September,
3-7, 2012
PARTICIPANTS' TALKS |
Simona Arcuti
Spatio-temporal
models for zero-inflated data: an application to the abundance data of two crustaceans’
species in the
In the ecological field,
abundance data are often characterized by the zero inflation of population
distributions. In this work we consider two commercial species belonging to the
faunistic category of crustaceans, abundant in the
North-Western Ionian Sea, namely the Parapenaeus longirostris (Lucas, 1846) and the Aristaeomorpha
foliacea (Risso, 1816).
Biological data concerning the two species of shrimp are collected during trawl
surveys carried out from 1995 to 2006 as part of the international program
MEDITS (International bottom trawl survey in the
Maja Czokow
Bayesian networks in detecting key structure
features of a spring system in exploring proteins conformations
In our work we developed a mathematical model, which is applied to
explore conformational movements of proteins. The model can be used in order to
identify key residues (amino acids, groups of atoms), which have the greatest
impact on transition from one conformation to another [1] or to detect
intermediate conformations, a reaction path between two input conformations. We
are going to employ Bayesian network to infer, which real-valued parameters of
our mathematical model have the greatest impact on its efficiency. Therefore,
the Bayesian network presents influence of attributes on the quality of
solutions returned by the method. In order to find conditional probabilities
for the network, we are going to take advantages of numerical results obtained
by software implementation of our model. Numerous tests for different combinations
of the values of the model attributes, will guarantee good estimation of the Bayesian
network parameters. Our mathematical model of the protein conformations is
implemented by means of spring systems, which are represented by a graph G: =
(V; E) embedded in a Euclidean space R^3. The principal task for the spring
systems is to assume a required mechanical behaviour (physical locations of
network nodes, regarded as output) in response to suitable physical stimuli (displacements
of control nodes, regarded as input). In [2], we show how such systems can be implemented.
Each atom or group of atoms of given protein is represented by a node of V and
virtual bonds between them constitutes the set of springs E. In order to
accomplish the objective, the expected mechanical behaviour of the spring
system is defined by conformations of a given protein.
References
[1] Chen, W. Z., Li,C. H., Su, J. G., Wang, C.
X., Xu, X. J.
Identification of key residues for protein conformational transition
using elastic network model
[2] Czoków, M., Schreiber, T.: Adaptive
Spring Systems for Shape Programming. Proc. of the 10th International
Conference on Artificial Intelligence and SoftComputing,
Martina Feilke
Bayesian
spatial analysis of FRAP-images
Fluorescence Recovery after Photobleaching
(FRAP, see for example, Sprague and McNally, 2005) is a method in biology to
investigate in vivo the binding behaviour of molecules in a cell nucleus. The
molecules are therefore tagged fluorescently, a part of the cell nucleus of the
cell of interest is bleached, and the recovery of the bleached part of the
nucleus is observed by taking pictures of the nucleus in predefined time
intervals. The aim is to get information about the speed of the movement of the
unbleached molecules to observe their binding behaviour. More specifically, one
wants to get information about the existence of one or more binding sites for
the molecule as well as the duration of residence at a specific binding site. To
date, analysis of FRAP data has been performed either for only the bleached
part of the cell nucleus (for example, in Sprague et al., 2004), or for both
the bleached part and the unbleached part of the cell nucleus separately (for
example, in Phair et al., 2004), or for a finer
subdivision of the cell nucleus into a small number of disjoint parts (for
example, in Beaudouin et al., 2006).
Our goal is to perform a spatial analysis of
FRAP data at the pixel level. We plan to incorporate the concentration of
unbleached molecules of interest in neighbouring pixels into the fit of the
concentration curve per pixel, which is obtained by the imaging of the cell
nucleus, to account for diffusion. Moreover, we plan to model one binding
reaction per pixel. By solving the differential equations based on the
compartment model that describes the change of the concentration of unbleached
molecules in each pixel in the cell nucleus we aim to get a nonlinear
regression equation per pixel by which we can model this concentration at any
time during the recovery.
For each pixel, we intend to obtain estimates of
the on- and off-rates of the binding reaction providing information about the
binding behaviour of the molecules, as well as estimates of the volume of the
compartments of interest by applying a MCMC-algorithm with Gibbs- and/or
Metropolis-Hastings-update-steps.
References
Joel Beaudouin
et al. Dissecting the Contribution of Diffusion and Interactions to the
Mobility of Nuclear Proteins. Biophysical Journal,
90:1878-1894, 2006.
Robert D. Phair et al.
Global Nature of Dynamic Protein-Chromatin Interactions In
Vivo: Three-Dimensional Genome Scanning and Dynamic Interaction Networks of
Chromatin Proteins. MOLECULAR AND CELLULAR BIOLOGY, 24(14):6393-6402, 2004.
