Speakers at CMSB 2017
Stephan Grill (Technische Universität Dresden, Germany)
Control of mechanochemical self-organization during cell polarization
Biological pattern formation often relies on self-organization, integrating chemical with mechanical patterning processes. Upstream guiding cues ensure that patterns form at the right time and in the right place, but how such guiding pre-patterns control self-organization remains unexplored. Here we investigate PAR polarity establishment in the Caenorhabditis elegans zygote, by combining measurements of the spatial distribution of protein numbers and fluxes with a physical theory. We characterize the handover from a pre-pattern to mechanochemical self organization, and find that guiding cues from the centrosome steer the patterning system comprised of PAR proteins and the actomyosin cortex beyond a transition point at which the patterned state becomes self-organized. This mechanism of controlled pattern formation integrates mechanical and molecular aspects of biological pattern formation with guiding cues.
Russ Harmer (CNRS & École normale supérieure de Lyon, France)
Bio-Curation for Cellular Signalling: the KAMI Project
The general question of what constitutes bio-curation for rule-based modelling of cellular signalling is posed. A general approach to the problem is presented, based on rewriting in hierarchies of graphs, together with a specific instantiation of the methodology that addresses our particular bio-curation problem. The current state of the ongoing development of the KAMI [Knowledge Aggregator & Model Instantiator] bio-curation tool, based on this approach, is detailed along with our plans for future development.
Philipp Hennig (Max Planck Inst. for Intelligent Systems, Tübingen, Germany)
Probabilistic Numerics — Uncertainty in Computation
The computational complexity of inference in statistical models is dominated by the solution of non-analytic numerical problems (large-scale linear algebra, optimization, integration, the solution of differential equations). But a converse of sorts is also true — numerical algorithms for these tasks perform statistical inference! They estimate intractable, latent quantities by collecting the observable result of tractable computations. Because they also decide adaptively which computations to perform, these methods can be interpreted as autonomous inference agents. This observation lies at the heart of the emerging topic of Probabilistic Numerical Computation, which applies the concepts of probabilistic (Bayesian) inference to the design of algorithms, assigning a notion of probabilistic uncertainty to the result even of deterministic computations. I will outline how this viewpoint is connected to that of classic numerical analysis, and show that thinking about computation as inference affords novel, practical answers to the challenges of large-scale, big data, computational statistical inference.
Lea Popovic (Concordia University Montreal, Canada)
Calculating rare events in stochastic reaction networks
This talk will present the mathematical tools for calculating the probabilities of rare events in stochastic reaction networks. Using tools for Markov processes we can describe the mean and the variability of molecular amounts of different species as they evolve over time, even for models that involve multiple-scales. Theory of large deviations goes one step further to assess events not captured by such law of large number or central limit theorem results, whose probabilities are exponentially small. We give examples where such events are key for the dynamics of the reaction networks (e.g. bistability). In the case that reaction dynamics is on multiple scales calculating such events is technically much more challenging. However, analytical results can also be very useful in designing efficient algorithms for simulating such rare events.