March 03-07, 2008
Estimating neuronal network connectivityMarch 05, 2008 2:50 pm - 3:30 pm
The estimation the connectivity structure of neuronal networks is
hindered by one's inability to simultaneously and individually measure
the activity of all neurons. Many unmeasured neurons could be
interacting with the small set of measured neurons and corrupting
estimates of connectivity in unknown ways. For example, a common
connection from an unmeasured neuron could introduce correlations
among two measured neurons, which might lead one to erroneously infer
a connection between the measured neurons. We present a model-based
approach to control for such effects of unmeasured neurons. We
demonstrate the promise of this approach via simulations of small
networks of neurons driven by a visual stimulus.
The spontaneous emergence of cell polarity
March 04, 2008 9:50 am - 10:30 am
Cdc42 in yeast cells has the tendency to concentrate in polarized caps on the cell membrane, even when all other spatial cues have been deactivated.
In order to understand what mechanism might be responsible for this tendency, we considered the simplest possible positive feedback model for the cytosol-membrane diffusion of Cdc42 in a yeast cell. We found that stochasticity and positive feedback were the only necessary ingredients to achieve polarization, and we observed that polarization can be switched on and off by regulating precisely one of the four parameters in the model.
Predictions arising from the model have turned out to be consistent with experimental data.
Predictive models of Cis-regulatory transcriptional grammar
March 03, 2008 4:45 pm - 6:30 pm
Cis regulatory information comprises a key portion of genetic coding, yet despite the abundance of genomic sequences now available, identifying and characterizing this information remains a major challenge. We are pursuing a unique “bottom up” approach to understand the mechanistic processing of the regulatory elements (input codes) by the transcriptional machinery, using a well-defined and characterized set of repressors and activators in Drosophila blastoderm embryo. We are identifying quantitative values for parameters affecting transcriptional regulation in vivo, which are used to build and test mathematical models. The models will aid bioinformatics tools that predict the outputs of novel cis-regulatory elements in Drosophila and other organisms.
Giant, Krüppel and Knirps are short-range transcriptional repressors involved in the developmental patterning of Drosophila blastoderm embryo. Using defined regulatory modules tested in germline transformed embryos, we are measuring quantitative parameters describing the effects of spacing, stoichiometry, arrangement, specificity and binding site affinities of these repressors on cassettes driven by Dorsal/Twist activator set. To develop predictive models, we modify the Fractional Site Occupancy Models (that are mainly used for prokaryotic gene regulation) by using part of our data set. This modified model is being used to predict the output of novel permutations of sites, which will allow us to test and refine parameters used for the model.
Root networks: inside out
March 07, 2008 9:50 am - 10:30 am
Central to development are the specification and maintenance of cell identity. We are using Systems Biology approaches to understand the regulatory networks underlying these processes at the resolution of individual cell types. We are also using novel mathematical descriptors to investigate the growth and response of the physical networks that roots make as they explore their environment.
Multiscale look in protein interactomics
March 03, 2008 4:45 pm - 6:30 pm
This is a companion poster which complements the corresponding graphical results while the other one addresses the methodological part.
Multiscale tour in protein interactomics
March 03, 2008 4:45 pm - 6:30 pm
Protein interactomics represents a challenging field for
complex network analysis, and extensive efforts are ongoing to
model the topology. Both structure and dynamics are of
interest, and while the former aspect is more studied, as
single or multiple snapshots are available from many biological
sources, the latter is still at its infancy stage. With regard
to both aspects, we analyze the possible contribution coming
from multiscale decompositions of some characterizing protein
Inference of morphogenic pathways from live cell imagesMarch 06, 2008 2:00 pm - 2:40 pm
Morphogenic pathways such as those implicated in cell migration are regulated in space and time. Often they integrate mechanical signals with long-range effects and chemical signals with shorter range effects. One of the prime challenges in the analysis of pathways is to define the hierarchy and kinetics of signal transduction between spatially and temporally distributed pathway components. My lab is building a novel image analysis paradigm by which we multiplex image measurements across many experiments to register time courses of signaling events relative to cellular outputs. Indirectly, this defines also the sequence of activation of pathway components that are not simultaneously measured. Key to our multiplexing concept is the local analysis of constitutive stochastic fluctuations in pathways components with high resolution, i.e. below the diffusion radius of signaling molecules. Under these conditions, the correlation of time courses reveals precise information of the timing and of the spatial relationships between pathway activities, independent of intra-cellular and inter-cellular heterogeneity. From the timing, we can then infer causality and derive maps of the pathway hierarchy. In this presentation I will outline the idea of image fluctuation analysis and multiplexing and present first examples of pathway inference that underpin the potential of this data analysis approach.
Signaling networks in chemotaxis and cytokinesisMarch 06, 2008 9:50 am - 10:30 am
Joint work with Yoichiro Kamimura, Meghdad Rahdar,
Jane Borleis, Yu Long, Sandra de Keijzer, Jonathan Franca-Koh,
Kristen Franson, Michelle Tang, and Stacey S. Willard
(Department of Cell Biology, Johns Hopkins University,
Baltimore, MD, USA 21205).
