Imaging, Spatial Modelling and Simulation in Genetics and Neurobiology
10:00am - 10:15am Tea & Coffee, Complexity Doctoral Training Centre, Common Room.
10:15am - 11:00am Dendrites: Bug or Feature? Arnd Roth, London.
11:00am - 12:30am Rhythmicity of genetic networks: interplay of feedback, phase repelling quorum sensing, size and stochasticity.
Alexey Zaikin (UCL)
12:30am - 1:30pm Lunch Break, Complexity Doctoral Training Centre, Common Room.
1:30pm - 3:00pm Computational methods for modeling and simulation of spatially extended biological systems within the DUNE framework. Stefan Lang, Heidelberg.
3:00pm - 3:30pm Tea Break, Complexity Doctoral Training Centre, Common Room.
3:30pm - 4:30 Image Processing in the Life Sciences: 3D Segmentation of Neurons and Quality Control of High-Throughput Imagery. Christian Scheelen, Heidelberg
Dendrites: Bug or Feature? By Arnd Roth.
How does dendritic morphology shape the functional architecture
of different types of neurons? Using compartmental models of
reconstructed neurons endowed with the same distribution of active
conductances we isolate morphology as the only variable. We show that
the spread of subthreshold synaptic potentials, the forward- and
backpropagation of action potentials in dendrites, the conditions for
initiation of local dendritic spikes as well as the interaction of
somatic and dendritic action potential initiation sites depend on the
dendritic branching pattern of the neuron.
Rhythmicity of genetic networks: interplay of feedback, phase repelling quorum sensing, size and stochasticity.
By Alexey Zaikin (UCL)
Recent achievements of synthetic biology enabled experiments with relatively simple genetic networks but coupled in complex way. Modelling of such systems enables to sunderstand how collective phenomena emerge from passive intercellular communication and address the question: which mechanisms of intercell communication can be responsible for multirhythmicity of coupled genetic units? Here I will consider systems with an autoinducer intercell communication system, coupled relaxators and coupled repressilators, and show that even very simple units, if coupled in a specific way, can lead to multithythmicity and very complex dynamics. These dynamical regimes should be certainly taken into account for constructing a synthetic genetic networks or for understanding of complex dynamics in natural genetic networks. Noteworthy, these findings are model independent and could be also found in models of coupled neurons and in calcium dynamics.
Computational methods for modeling and simulation of spatially extended biological systems
within the DUNE framework. By Stefan Lang.
The rapid progress of measurement and imaging techniques in life sciences enables
us to investigate spatially extended biological systems with a resolution and
extension that spans from molecular level up to large cell populations.
Many processes of interest can therefore be characterized by observation.
A promising approach to acquire a more detailed functional understanding of the
underlying biophysical principles is to describe the dynamics of these systems
by partial differential equations (pde). Elementary quantities, e.g. energy
or mass, are intrinsically described thereby with conservation laws.
We present methods for modeling and simulation of spatially extended biological
systems in the context of the Distributed Unified Numerics Environment (DUNE).
DUNE is a general framework to model and simulate processes described by
means of pde systems.
Starting from the well-known Hodgkin-Huxley equation as
second order pde describing nonlinear signal propagtion in neurons we introduce
NeuroDUNE, a simulator for large-scale neuron networks.
For biophysical pde models in 3D dimensions an extensive toolset is provided within
DUNE. Typical tasks arising in simulation studies as discretization, flexible handling of meshes
and solution of linear or nonlinear systems are therefore well supported by
DUNE modules. By example guiding principles, structure as well as current extend of major DUNE
modules are illustrated to get insight into a state-of-the-art approach to
pde based modelling and simulation.
Image Processing in the Life Sciences: 3D Segmentation of Neurons and
Quality Control of High-Throughput Imagery
By Christian Scheelen, Heidelberg
In this talk, I will give an introduction to image processing and
present two exemplary biological applications: (i) Serial Block-Face
Scanning Electron Microscopy [Denk and Horstmann 2004] is a promising
means for acquiring high-resolution images of neural structures. In
order to determine the neural connectivity matrix of the brain,
efficient 3D segmentation algorithms are required. I will report a novel
method based on machine learning techniques [Andres et al 2008]. (ii)
For studying viral replication, high-throughput microscopic imagery of
genome-wide siRNA screens is analyzed automatically. However, images
degraded by dirt or acquisition artifacts must be excluded before
performing quantitative analysis. Here, I will present my own work on an
unsupervised learning approach: a one-class Support Vector Machine is
trained on non-defectiveimages, the aim being the detection of such
defective regions without need for user interaction.
This is joint work with Bjoern Andres, Christoph Sommer, Ullrich Koethe,
Fred A. Hamprecht in cooperation with our collaborators (i) Moritz
Helmstaedter, Winfried Denk and (ii) Kathleen Boerner, Maik Lehmann,