted.
Large multi-site docking (LMD) proteins, like the members of the Gab, IRS/Dok, p130Cas and FRS families,
serve as assembly platforms for signalling protein complexes, built from many components through
specific protein-protein interactions. These complexes are believed to function as molecular
'computing units' that integrate multiple signalling inputs to produce several well-coordinated biological
outputs (cell survival, proliferation, cell shape changes, migration, invasion etc.). Somewhat
surprisingly, the LMD proteins appear to be mostly intrinsically disordered, that is lacking classical
structural elements (alpha-helices, beta-sheets) and folded domains, according to current structural
prediction programs. Based upon these observations, as well as important experimental data from the
collaborating Schaper group and from our own work we have developed a model for co-translational
'compaction' of LMD proteins (the 'N-terminal folding nucleation hypothesis'), which would seem to
explain how molecular computation of multiple intracellular signals can be mechanistically achieved.
The predictions from this model can be experimentally tested, as will be discussed in the lecture.
Mirela Domijan, Warwick Systems Biology Centre at University of Warwick
Some observations on interaction graphs of mass-action reaction networks
Abstract:
Recently, there has been growing interest in using
graphical methods to analyse behaviour of reaction networks that are
described by systems of ordinary differential equations (ODEs).
Graphs have an enviable advantage that they can be used to study
models of large size and with parameter uncertainty. In this talk I
will focus on the interaction graph; a graph that is defined by the
signs of the Jacobian matrix entries. Its structures such as signed
circuits and the nucleus (or Hamiltonian hooping) have been linked to
a variety of network dynamics. I will talk about some of our recent
observations* about these structures and showcase how they further, or
in some cases do not further, our understanding of the underlying
dynamics.
Domijan M and Pecou E. On the interaction graphs of mass-action
reaction networks, in review for J. Math. Biol., 2011.
Prof. Nadav Skjøndal-Bar (Department Chemical Engineering, NTNU, Trondheim, Norway)
Gastrin regulated transcriptional network : Network Component Analysis
Abstract:
Gastrin is a hormone, mainly produced by G-cells in response to food,
and is under feedback regulation. Gastrin activates and stimulates processes in the stomach and gut,
including growth of specific cells in these tissues. Wrong regulation of Gastrin can result for
example in cancer tumours. The study of processes involved Gastrin can assist in a solution for
many cancer types. We aim at understanding the topology, the dynamics and the mechanisms of the
gastrin network using a large microarray dataset we generated. Many decomposition methods are
available to extract hidden regulatory signals from the high-throughput data sets, such as Principal
component analysis (PCA), Independent component analysis (ICA), Singular value decomposition method
(SVD). These methods can reduce the dimensionality of data but fails to extract the biologically
significant information. Network Component Analysis (NCA) makes use of available information on the
system from experiments and the extracted signals are biologically significant. Using the NCA we
manage the estimate kinetic data of many TF's involved in the process and characterize them according
to early-late response in gene expression. Through an extension of the NCA method, we manage to
constract a network topology of the key TFs in the process.
Prof. Martha Grover (Georgia Institute of Technology, Atlanta, USA)
Control of Macromolecular Assembly
Abstract:
Atoms and molecules interact dynamically via local forces, and these interaction rules can be
manipulated through macroscopic system inputs such as temperature, pressure, and electric field.
Moreover, the nature of the individual molecules can be designed to achieve a desired macromolecular
assembly of the entire system. Biological systems achieve great complexity and robustness via this
bottom-up molecular self-assembly, although human-designed systems are usually manufactured with a
top-down approach.
The molecular structure of a material strongly impacts its mechanical, electrical, and optical
properties, and ultimately the performance of the system in which it is incorporated. Often a perfectly
ordered crystalline structure is desired, but defects reduce performance from this ideal case. In other
systems the intended material structure is amorphous, but the details of the nanocrystalline ordering
and molecular orientation strongly impact the material properties. A material may exist in its
thermodynamic equilibrium structure, but often materials are locked into non-equilibrium meta-stable
configurations during their processing. Even though the perfect crystalline state may be the thermodynamic
equilibrium, the dynamics of nucleation and growth of crystalline domains during temperature annealing may
create distinct domains that intersect at grain boundaries. Dislocations and vacancies may also be locked
in during processing. Non-equilibrium structures vastly increase the space of possible structures, and
these dynamics can be intentionally exploited to achieve novel properties via time-varying process inputs.