The mechanisms of sensing shallow gradients of extracellular
signals is remarkably similar in Dictyostelium amoebae and
mammalian leukocytes. An extensive series of studies have
indicated that the upstream components and reactions in the
signaling pathway are quite uniform while downstream responses
such as PI (3,4,5)P3 accumulation and actin polymerization are
sharply localized towards the high side of the gradient.
Uniform stimuli transiently recruit and activate PI3Ks and
cause PTEN to be released from the membrane while gradients of
chemoattractant cause PI3Ks and PTEN to bind to the membrane at
the front and the back of the cell, respectively. This
reciprocal regulation provides robust control of
PIP3 and leads
to its sharp accumulation at the anterior. A similar
PIP3-based "polarity circuit" plays a key role in cytokinesis
where PI3Ks and PTEN move to and function at the poles and
furrow, respectively, of the dividing cell. Disruption of PTEN
broadens PI localization and actin polymerization in parallel,
leading to vigorous extension of lateral pseudopodia; however,
lowered levels of PIP3 do not greatly interfere with either
chemotaxis or cytokinesis, suggesting that additional pathways
act in parallel.
A screen to identify redundant pathways revealed a gene with
homology to patatin-like phospholipase A2. Loss of this gene
did not alter PIP3 regulation, but chemotaxis became sensitive
to reductions in PI3K activity. Likewise, cells deficient in
PI3K activity were more sensitive to inhibition of
activity. Deletion of the PLA2 homologue and two PI3Ks caused a
strong defect in chemotaxis and a reduction in
receptor-mediated actin polymerization. We propose that
PLA2 and PI3K signaling act in concert to mediate chemotaxis and
arachidonic acid metabolites may be important mediators of the
Evidence has suggested that PKB signaling plays a role in cell
motility and that TorC2 can regulate the actin cytoskeleton. We
have recently shown that activation of TorC2 and PKB occurs at
the leading edge of chemotaxing cells and plays a critical role
in directed cell migration. Within seconds of stimulation of
chemotactically sensitive cells, two PKB
homologs, PKBA and PKBR1, transiently phosphorylate at least
seven proteins. The
enzymes are activated by phosphorylation of their hydrophobic
motifs (HMs) through
TorC2 and subsequent phosphorylation of their activation loops
(ALs). Activation of
PKBR1, a myristoylated form persistently bound to the membrane,
does not require
PI(3,4,5)P3. Cells deficient in PKBR1 or TorC2, lack most of
substrates and are specifically impaired in directional
sensing. Thus, temporal and spatial activation of PKB signaling
by TorC2 is a critical event in directed cell migration that
can act independently of localized PI(3,4,5)P3.
Drosophila morphogenesis: Tissue dynamics and emergent
properties during dorsal closure
March 06, 2008 11:00 am - 11:40 am
Dorsal closure, an essential stage of Drosophila morphogenesis, provides a
model system for tissue dynamics. Our research approach is based on modern
genetics, in vivo imaging, laser microsurgery, digital image processing,
and quantitative modeling to identify the mechanical forces that connect
the genetic program of development to morphogenesis. Key to the dynamics
of dorsal closure are four biological processes, involving three tissues,
that are coordinated in space, synchronized in time, and remarkably
resilient both to genetic perturbations and to laser perturbations. These
processes can upregulate in response to laser perturbation, where there are
spatial, kinematic, and dynamic asymmetries associated with upregulation.
There also are asymmetries observed during non-perturbed, wild type
closure, which are associated with the failure of dorsal closure in several
mutant embryos. We are quantitatively characterizing emergent properties
during dorsal closure, i.e., a velocity governor and the apparent
coordination and synchronization of cell activities for these tissues.
Peralta et al. Upregulation of forces and morphogenic asymmetries in dorsal
closure during Drosophila development. Biophysical Journal 92: 2583-2596
Kiehart et al. 2005. Ultraviolet Laser Microbeam for Dissection of
Drosophila Embryos. In Cell Biology: A Laboratory Handbook, Third Edition.
J. E. Celis, editor. Elsevier, San Diego. 87-103 (2005).
Franke et al. Nonmuscle myosin II generates forces that transmit tension
and drive contraction in multiple tissues during dorsal closure. Curr Biol.
15: 2208-2221 (2005).
Hutson et al. Forces for morphogenesis investigated with laser microsurgery
and quantitative modeling. Science 300: 145-149 (2003).
Kiehart et al. Multiple forces contribute to cell sheet morphogenesis for
dorsal closure in Drosophila. J. Cell Biol. 149: 471-490 (2000).
Math matters public lecture: The best of all possible worlds: The idea of optimizationMarch 04, 2008 7:00 pm - 8:15 pm
The idea of optimization is intimately connected with modern science. Pioneers like Galileo, Fermat, and Newton, were convinced that the world had been created by a benevolent god who had established the laws of nature as the most efficient way to achieve his purposes: in short, this is the best of all possible worlds, and it is the task of science to find out why and how. Gradually this view was overturned, leaving optimization as an important tool for the human-engineered world. More recently, game theory has come to replace optimization for describing situations where a multitude of individuals with conflicting interests make decisions based on imperfect information. In this lecture, Professor Ekeland will guide us along the path from Fermat to modern economic theory, and from optimization to game theory.Read More...