Volker J. Schmid et
al. A Bayesian Hierarchical Model for the Analysis of a
Longitudinal Dynamic Contrast-Enhanced MRI Oncology Study. Magnetic
Resonance in Medicine, 61:163-174, 2009.
Brian L. Sprague and James G.
McNally. FRAP analysis of binding: proper and fitting. TRENDS
in Cell Biology, 15(2):84-91, 2005.
Brian L. Sprague, Robert L. Pego, Diana A. Stavreva, and
James G. McNally. Analysis of
Binding Reactions by Fluorescence Recovery after Photobleaching.
Biophysical Journal, 86:3473-3495, 2004.
Luca Ferreri and Mario Giacobini
A discrete
stochastic model of the transmission cycle of the tick borne encephalitis virus
Tick borne encephalitis (TBE)
is an emergent zoonosis transmitted by ticks in several
woodland areas of the
TBE is naturally maintained
by a cycle involving hard ticks belonging to the Ixodes
spp. as vectors and mice as hosts animals. In fact,
hard ticks need only one complete blood meal to moult. Furthermore, immature
ticks - larvae and nymphs - usually feed on small vertebrates while adults ticks prefers large mammals. However, the main route
of transmission of the TBE viruses arises from infected nymphs to larvae cofeeding on the same mice.
In this work we try to
formulate a discrete stochastic model that describes the aforementioned
transmission cycle. In particular, we consider a stochastic network contact
structure in order to describe the potential numbers of transmissions from
nymphs to larvae over different months in years. From this mathematical model
we have achieved some interesting analytical results that in the future we
intend to validate by stochastic simulations.
Thijs
Janzen
Diversification
in a dynamic landscape
Allopatric speciation is
often viewed as a slow and gradual process. Over time reproductive isolation is
achieved due to a lack of gene-flow as a result of the formation of a
geographical barrier. Because geographical changes are relatively slow, allopatric speciation is usually not associated with the
generation of, or dynamic changes in, biodiversity. In contrast, environmental
factors such as water level or temperature might rapidly change the
distribution of viable habitat. Here we study the effect of such changing
environmental factors on diversification in a model containing allopatric speciation due to dynamical environmental
factors and sympatric speciation. We use the cichlids in
Eugenia Koblents
Many problems of current interest in science and engineering rely on the
ability to perform inference in high-dimensional spaces. A very common strategy,
which has been successfully applied in a broad variety of complex problems, is
the
An important drawback of the importance sampling approach, and
particularly of PMC, is that its performance heavily depends on the choice of
the proposal distribution (or importance function) that is used to generate the
samples and compute the weights. When the variable of interest is high-dimensional
or the proposal is very wide with respect to the target, the importance weights
degenerate leading to an extremely low number of representative samples.
We propose a novel PMC scheme which is based on a simple proposal update
scheme and introduces a technique which avoids degeneracy of the importance weights
and increases the efficiency of the PMC scheme when drawing from a poorly
informative proposal.
As a practical application of interest we have applied the proposed algorithm
to the challenging problem of estimation of the rate parameters in stochastic
kinetic models (SKM). Such models describe the time evolution of the population
of a set of species which evolve according to a set of chemical reactions and
present an autoregulatory
behaviour. We propose a particularization of the proposed algorithm to SKMs and present numerical results based on a simple SKM,
known as predator-prey model.
Laura Martin
Fernandez, Ettore Lanzarone,
Joaquin Miguez, Sara Pasquali
and Fabrizio Ruggeri
Particle filter estimation in a stochastic
predator-prey model
Parameter estimation and population tracking in predator-prey systems
are critical problems in ecology. In this paper we consider a stochastic predator-prey
system with a Lotka-Volterra functional response and
propose a particle filtering method for jointly estimating the behavioural parameter
representing the carrying capacity and the population biomasses using field data.
In particular, the proposed technique combines a sequential
Michal Matuszak
Application of the Bayesian influence diagram
framework to the ramified optimal transport problem
A tree leaf transports resources like water and minerals from its root
to its tissues. The leaf tends to maximize internal efficiency by developing an
optimal transporting system. That observation can be applied to the, well-known
NP-hard, ramified optimal transport problem, where the goal is to find an
optimal transport path between two given probability measures. One measure can
be identified with a root (source) while the other one with tissues (target).
We will present an algorithm for solving a ramified optimal transport problem within
the framework of Bayesian networks. It is based on the decision strategy
optimisation technique that utilises self-annealing ideas of Chen-style
stochastic optimisation, and uses Xia's formulation for the cost functional.