Stochastic simulations provide a quantitative framework in which to predict the overall dynamic
organization of millions of atoms, based on local pair-wise interactions between individual atoms
or small molecules. The events included in these kinetic Monte Carlo simulations may be selected
using first-principles calculations, experimental measurements, or a combination of both. Two case
studies will be described in this presentation. The first example focuses on surface morphology evolution
during thin film deposition. Here the stochastic simulations provide the starting point to derive
reduced-order coarse-grained models, which are subsequently used in a dynamic optimization to control
the deposition process. In the second case study, the design of the local interaction rules between
particles is considered, such that a desired assembly can be achieved in minimum time. Markov chain
theory is employed to bound the convergence rate of the assembly process.
Martin Schuster, Ph.D., Associate Professor (Department of Microbiology, Oregon State University, USA)
Cooperation and cheating in bacterial quorum sensing
Abstract
PD Dr.-Ing. Niels Grabe (Universitätsklinikum Heidelberg)
Towards Modelling of Epidermal Skin Homeostasis
Abstract
Dr. Carito Guziolowski (Universitätsklinikum Heidelberg)
Analysis of large-scale biological networks with constraint based approaches
Abstract
Dr. Hans-Michael Kaltenbach (Computational Systems Biology Group at ETH Zürich)
Monotone decomposition of biochemical reaction networks
Joseph X. Zhou (Institute for Biocomplexity and Informatics, University of Calgary, Canada)
Cancer Attractor: A Systems Biology Understanding
And Differentiation Therapy Of Cancer
Abstract: Based on clone evolution theory of cancer, cancer will be initiated
if a cell receives multiple-hit mutations in at least five biological pathways of cancer
hallmarks. However, the discovery of cancer stem cells (CSC) seriously challenges this view.
It is proved, by xenograft experiments in both Leukemia and solid cancers, that cancer
cells are, like other cells[1], intrinsically heterogeneous and only small portion of
them can regenerate all cancer cell lineages. If cancer cells need multiple-hit oncogenetic
mutations to obtain stemness with higher proliferation rate and evolutional advantages over
normal cells, why large number of cancer cells do not have the infinite self-renew ability?
To solve this puzzle, we propose a cancer attractor hypothesis based on theory of complex
gene regulatory networks (GRN) that control cell fates and that the CSC is trapped in a
pathological cancer attractor state of GRN. Multiple-hit oncogenetic mutations only make
cells to lose the stability of development hierarchy (stem cells->progenitor
cells->differentiated cells) and gain more cellular plasticity. These cells do not
automatically gain the stemness. They need further intercellular signalling and environmental
cues to enter the cancer stem cell state as a meta-stable cancer attractor in GRN[2,3]. This
leads to a new approach to cancer drug development that departs from the existing paradigm of
identifying critical cancer or cancer stem cell specific pathways and blocking them with
target-selective drugs. Instead, we try to find small drug molecules which destroy the
stemness of cancer stem cell and decrease the propensity of cancer cells to enter the CSC
state. Simply put, instead of using target-selective drugs which may apply selection pressure
upon cancer and trigger drug resistance, we use a differentiation therapy to cure cancer by
reprogramming CSCs to non-dividing cells with much less selection pressure[4,5]. In past
five years, with a new multi-step combinatorial screening scheme of FDA-approved small drug
molecule library (JHCLL) on breast cancer line MCF7, we have already found 16 non-cytotoxic
drug molecules which have a high efficiency of reprogramming MCF7 to non-dividing states.
Our new paradigm for cancer drug discovery and preliminary results are present here.
1. Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S (2008) Transcriptome-wide noise controls
lineage choice in mammalian progenitor cells. Nature 453: 544-547.
doi:10.1038/nature06965
2. Huang S, Ernberg I, Kauffman S (2009) Cancer attractors: a systems view of tumors from a
gene network dynamics and developmental perspective. Seminars in cell & developmental biology 20: 869-76.
Available here.
Accessed 16 Jul 2010.