A "bottom-up" approach to deciphering and predicting cis-regulatory
transcriptional grammar in DrosophilaMarch 04, 2008 4:00 pm - 4:15 pm
Joint work with
Ahmet Ay2, Evan Dayringer2, Rupinder Sayal1, Chichia Chiu2, David N. Arnosti1.
Cis regulatory information comprises a key portion of genetic coding,
yet despite the abundance of genomic sequences now available,
identifying and characterizing this information remains a major
challenge. We are pursuing a unique “bottom-up” approach to understand
the mechanistic processing of the regulatory elements (input codes) by
the transcriptional machinery, using a well-defined and characterized
set of repressor and activator transcription factors in Drosophila
blastoderm embryos. We are identifying quantitative values for
parameters affecting transcriptional regulation in vivo, which are used
to build and test mathematical models that predict the outputs of novel
Giant, Krüppel, Knirps are short-range transcriptional repressor
proteins involved in the developmental patterning of Drosophila
blastoderm embryo. Using defined regulatory modules tested in germline
transformed embryos, we are measuring quantitative values of Giant
protein (input) and lacZ mRNA (output) using confocal laser scanning
microscopy technique. The expression of lacZ reporter gene is driven by
Twist/Dorsal activator proteins. We build 3-scale mathematical models
which consist of computer simulation of transcription factors (TF)
dynamic, mathematical description of cis-regulatory elements (RE) along
the DNA enhancer cassettes, and integration of TF and RE to predict
transcription output. Our modeling at the DNA level will integrate all
the key parameters of the binding sites including arrangement, spacing,
stoichiometry, affinity, proximity to basal promoter and collaboration.
We employ a nucleotide-base potential function for repressor and
activator binding sites and partial differential equations to derive
density functions representing the transcriptional map of clusters of
binding sites. These models are being used to predict the output of
novel permutations of binding sites, which will allow us to test and
refine parameters used for the model.
In one line of investigation, fluorescence quantitation of mRNA lacZ
expression was used to measure the effect of moving Giant repressor
binding sites from a position adjacent to Twist/Dorsal activator sites
to a distal site 125bp upstream. Our mathematical model successfully
predicted the distance effect of intermediate positions, such as 25, 50,
75 and 100bp compared to the experimental results. Enhancer cassettes
with 1, 2 and 3 binding sites of Giant or Krüppel repressors upstream
from Twist/Dorsal activator sites were analyzed for the effect of
stoichiometry on lacZ expression. The in situ experimental results
indicate a cooperative contribution within Giant and Krüppel repressor
proteins on lacZ repression. Our model predicts a strong cooperativity
within these repressor proteins. In other modules examined the effect of
arrangement of Giant binding sites to the Twist/Dorsal activator sites,
it showed a significant difference in transcriptional output providing a
good evidence for the importance of modeling many key parameters of
cis-transcriptional regulation. Extension of these predictive models to
endogenous cis-elements will provide novel insights on regulatory
element design and evolution, and should provide a bioinformatics tool
for predicting quantitative output of novel regulatory elements.
1Department of Biochemistry & Molecular Biology
Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
Circadian clocks and switches in cynanobacteria
March 04, 2008 11:00 am - 11:40 am
Synechococcus elongatus, a single celled cyanobacterium, has a remarkably precise and robust circadian clock which has been reconstituted in vitro with just three proteins. Its simplicity makes it an ideal system for understanding dynamical functions that can arise from the kinetics of multiple phosphorylations. Recent experiments and theoretical analysis will be presented along with speculations on the evolutionary origins of this clock.
Engineered gene circuits
March 05, 2008 9:00 am - 9:40 am
Uncovering the structure and function of gene regulatory
networks has become
one of the central challenges of the post-sequencing era.
of protein-DNA feedback loops and gene regulatory networks have
proposed, and recently, certain qualitative features of such
models have been
experimentally corroborated. For the first portion of the
progress in constructing two synthetic gene oscillators will be
oscillators were built in accordance with design criteria that
was developed with
computational modeling. Both oscillators are robust, with all
with a characteristic frequency that can be tuned with external
inducers or temperature
shifts. In the second part of the presentation, the response of
gene regulation to periodic changes in the external carbon
source will be discussed.
The central finding is the metabolic regulatory system acts as
filter that reliably responds to a slowly changing environment,
ignoring fluctuations that are too fast for the cell to mount
an efficient response.
Computational modeling calibrated with experimental data is
used to determine
that frequency selection in the system is controlled by the
interaction of coupled
positive feedback networks governing the signal transduction of
carbon sources. The simulations suggest that the feedback loops
a robustness to environmental fluctuations on cells regardless
of deficiencies in
network components. This prediction is validated with an
of two cellular strains that exhibit the same filtering
having markedly different induction characteristics. The
highlights the utility of engineering-based methods in the
gene regulatory networks.