Resulting transport paths are represented in the form of tree-shaped structures.
Preetam Nandy and Michael Unger
Optimal perturbations for the identification of
stochastic reaction dynamics
Identification of stochastic reaction dynamics inside the cell is
hampered by the low-dimensional readouts available with today’s measurement
technologies. Moreover, such processes are poorly excited by standard
experimental protocols, making identification even more ill-posed. Recent
technological advances provide means to design and apply complex extra-cellular
stimuli. Based on an information-theoretic setting we present novel
Robert Ness
Causal network modeling
for drug target discovery
Development of algorithmic approaches to interpretation of large-scale genetic,
transcriptomic, proteomic, and metabolic datasets is
a key focus of computational biology.
In pharmaceutical research and development, these methods are used to
gain a mechanistic understanding of the biological question of study.
One such method is causal network modelling, a systematic computational analysis
that identifies upstream changes in gene regulation that can serve as
explanations for observed changes in experimental data. These upstream gene regulation events
are identified using a directed interaction network. Different hypotheses for upstream causal
events are compared by using the network model to make predictions for the
observed data, then evaluating the accuracy of the predictions. The common method for making such
predictions is the shortest-paths algorithm, which predicts the regulatory
effect based on the net effect along the edges of the shortest path in the
network between the upstream regulation event and the observed regulation event
in the data.
While the causal network modelling approach is promising, the use of shortest
paths based predictions is flawed.
It ignores the topological complexity that is characteristic of
biological networks, such as feedback loops, which have an essential impact on
net effect. It also only considers
upstream hypotheses concerning individual genes, despite the fact we now know
disease are too complex to target individual targets in isolation.
To address this, we are developing a statistical approach to causal
network modelling. This
incorporates probabilistic modelling of the network can be used to capture
topological complexity and quantify uncertainty, in contrast to shortest paths
algorithm. Further, we can evaluate
upstream regulation events that involve multiple genes, instead of evaluating single-gene
hypotheses separately.
Hossein Farid Ghassem Nia
Bayesian decision making in computer vision with
an approach to industrial automation
Computer vision is becoming the main stream in automation and quality
control industry. In some applications, it is critical to make correct decision
based on the uncertain data from vision systems and draw a conclusion based on analysis
of data and predefined models. In this presentation, we introduce the application
of Bayesian theory in a novel computer vision system in automation industry. In
our research, we used Bayesian theory in image processing and signal analysis
to find region of interest. We also show that how we developed our theory to
analyse mass spectrometry data of melanoma patients. In addition, we are aiming
to demonstrate some on-going challenges in this project regarding minimizing
error in decision making.
Gian
Marco Palamara
Statistical inference for temperature dependent
logistic time series
Methods of parameter estimation are fundamental tools to assess the predictive
power of theoretical models of population dynamics. The use of simple models
like the logistic growth and the ability to infer parameters from time series
data is emerging as a key problem in population ecology. We simulate stochastic
logistic time series from different birth and death processes using the
classical Gillespie algorithm. Logistic growth is the building block of more
complex population models and can be used to test different methods of
parameter estimation. We are able to simulate temperature dependence of the
parameters of the logistic growth (namely growth rate and carrying capacity)
for different birth and death processes. We apply to those simulated time
series data different observation processes based on discrete time sampling of
a typical experiment and on the spatial homogeneity of a population. We then
construct different likelihood functions in order to fit simulated data to
different models.
We find that there is a constant bias in fitting deterministic models to
stochastic data. This bias is based upon the choice of the correct parameterisation
of the variance of the observed data and does not depend on the observation
process we use. Taking into account the observation process we are able to
disentangle the intrinsic stochasticity of the
biological process from the noise induced by the observation itself. Bayesian approaches
are particularly convenient when dealing with incomplete data
Jaroslaw Piersa
Statistical description of functional neural
networks
The aim of the presentation is to briefly discuss mainly statistical tools
for description of large-scale activity-flow graph in artificial neural
networks.
Suppose we are given a recurrent artificial neural network i.e. a set of
neurons connected by synapses, with its stochastic, energy-driven dynamics. During
the dynamics action potentials (or spikes) transmit signals between pairs of
neurons by travelling along the synapses.
These spike travels yield spike- or activity-flow graphs, consisting of
the synapses, which took part in transmitting the information.
Due to the scale of the graphs one must resort to mixed statistical and random-graph-theoretical
approach in order to describe properties of the activation-flow network. Among
the discussed properties we mention average connectivity, empirical degree
distribution, characteristic and maximum (the diameter) path length, clustering
coefficient. Additional features include spectral density, 'small-world-ness
indicator’, resiliency to random damage, graph degeneracy, degree assortativity etc.