3. Huang S, Ingber DE (2007) A non-genetic basis for cancer progression and metastasis:
self-organizing attractors in cell regulatory networks. Breast disease 26: 27-54. Available
here.
4. S. Huang (2011) Systems biology of stem cells : three useful perspectives to help
overcome the paradigm of linear pathways. Philosophical Transactions of the Royal Society B
Biological Sciences Transactions Society, Royal. doi:10.1098/rstb.2011.0008
5. J. X. Zhou (2011) Understanding gene circuits at cell-fate branch points for rational
cell reprogramming . Trends in Genetics. Available here.
Prof. Dr. Fred Schaper (Chair for Systems Biology, University of Magdeburg)
More than JAKs and STATs: New insights into the regulation of interleukin-6 signal transduction
Abstract: Inflammation is a physiological response of the organism to cope with infections
by microrganisms and chemical, physical or thermal induced traumata. Interleukin-6 (IL-6) is a crucial
cytokine which regulates the outcome of inflammatory events. Disregulation of IL-6 signal transduction
is associated with many inflammatory as well as proliferative diseases.
IL-6 activates the JAK/STAT pathway but also the MAPK- and PI3K-cascade. In the past major focus was
on IL-6-induced STAT3 activation and its negative regulation. Recently, it became obvious that also
IL-6-induced MAPK activation is crucial for promoter activation of a set of IL-6-dependent genes.
An overview on IL-6 signal transduction will be presented. A special focus will be on the negative
regulation and the balance of the pathways initiated by IL-6. Additionally, cross-regulation of IL-6
signalling by other hormones will be discussed.
Dr. Ronny Straube (Max Planck Institute Magdeburg)
Bistable Network Motifs in Signal Transduction Networks
Dr. Jörg Schaber (University of Magdeburg)
Automated ensemble modeling with modelMaGe: technology and application
Dr. Utz-Uwe Haus (University of Magdeburg)
Discovering All Associations in Discrete Data Using Frequent Minimally Infrequent Attribute Sets
Abstract: Associating biological categories with measured or observed
attributes is a central challenge for discrete mathematics in life
sciences. We propose a new concept to formalize this question: Given
a binary matrix of objects and attributes, determine all attribute
sets characterizing object sets of cardinality t1 that do not
characterize any object set of size t2 > t1. We determine how many
such attribute sets exist, give an output-sensitive quasi-polynomial
time algorithm to determine them, and show that k-sum matrix
decompositions known from matroid theory are compatible with the
characterization. (joint work with Dr. Elke Eisenschmidt, University of Magdeburg)
Prof. Stefan Schuster, Department of Bioinformatics, Faculty of
Biology and Pharmacy, Friedrich Schiller University Jena
Theoretical Systems Biology in Biotechnology: Promises, success stories
and limitations
Abstract
Dr. Steffen Waldherr, Institut for Systems Theorie and Automatic Control, Universität Stuttgart
Parametric uncertainty analysis of biochemical signal transduction models
Abstract: Dynamical models of biochemical signal transduction pathways are often
affected by large uncertainties on the parameter values. Robustness
analysis is an efficient tool to quantify the effects of model
uncertainty on qualitative properties of the model. This talk addresses
in particular the robustness analysis problem for qualitative dynamical
behaviour in the pathway, such as sustained oscillations or bistability.
The level of parametric uncertainty not affecting the dynamical
behaviour is thereby quantified by a suitably defined robustness
measure. In the robustness analysis of biochemical networks, there are
several challenges which obstruct the direct application of control
engineering methods to this problem. These challenges include
nonlinearity of the equations, dependence of the steady state on
uncertain parameters, and the need to consider a nominally unstable
system. This talk presents a novel solution to the robustness analysis
problem, overcoming the mentioned challenges within a control
engineering point of view. To this end, parameter values yielding a
change in the dynamical behaviour are characterised via a feedback loop
breaking approach. Based on this approach, two methods are proposed: one
to compute robustness certificates, yielding a lower bound on the
robustness measure, and one to search for nearby bifurcations, yielding
an upper bound. To illustrate the proposed methods, an analysis of the
NF-kB pathway is presented. This pathway is a central player in the
mammalian immune system and of high biomedical relevance. The
uncertainty analysis yields novel biological insights into the
oscillatory behaviour of this pathway.