Using CellDesigner to create the iron metabolic network
March 03, 2008 4:45 pm - 6:30 pm
Iron is necessary for cell growth, but through the Fenton reaction,
excess iron can cause dangerous radicals. Hence, a cell's metabolism of
iron is essential for survival. We present a model of iron metabolism
in a generic epithelial cell created using CellDesigner, a diagram
editor for creating biochemical or gene regulatory networks. This
network includes all possible interactions found in the literature and
includes reactions specific to intestinal epithelial cells (iron uptake)
and hepatocyes (iron storage).
Identifying transcriptional dichotomies by stochastic samplingMarch 05, 2008 11:00 am - 11:40 am
The developmental fate of cells depends upon the microenvironment provided by neighboring cells, growth factors, and the extracellular matrix. Subtle differences in the microenvironment can lead to heterogeneous phenotypes, even within isogenic cell populations that have been cultured uniformly. The genes and proteins linking microenvironment variability to cell outcomes are mostly unknown, because there has not been a way to systematically identify molecular differences among cells that otherwise appear identical. Here, I will present a new technique (called "stochastic sampling") that attempts to address this general problem. Stochastic sampling involves the repeated, random selection of very-small cell populations (~10 cells) followed by quantitative gene-expression profiling and simple statistical analysis. Comparing how transcripts are distributed across repeated samplings distinguishes molecular dichotomies (high expression in some cells, low expression in others) from biological noise. We combine laser-capture microdissection, a customized single-cell amplification protocol, and quantitative PCR to implement stochastic sampling in an in vitro model of mammary-gland morphogenesis. The distinct gene-expression patterns predicted by stochastic sampling were verified in situ by multicolor RNA FISH, identifying coregulated gene clusters that were completely obscured at the population level. Last, I will discuss preliminary work showing how our approach can be scaled up to the entire transcriptome by using oligonucleotide microarrays. These stochastic-profiling experiments have revealed unanticipated transcriptional dichotomies that may play critical roles during mammary-gland morphogenesis.
Robustness of signal processing in bacterial chemotaxisMarch 04, 2008 2:50 pm - 3:30 pm
Any signalling network must be able to extract weak signals from a noisy
environment and be robust against background perturbations, such as
stochastic fluctuations in protein levels or variations in temperature and
ambient stimulation. Our aim is to elucidate how these tasks have been
solved in the evolutionary design of the chemotaxis pathway in E. coli, one
of the best studied models for signal transduction. Combining
fluorescence-based experimental analyses of spatial and temporal dynamics of
the intracellular signal processing with quantitative computer modelling,
we show that the robustness of chemotactic signalling is ensured at multiple
levels, from gene expression to pathway topology to reaction rates.
Mapping evolutionary pathways of HIV-1 drug resistance using
conditional selection pressureMarch 04, 2008 2:00 pm - 2:40 pm
Can genomics provide a new level of strategic intelligence about
rapidly evolving pathogens? We have developed a new approach to
measure the rates of all possible evolutionary pathways in a genome,
using conditional Ka/Ks to estimate their "evolutionary velocity",
and have applied this to several datasets, including clinical
sequencing of 50,000 HIV-1 samples. Conditional Ka/Ks predicts the
preferred order and relative rates of competing evolutionary
pathways. We recently tested this approach using independent data
generously provided by Shafer and coworkers (Stanford HIV Database),
in which multiple samples collected at different times from each
patient make it possible to track which mutations occurred first
during this time-course. Out of 35 such mutation pairs in protease
and RT, conditional Ka/Ks correctly predicted the experimentally
observed order in 28 cases (p=0.00025). Conditional Ka/Ks data
reveal specific accessory mutations that greatly accelerate the
evolution of multi-drug resistance. Our analysis was highly
reproducible in four independent datasets, and can decipher a
pathogen's evolutionary pathways to multi-drug resistance even while
such mutants are still rare. Analysis of samples from untreated
patients shows that these rapid evolutionary pathways are
specifically associated with drug treatment, and vanish in its absence.
A mathematical model of the TNF signaling network
controlling apoptosis via NF-kB and JNK
March 03, 2008 4:45 pm - 6:30 pm
Tumor necrosis factor alpha (TNF) is a pro-inflammatory cytokine that
is released in response to trauma or infection. Binding of TNF to its
receptor (TNFR) results in direct activation of caspase-8 and the
apoptotic machinery via the intracellular death domain of TNFR.
Healthy cells, however, are highly resistant to TNF-induced apoptosis
owing to concurrent activation of nuclear factor kappa B (NF-kB), a
pro-inflammatory, anti-apoptotic transcription factor. Recent studies
suggest that the anti-apoptotic function of NF-kB may be mediated
through cFLIP, whose pseudo-caspase domain prevents activation of
caspase-8 by ligand-bound TNFR. To investigate the importance of this
mechanism under various physiological conditions, we have constructed
a mathematical model of TNF-induced NF-kB activation and regulation of
the JNK pro-apoptotic pathway.