The aim of such description is two-fold. First, the properties of the
model seem to be interesting by themselves. Second, the statistical description
can be compared to those obtained from values, reported in medical data of the fMRI brain analyses and, by extension, shed some light onto
the at least some principles of brain work, at least at macroscopic level.
Ihor Smal
Sequential
Time-lapse fluorescence microscopy imaging has rapidly evolved in the
past decade and has opened new avenues for studying intracellular processes in
vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed
efficiently and reliably by means of manual processing. Many popular tracking
techniques exist but often fail to yield satisfactory results in the case of high
object densities, high noise levels, and complex motion patterns. Probabilistic
tracking algorithms, based on Bayesian estimation, have recently been shown to offer
several improvements over classical approaches, by better integration of
spatial and temporal information, and the possibility to more effectively incorporate
prior knowledge about object dynamics and image formation. We propose an improved,
fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image
sequences. It involves a new track management procedure and allows the use of
multiple dynamics models. The accuracy and reliability of the algorithm are
further improved by applying marginalization concepts. Experiments on synthetic
as well as real image data from three different biological applications clearly
demonstrate the superiority of the algorithm compared to previous particle filtering
solutions.
Sofia Tsepletidou
Computational Bayesian tools for modeling the aging process
Whereas the aging process is obvious in macroscopic organisms, it is not
in single celled ones. However, when monitoring the growth of rod-shaped
bacterial colonies, for instance using the model organism E. coli, it is made
possible to recognize an aging mechanism. This is due to the division process,
which splits the cell transversally producing a new end per progeny cell; this
new end is called new pole, whereas the other pre-existing end, old pole. Thus,
the replicative age is defined as the number of
generations elapsed since the old pole arose. The older this pole, the slower
is its growth; thus, more damages are expected to have accumulated –
increased physiological age. However, the replicative
age accounts for a significant, yet limited fraction of the variability
observed in the physiological characteristics. Understanding the impact of the replicative age on the physiological measurements, as well
as, the mechanism with which the cells are rejuvenated, symmetrical or not, is
possible by reconstructing a hidden quantity that would govern the physiology
of the cell while fulfilling basic conservation laws. Estimation is made in
form of exploration of the approximate posterior distribution for the
parameters of the constructed mathematical model. Approximate
Bayesian Computation methods (ABC rejection sampler and ABC MCMC
sampler) are considered in order to avoid the combinatorial cost, as well as,
the difficulty of computing the distribution of the statistics which this study
is relied on. Results show that the method recognizes well the presence and the
absence of asymmetry, but not at a low level.
Giorgio Vacchiano
Modeling
dynamics of forest ecosystems
Forests provide man with relevant goods and services. These include not only
timber and firewood, but also non-wood forest products, protection from hydrogeological hazards, carbon sequestration, recreation
and tourism, habitat for animal and plant biodiversity. However, forest ecosystems
face pressures that may exceed their resistance or adaptation potential
(resilience). On one hand, human-induced climate change is forecasted to impact
photosynthesis, wood production, tree mortality, regeneration, tree species
distribution, soil properties, and frequency and severity of disturbances such
as fire or pest and pathogen outbreaks. On the other hand, changes in land use
brought about by socio-economic processes are affecting forest distribution,
composition and structure even faster than climatic forcing, e.g. by
deforestation (in developing countries) or formation of secondary woodlands
and/or spreading of invasive alien species (in developed countries for the most
part). In order to ensure the
continuity of forest services and the sustainability of forest resources in
face of ongoing changes, scenarios of response to external drivers and
alternative pathways of resource exploitation are needed.
Felix Weidemann
A Bayesian approach to infectious disease
modelling using ordinary differential equations: rotavirus in Germany
Understanding infectious disease dynamics using epidemic models requires
the quantification of several parameters describing the transmission process.
In the context of predictive transmission modelling this quantification is
commonly based on disconnected epidemiological studies to fix as many
parameters as possible in advance. This approach often leads to biased
inference for parameters left to estimate due to dependency structures inherent
in any given model, without sufficiently assessing the uncertainty regarding
those detached assumptions. We developed a Bayesian inference framework that
lessens the reliance on external parameter quantifications due to a data driven
estimation approach. We extended this idea with model averaging techniques with
a focus on the residual autocorrelation, to weaken those estimates dependence
on the underlying model structure. We applied our methods to the modelling of
age stratified weekly rotavirus incidence data in Germany from 2001-2008 using
a complex susceptible-infected-recovered type model taking maternal antibodies,
waning immunity seasonality and underreporting into account. Our results not
only give valuable insight into the transmission processes, but also show the
severe consequences of fixing parameters beforehand regarding the model
predicted dynamics.