Björn Heynisch, University of Magdeburg
Influence of Host Cell Defense during Influenza Vaccine Production in MDCK Cells
Abstract: For cell culture-based influenza vaccine production virus yield optimization is of crucial
importance. In particular, with the recent threat of the new H1N1 pandemic, not only seasonal
vaccines but also pre-/pandemic vaccines have to be supplied in large quantities. In vivo
influenza replication is limited by the immune system, but for production cell lines the
impact of cellular defense mechanisms on virus yield is unknown. In influenza-infected adherent
Madin-Darby canine kidney (MDCK) cells the interferon (IFN) response and subsequent induction
of the antiviral state was monitored. Virus yield and host cell signaling intensity were
strain-dependent. By over-expression of viral antagonists IFN-signaling could be reduced
up to 90%. However, maximum virus titer determined by real-time PCR and HA-assay was not
altered significantly. Stimulation of the antiviral state by conditioned medium led to enhanced
IFN-signaling, which initially slowed down virus replication but had only minor effects on
final virus titers. Interestingly, minireplicon assays revealed that canine Mx proteins are
lacking the antiviral activity against influenza of their human or mouse counterparts. In
summary, for MDCK cell culture-based influenza virus production host cell defense mechanisms
seem to play only a minor role for final virus yields. Antiviral mechanisms of these epithelial
cells may slow down influenza replication, which in vivo gains time for the immune system to be
activated, but do not reduce maximum virus titers obtained in the bioprocess.
Dr. Birgit Schöberl, Merrimack Pharmaceuticals (Cambridge, US)
Applying engineering principles to the development of novel
cancer therapies
Abstract: Combining quantitative biology and computational modeling provides a
powerful toolkit to design novel therapies in a context dependent
manner. We will provide multiple examples where we translated the
insights gained from modeling and simulation into practice by
engineering and testing novel, antibody-based therapeutics in the
context of the computer simulations. Through this iterative process
between computational modeling and antibody engineering, we gain a
deeper understanding of the drug's mechanism of action which allows us
to design therapeutics with a specific tumor type in mind. This context
specific design can subsequently be translated into the clinic.
Dr. Joseph Xu Zhou, Centre for Information Services and High Performance Computing (ZIH), TU Dresden
Predicting pancreas cell fate decisions and reprogramming with a gene regulatory network model
Prof. Friedrich Srienc, Department of Chemical Engineering and
Materials Science, and BioTechnology Institute, University of Minnesota,
Minneapolis/St.Paul, MN
From Metabolic Pathway Analysis to a Theory of Evolution
Abstract: Complex metabolic networks can be decomposed into
a set of discrete fundamental pathways or elementary modes that support
cell function under the constraint of mass conservation. The identification
of these modes enables the analysis of the pathway capabilities and the
rational design of efficient networks. This analysis approach is of
immense value in biotechnology as metabolic networks can be engineered
on a completely rational basis. The analysis predictions have been
confirmed in several experimental systems related to biofuels production
and to the production of secondary metabolites. Moreover, the set of
discrete elementary modes in a network can be interpreted with the tools
of statistical thermodynamics. The usage probabilities of individual
elementary modes are expected to be distributed according to Boltzmann's
distribution law such that the rate of entropy production is maximized.
Adaptive evolution experiments support the idea that metabolic networks
evolve towards such state. Ultimately, evolution of metabolic networks
appears to be driven by forces that can be quantified by the distance of
the current metabolic state from the state of maximum entropy production
that represents the unbiased, most probable selection of fundamental
pathway choices.
Prof. David Fell, School of Life Sciences, Oxford Brookes University
Building and analyzing genome-scale metabolic networks
Abstract: My group has been building structural metabolic models from annotated genome
sequences for bacteria, such as Saccharopolyspora erythrea and Streptococcus agalactiae, and a plant,
Arabidopsis thaliana, and further models are under construction. By a structural metabolic model, we
mean a list of connected, stoichiometrically-balanced reactions catalyzed by the complement of enzymes
considered to be encoded in the genome. Analysis of the model then consists of mathematical and
computational operations on the stoichiometry matrix derived from the reaction list, with the assumption
that all internal metabolites are at steady state as nutrients are converted into end products such as
biomass. The techniques include, but are not limited to, linear programming (also referred to as Flux
Balance Analysis in this context).