Noise and dynamics of the flagellar length control systemMarch 03, 2008 10:10 am - 10:50 am
A basic unsolved question in cell biology is how the size of organelles is determined. Eukaryotic flagella (also known as cilia) are ideal organelles to study size control because they are linear structures and therefore require only a single number to specify size. Based on pulse-labeling and other experimental analyses, we have developed a simple model for flagellar length control based on the inherent length-dependence of intraflagellar transport. We compare the predicted dynamic behavior of the model, implemented by a series of increasingly complicated network implementations, with experimental observations of live cells. In order to further explore the functioning of this system, we have begun to measure biological noise in length control, and to couple experimental measurements of noise with predictions based on small-signal noise analysis of the model. These analyses allow us to deduce how mutations which affect length may change individual parameters within the model, as well as to predict novel phenotypes to be sought in future genetic screens. The approaches we are developing for studying flagellar length control should be applicable to other organelle size control problems.
Gene vectors and distances between them: the neglected aspect of
March 03, 2008 4:45 pm - 6:30 pm
Complex networks may be around us, and biologists have taken them to
heart. Some of these networks exist in a real sense: a signal can be
sent from an Internet address to other addresses, and perhaps from some
cells in metazoan neural system to some other cells. But is there any
physical sense of, say, protein-protein interaction network? For
example, can anything be sent or propagated across it? Another question
has to do with the quality of evidence. After the first round of claims
that certain biological networks are scale-free, or small-world, or
highly robust, we entered the stage of much more careful analysis, and
many of these conclusions are being refined and sometimes even refuted.
Finally, there is the "so what?" factor. Much attention has been given
to the global properties of biological networks, e.g., their node-degree
distribution - but, even when we finally describe such properties with
some accuracy, which of them will be important for understanding of
My aspiration is to focus on what is real, and perhaps fundamental,
about gene networks. My main claim is that any gene network is defined
by the set of distances (or similarities) between each pair of genes,
and by statistics that tells which of them are close enough to be of
interest. Moreover, I speculate that if the network is directed, the
directions of the edges can also be inferred from the distance matrix.
Thus, understanding of the properties of distances between genes is a
prerequisite for any "network biology."
From systems to synthetic biology: A control theoretic
March 03, 2008 4:45 pm - 6:30 pm
Applying results from dynamical systems, control and
optimization, we develop new approaches for designing experiments to
elucidate the biochemical network structure of the chemotaxis
mechanism in R. sphaeroides. Biological information and data is used
to create initial models (model determination); an experiment is then
designed in order to discriminate between these models; and a model
invalidation procedure closes the loop. This way we can develop an
understanding of the underlying biochemical network structure: a
Synthetic Biology approach could then be used to redesign such
networks for improved or modified functionality.
The vertebrate segmentation clock: converting time into embryonic patternMarch 07, 2008 9:00 am - 9:40 am
The vertebrate body can be subdivided along the antero-posterior (AP) axis into repeated structures called segments. This periodic pattern is established during embryogenesis by the somitogenesis process. Somites are generated in a rhythmic fashion from the paraxial mesoderm and subsequently differentiate to give rise to the vertebrae and skeletal muscles of the body. Somite formation involves an oscillator, the segmentation clock whose periodic signal is converted into the periodic array of somite boundaries. This clock drives the dynamic expression of cyclic genes in the presomitic mesoderm and requires Notch and Wnt signaling. Microarray studies of the mouse presomitic mesoderm transcriptome reveal that the segmentation clock drives the periodic expression of a large network of cyclic genes involved in cell signaling. Mutually exclusive activation of the Notch/FGF and Wnt pathways during each cycle, suggests that coordinated regulation of these three pathways underlies the clock oscillator. Whereas the segmentation clock is thought to set the pace of vertebrate segmentation, the translation of this pulsation into the reiterated arrangement of segment boundaries along the AP axis involves FGF and Wnt signaling. The FGF pathway controls the positioning of the wavefront, which corresponds to the level of the presomitic mesoderm where cells respond to the clock. fgf8 mRNA is only transcribed in tail bud precursors and it progressively decays in newly formed paraxial mesoderm cells, thus forming a dynamic mRNA gradient. This mRNA gradient is then translated into a graded FGF signaling response used to position the wavefront. This mechanism provides an efficient means to couple the spatio-temporal activation of segmentation to the posterior elongation of the embryo.
Digital negative and analog positive selection
thresholds in T cells:
A molecular model
March 03, 2008 4:45 pm - 6:30 pm
After the T cell receptor (TCR) has been formed on immature T cells in the
thymus (thymocytes), they go through a process of selection in which the TCR
is tested for its response against self-peptides. Thymocytes that either
show very weak responses die through neglect (positive selection); those
that show very strong responses die through apoptosis (negative selection).
