Serena Bradde

We will review the following papers:

1) P.-G. de Gennes, Chemotaxis: the role of internal delays. When exposed to certain chemoattractants, bacteria like Escherichia coli move up the concentration gradient nablac with a velocity kappanablac.Microscopically, E. coli moves at constant speed when itrsquos flagellum is rotating counter-clockwise (ccw) and tumbles when the rotation is clockwise (cw). The lifetime of a ccw interval, tau+, is a function of the concentration c(tprime) experienced at earlier times. The corresponding response function was measured long ago by Berg and co-workers. We present here a detailed description of the motion taking place during one ccw interval. This gives an explicit formula relating the chemotactic coefficient kappa to the response function; the formula has some surprising features.

2) A. Celani and M. Vergassola, Bacterial strategies for chemotaxis response. Regular environmental conditions allow for the evolution of specifically adapted responses, whereas complex environments usually lead to conflicting requirements upon the organism’s response. A relevant instance of these issues is bacterial chemotaxis, where the evolutionary and functional reasons for the experimentally observed response to chemoattractants remain a riddle. Sensing and motility requirements are in fact optimized by different responses, which strongly depend on the chemoattractant environmental profiles. It is not clear then how those conflicting requirements quantitatively combine and compromise in shaping the chemotaxis response. Here we show that the experimental bacterial response corresponds to the maximin strategy that ensures the highest minimum uptake of chemoattractants for any profile of concentration. We show that the maximin response is the unique one that always outcompetes motile but nonchemotactic bacteria. The maximin strategy is adapted to the variable environments experienced by bacteria, and we explicitly show its emergence in simulations of bacterial populations in a chemostat. Finally, we recast the contrast of evolution in regular vs. complex environments in terms of minimax vs. maximin game-theoretical strategies. Our results are generally relevant to biological optimization principles and provide a systematic possibility to get around the need to know precisely the statistics of environmental fluctuations.

Principles of microRNA regulation

Debora Marks, Harvard

All animals express microRNA genes, which in turn regulate the expression of the protein repertoire of the organism. These thousands of small RNA genes add another layer of recursive complexity to a seemingly impenetrable molecular circuitry. An important component of microRNA biology has been to quantitate the global scope and extent of their control of gene expression. Analysis methods have concentrated on predicting complementary sequence matches between the small RNA and potential target genes. Although some rules have emerged, functional gene target prediction still remains an unsolved problem. Recent
work illustrates that system level properties of the cells e.g. limiting protein machinery and the target gene abundance are determinants of how much a gene will be regulated by microRNAs or siRNAs. Our overall goal is to develop a mathematical, predictive theory of gene regulatory programs which control the behaviour of cells.

(*) The seminar will take place at the Centro per le Biotecnologie Molecolari (CBM), via Nizza 52, Torino

Systems Biology of Cancer Cells

Chris Sander, Memorial Sloan Kettering Cancer Center (MSKCC)

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations.
Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in non-linearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method we performed drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions.
Possible applications of the combinatorial perturbation approach include the discovery of regulatory interactions, the design of targeted combination therapies, and the engineering of molecular biological networks.

* Seminar at Aula Magna, Politecnico di Torino main building

In vitro evolution experiments and the speed of adaptation

Luca Peliti, Universita di Napoli "Federico II"

A Message Passing Algorithm for Cognitive Radio Networks

Federico Penna

A reliable estimation of the channel occupation probabilities is crucial for the next-generation Cognitive Radio networks, where secondary (unlicensed) users must constantly monitor the spectrum to detect the presence of primary (licensed) users. In this work we propose a novel algorithm to solve this inference problem in a distributed fashion, through an iterative exchange of messages among the network nodes. The network is represented by a probabilistic graphical model that incorporates the individual nodes' measurements, the spatial correlations between pairs of neighboring nodes, and the temporal evolution of the occupation probabilities. Belief Propagation is applied to perform Bayesian inference on the resulting graph. Thanks to the correspondence between graph nodes and physical network nodes, the algorithm is implemented as a Network Message Passing strategy where messages are actual packets sent by network nodes to neighbors.

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