In fact, analysis cannot be separated from model construction, since errors, uncertainties and
incompatibilities in the information used to construct the models mean that there are many problems to
identify and correct during model construction. These slow down network construction, so part of our
research has been to develop methods that can assist in identifying the sources of errors in large networks.
Much analysis of genome-scale metabolic networks has revolved around optimizing the rate of biomass
production (or yield) and preicting the effect of mutations. We have been concerned to develop other
analyses that provide insight into properties and potential behaviours of the network, and this will
be illustrated with reults from our Arabidopsis model.
Prof. Jörg Stelling, ETH Zürich
Computational Engineering of Synthetic Genetic Circuits
Abstract: Ultimately, synthetic biology aims at establishing novel, useful biological
functions by suitably combining well-characterized parts. Especially when complex circuits -- in
terms of the number of components and interactions involved, or with respect to the dynamic behavior
-- are to be designed, computational engineering methods have to be an integral part of the approach.
This talk will focus on engineering concepts to achieve scalability and robustness (relative
insensitivity to external or internal perturbations of the designed circuits). Both are important
concerns for the field because the biology-based parts employed are not (yet) well-characterized,
the circuits have to operate in a noisy (cellular) environment, and they cannot be completely isolated
from, e.g., a cellular context.
More specifically, major open issues exist regarding (i) principles of circuits design with
standardized parts, and (ii) principles for the design of robust performance of synthetic circuits.
'Classical' synthetic genetic circuits as well as novel systems such as a tunable synthetic oscillator
in mammalian cells will be discussed as prototypical examples to illustrate our current capabilities.
In perspective, synthetic approaches do not only have the potential of major impacts in different
application areas, but also present challenging problems for engineering design.
Dr. Verena Wolf
Universität Saarbrücken
Stochastic Modelling of Biochemical Networks
Dr. Lars Küpfer
Systems Biology and Computational Solutions
Bayer Technology Services GmbH, Leverkusen
Application of integrated multi-scale models to pharmaceutical research and development
Abstract:
Computational models play an increasing role in pharmaceutical research and development,
since they offer an efficient way for storing, representing and analyzing experimental
data at each stage of (pre-)clinical development. Physiologically-based pharmacokinetic
(PBPK) models are a special form of pharmacokinetic models which represent the processes
underlying the distribution of a substance within the human body mechanistically at a
high level of detail. Based on generic drug distribution models and extensive collections
of physiological parameters they thus enable a comprehensive simulation of drug
pharmacokinetics at the whole-body scale. Moreover, structural refinements such
as metabolization processes or active transport can easily be introduced into the
basic PBPK models such that structural hypotheses can be evaluated.
Using different exemplary case studies we show here how computational models allow
the investigation of mechanisms governing a specific pharmacokinetic behavior.
Simultaneous consideration of a drug's modes of action at the cellular level enables
construction of integrated multi-scale models based on which dose-effect relationships
can be investigated from a systems perspective. Such multi-scale models have amongst
others been used for the correlation of genetic predisposition in a patient subgroup
with clinical endpoints. Computational models thereby significantly support assessment
of crucial points in drug R&D such as (1) a rigorous evaluation of drug efficacy at an
early phase of clinical development, (2) avoidance of adverse effects and (3) development
of individualized therapeutic designs.
Biographical Sketch:
Lars Kuepfer is a scientist in the group "Systems Biology and Computational Solutions"
of Bayer Technology Services GmbH in Leverkusen. He studied chemical engineering at the
TH Karlsruhe, RWTH Aachen and Carnegie Mellon University, Pittsburgh, and received his
Ph.D. degree from ETH Zurich. In his thesis, Lars Kuepfer analyzed metabolic and
regulatory principles in yeast based on computational models. In his current position,
he works on pharmacokinetic and pharmacodynamic modeling of novel drug candidates thereby
supporting decision making along the pharma development process. Lars Kuepfer's main
research interests are in the area of pharmacogenomics, metabolic modeling and system
identification.
| 