Defects in this selection process is an important component of
predisposition towards T cell induced autoimmunity. Experiments have also
shown that positive selection is a graded function of the strength of the
signal received at the TCR while negative selection is a much sharper,
almost digital function. Some point downstream of the TCR, weak signals
appear to transduce along a different path of the signaling network than
strong signals. The molecular origins of these differences have been a
subject of much investigation in immunology. We have built a new explanation
of the experiments based on a computational model of a module of the protein
signaling pathway that describes the experimental observations
qualitatively. Experiments are also underway to test the model.
Adaptation-driven models of cancer invasion: Experimental
parameterization and validationMarch 05, 2008 9:50 am - 10:30 am
Computer simulations based on the Hybrid Discrete-Continuous
model of cancer invasion (Anderson et al., Cell. 2006,127:905)
predict that the degree of severity
of the tumor microenvironment (tmE) directly impacts on the
emergence of invasion. More
precisely, harsh ME conditions (e.g., hypoxia, discontinuous
matrix, inflammation) select for
dominant aggressive clones that grow into a fingering,
infiltrating mass. In contrast, in mild ME
conditions (normoxia, homogenous matrix) selection for dominant
aggressive clones is relaxed,
so that they coexist with less aggressive ones and, together,
they grow into a smooth-margin, noninvasive
tumor mass. To populate HDC simulations with experimental data,
we use a panel of
cell lines, derived from the breast epithelial cell MCF10A. We
have established a collection of
variants of this platform cell line with distinct invasive
potential, generated by transfection of
oncogenes or passaging in vivo. We measured the following
consumption/hypoxia, tumor cell proliferation, tumor cell
survival, metabolic consumption, and
matrix degrading enzyme activity. Initial simulations with
these homogenous data indicate that
invasion may require competition between phenotypes with
distinct adaptive traits to the tmE.
To validate these predictions in vitro, we have developed a
novel Island Invasion Assay (IIA),
which closely mimics the spatial arrangements of the HDC model.
Preliminary results suggest
that IIA supports the HDC predictions concerning invasion
(fingering) under stressful tmE
conditions. In addition, some unexpected results point to novel
features that could be included in
the HDC model to increase realism. This is an excellent example
of synergistic interactions
between modeling and experimentation, which will hopefully
produce novel insights in the
mechanisms underlying cancer invasion. For in vivo validation,
we are comparing orthotopic
(“mild ME”) versus subcutaneous (“harsh ME”) human breast
cancer xenografts in mice (in vivo
VICBC group, headed by Lisa McCawley). We utilized MCF-10
variant cell lines, CA1a and
CA1d, shown to have distinct and consistent tumorigenic
properties through repeated passage in
immunocompromised mice. Several in vivo imaging modalities are
being exploited to provide
quantitative analysis of cellular parameters of the same tumor
over time: Magnetic Resonance
Imaging (MRI) analysis to distinguish tumor volume and necrotic
tumor areas (i.e. nonoxygenated
states) from viable (i.e. oxygenated) tissue; positron emission
topography (PET) scan
imaging of 18F-labeled-fluorodeoxyglucose(FDG) for metabolism;
Optical imaging analysis of
fluorogenic probes termed “proteolytic beacons” for matrix
degrading protease activity (i.e.
substrate hydrolysis). Tumor specimens are also biopsied at
fixed volumes (0.5, 1 and 1.5 cm
diameters) for further ex vivo analyses, including histology to
immunohistochemistry and immunofluorescence to measure cellular
incorporation), apoptosis (TUNEL) and hypoxia (hypoxiprobe).
Initial results from these analyses
reveal advantages and limits of in vivo parameterization and
validation. The main advantage is
that relevant variables and parameters for tumor invasion can
only be truly identified in a living
organism, at least until convincing in vitro surrogates for
spatial invasion, such as the IIA, are
developed. The limits reflect largely the fact that
conventional experimental biology has been
seldom used for model validation, so that tools and approaches
must be refined and adapted.
These broad issues as well as intitial specific data will be
The evolutionary design of proteinsMarch 03, 2008 9:20 am - 10:00 am
Classical studies show that for many proteins, the information required for specifying the tertiary structure is contained in the amino acid sequence. However, the potential complexity of this information is truly enormous, a problem that makes defining the rules for protein folding difficult through either computational or experimental methods. In the past few years, we have reported a new method (that we call the statistical coupling analysis or SCA) for estimating the conserved evolutionary constraints between sites on proteins through statistical analysis of large and diverse multiple sequence alignments1,2. Experiments in several protein systems demonstrate the functional importance of this evolution-based mapping of amino acid interactions1,3,4 and recently, the SCA information was shown to the necessary and sufficient to design functional artificial members of a small protein family in the absence of any structural or chemical information. These results support the hypothesis that in a purely statistical and mechanism-free way, the SCA captures the evolutionary rules for specifying natural-like proteins. In recent work, we have extended these results to the design of larger protein domains and are working on the physical mechanisms underlying the statistical coupling. The evolutionary constraints between residues are much more sparse and heterogeneous than traditional analyses of atomic structures would suggest, a finding that guides our thinking about potential general principles underlying the natural design of proteins.
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Analysis of gene sets based on the underlying regulatory
March 03, 2008 4:45 pm - 6:30 pm
Development of high throughput technologies including DNA
microarrays has facilitated the study of cells and living organisms.
The challenge is
no longer to identify
the genes or proteins that are diffentially expressed, but
rather finding sub-systems
that interact with each other in response to a given
environmental condition. Study
of these interacting sub-systems has provided an invaluable
source of additional information that can be used to better understand the
complex mechanisms of life. In this
paper, we propose a latent variable model that directly
incorporates the external information about the underlying network. We then use the theory of
mixed linear models
to present a general inference framework for the problem of
testing the significance of
subnetworks. Performance of the proposed model is evaluated on
simulated and real data examples.
Modeling histone mediated epigenetics as a positive feedback process March 06, 2008 9:00 am - 9:40 am
Chromosomal regions can adopt stable and heritable alternative states resulting in bistable gene expression without changes to the DNA sequence. Such epigenetic control is often associated with alternative covalent modifications of histones. The stability and heritability of the states are thought to involve positive feedback where modified nucleosomes recruit enzymes that similarly modify nearby nucleosomes. We developed a simplified stochastic model for dynamic nucleosome modification based on the silent mating-type region of the yeast Schizosaccharomyces pombe. We show that the mechanism can give strong bistability that is resistant both to high noise due to random gain or loss of nucleosome modifications and to random partitioning upon DNA replication. However, robust bistability required: (1) cooperativity, the activity of more than one modified nucleosome, in the modification reactions and (2) that nucleosomes occasionally stimulate modification beyond their neighbor nucleosomes, arguing against a simple continuous spreading of nucleosome modification.
Ian B. Dodd, Mille A. Micheelsen, Kim Sneppen and Geneviève Thon
Cell, Vol 129, 813-822, 18 May 2007
Qualitative/quantitative analysis of biomolecular network dynamics
March 04, 2008 9:00 am - 9:40 am
Biomolecular networks, while exhibiting a rich variety of behaviors in
signaling and regulation, would appear to be fairly well behaved as dynamical
systems. Their (mathematical) models have solutions that tend to settle into
well-defined steady states or periodic, but not "chaotic", behavior. This
presents one major challenge to theoreticians: what is special about such
networks, vis a vis general dynamical systems? A second challenge arises in
the mathematical analysis itself: while on the one hand good qualitative,
graph-theoretic, knowledge is frequently available, on the other hand it is
often hard to experimentally validate the form of the nonlinearities used in
reaction terms, and even when such forms are known, to accurately estimate
coefficients (parameters, such as kinetic constants). This
"data-rich/data-poor" dichotomy seems to be pervasive in systems biology.
This theory talk is concerned with both challenges. We approach the problem
through the standard paradigm in control theory and signal processing, that of
viewing larger systems as interconnections of input/output subsystems:
provided that these subsystems are individually well-behaved, more complex
behaviors arise from the global interconnection structure. This brings up a
host of issues, from basic issues of modularity, retroactivity, and input and
output "impedance", to the characterization of classes of appropriately
"simple" components, to the question of what type of quantitative information
suffices for obtaining precise conclusions regarding dynamics (such as the
existence of multiple stable steady states, or oscillations).
As one specific example, we consider order-preserving (monotone) components,
which enjoy particularly nice dynamical properties as well as robust responses
to perturbations. Their interconnections may be, in principle, studied
through a blend of qualitative and (relatively sparse) quantitative
information, allowing one to draw conclusions about global dynamical behavior
and the location of steady states. The talk will also present evidence
suggesting that natural signaling and transcriptional regulation networks may
be close to monotone, and that networks that are close to monotone are
statistically better behaved than more arbitrary ones.
The website http://www.math.rutgers.edu/~sontag/
copies of papers with technical details.
Bayesian network algorithms for revealing structure of complex biological networksMarch 05, 2008 2:00 pm - 2:40 pm
Network inference algorithms, and in particular Bayesian networks algorithms, are being applied with growing regularity in computational molecular biology to recover gene regulatory networks from gene expression data. However, the basic task at hand -- to predict causal relationships based on repeated concurrent measurements of multiple variables -- -is not necessarily limited to the molecular realm. Here, I present my research on using Bayesian network inference algorithms to recover networks on several different levels of biological organization: genes, neurons, and ecosystems.
Quantifying and predicting gene regulation in single cellsMarch 03, 2008 2:50 pm - 3:30 pm
The quantitative relation between transcription factor concentrations
and the rate of protein production from downstream genes is central to
the function of genetic networks. I will describe a technique to
measure this relation, the gene regulation function, in individual
living cells. The gene regulation function is often a sigmoidal
function, and I will show that, by interpreting genetic networks as
inference modules, we can make predictions about why this sigmoidal
function has different characteristics for different genetic
networks. Our results should provide a basis for both understanding
cellular decision-making and for designing synthetic genetic circuits.
Noisy out of necessity: Probabilistic behavior in
cellular differentiationMarch 03, 2008 11:20 am - 12:00 pm
Diverse organisms ranging from bacteria to mammalian
stem cells undergo pluripotent differentiation where a
single cell can commit to one out of several cell
fates. How do underlying genetic circuits allow cells
to “choose” a specific cell fate and execute the
appropriate differentiation program? To address this
question we investigate a simple bacterial
differentiation system utilizing mathematical modeling
and quantitative single cell measurements. In
particular we are interested in elucidating the
significance of circuit dynamics and stochastic
behavior during cellular differentiation.
Joint analysis of gene expression and gene copy number
variations using independent component analysis
March 03, 2008 4:45 pm - 6:30 pm
It is recognized that a biological systems should be
multiscale and multi-modality imaging platforms such as using
gene expression and array CGH. While microarray gene
presents functional information, the array CGH analysis
structural variations of genome using gene copy number
integration of this complementary information is challenging.
the gene expression and copy number variations as two
measurements of a biological system and apply the independent
analysis (ICA) to project the data into statistically
biological processes, which are then integrated to identify
patterns in two inputs. We apply the method to cluster genes
to the integrated data, resulting improved identification of
are statistically significant in both measurements (e.g.,
expression and aCGH). We also compared the approach with
approaches such as principal component analysis (PCA), and
generalized singular value decomposition (GSVD),
Transient decision making in a transcriptional circuit of HIV-1
March 03, 2008 2:00 pm - 2:40 pm
Multistability (and steady-state behavior) is assumed to be
feature of genetic ³on/off² switches (e.g. Lambda-phage
coli lactose utilization, and human progenitor-cell
However, when one behavior of the switch is transient or
destroys the cell,
such as viral lysis, the underlying genetic circuit need
not be bistable.
Instead, transient excursions from a single monostable
state may allow
multiple behaviors, with switching arbitrated by the
lifetime (decay) of the
transient signal. I will discuss our recent work
strength in HIV by exploiting the stochastic noise inherent
gene-expression. Our results indicate that transcriptional
positive-feedback extends transient gene-expression
lifetime and thereby
controls cell-fate in HIV.
Modularity, feedback and recursion in the EGF receptor systemMarch 06, 2008 2:50 pm - 3:30 pm
The epidermal growth factor receptor (EGFR) is a central signal transduction pathway in epithelial cells and regulates diverse biological responses such as proliferation, migration and differentiation. Ligands for the EGFR, such as amphiregulin and TGFa are synthesized as membrane-anchored precursors and released by regulated proteolysis, leading to autocrine signaling. Ligand shedding is controlled by a number of different hormones and is responsible for EGFR “transactivation” or crosstalk. We have sought to understand the design principles of autocrine network in cells by using a combination of mathematical modeling and quantitative experimental approaches. We found that the core EGFR autocrine network can be modeled as three functional modules: ligand production, EGFR activation and ERK signaling. These three modules are linked in a recursive manner such that the output of the ERK module is linked to the input of the ligand production module. This positive feedback loop is shunted through the extracellular space, providing a mechanism by which the overall level of ERK signaling is linked to a cell’s environment. Each module in this circuit behaves as a linear control unit, apparently because of intramodule feedback. In contrast, intermodule feedback appears to control the overall level of signaling. Multiple growth factors feed into the EGFR autocrine circuit at different points, with consequent additive or synergistic effects. Despite the linearity of the overall autocrine circuit, internal module mechanisms can display nonlinear and even oscillatory behavior. In addition, consequent cellular outputs or decisions can be nonlinear or switch-like. We suggest that cell signaling networks are composed of a series of interconnected linear modules and that negative feedback is used extensively to increase module linearity and robustness. This structure likely evolved as a mechanism to facilitate module assembly into higher-order regulatory networks. However, the network can display non-linear properties at either the sub-module or supra-module level. Parsing signaling networks into their constituent modules is a potentially powerful approach for scaling signaling models to the multicellular level.
Homotopy methods for counting reaction network equilibria
March 03, 2008 4:45 pm - 6:30 pm
Dynamical system models of complex biochemical reaction networks are usually high-dimensional, nonlinear, and contain many unknown parameters. In some cases the reaction network structure dictates that positive equilibria must be unique for all values of the parameters in the model. In other cases multiple equilibria exist if and only if special relationships between these parameters are satisfied. We describe methods based on homotopy invariance of degree which allow us to determine the number of equilibria for complex biochemical reaction networks and how this number depends on parameters in the model.
Joint work with G. Craciun and J. W. Helton.
Speed analysis of traveling wave solutions of some nonlocal evolutionary
March 03, 2008 4:45 pm - 6:30 pm
Nonlinear traveling waves in synaptically coupled neuronal
March 03, 2008 4:45 pm - 6:30 pm
By using a singularly perturbed system of
integral differential equations as a reasonable realistic model,
we show how a nonlinear nonlocal neuronal network can generate several stable traveling waves,
including homoclinic orbits and heteroclinic orbits.
All of these orbits serve as nontrivial local attractors of the dynamical systems.
Furthermore, we establish relation between speed index functions and
stability index functions whose zeros are necessary and sufficient to
determine the stability of the brain waves.