One of the main motivations for examining host-pathogen relationships between viruses and cells is for the development of target-specific antimicrobials. Since many aspects of the complex nature of host-pathogen interactions are still enigmatic, even at the most fundamental level, an interdisciplinary and synergistic effort is proposed here to study viral infection and replication in the context of an intact host cell in four dimensions (4D, i.e. in 3D imaging as a function of time) and at a level of resolution never before achieved. Scientists from Geneva (CH), Atlanta (USA) and Norwich (UK) that are still early in their careers with expertise in genetics, live-cell fluorescence imaging, electron cryo-tomography (ECT, 3 dimensional electron microscopy) and mathematical modeling will use a bacterial model system (Caulobacter crescentus) to observe and quantitatively describe how viral particles (bacteriophages) infect and replicate in the live host. The emphasis will be on the mechanisms by which bacteriophages exploit both i) the surface structures on the host for infection (i.e. adsorption, adhesion and genome injection) and ii) the host cell cycle and/or its internal structures (cytoskeleton) for progeny production. C. crescentus is an ideal model system for these studies because it is small enough for whole-cell ECT imaging during bacteriophage infection and replication. Moreover, mutations can easily introduced into C. crescentus and it is easy to enrich for cells that are in a specific cell-cycle phase. These features provide an ideal experimental setting for a marriage of imaging and genetics to define external/internal structures and the cell-cycle parameters of the host that influence bacteriophage infection and replication. These studies, though fundamental in nature and of conceptual relevance for many aspects of biology, could potentially be useful in developing new antiviral treatments.

How does the brain process the complex, dynamic signals that we encounter in the natural environment? And are there different central circuits active for processing the different aspects of sensory stimuli, or are shared neural substrates involved? In the case of vision, we have a handle on these questions, with an understanding of elementary features and their representation in the brain. In the auditory system, especially above the thalamus, the issues are complex and the organizing principles much less clear, with no overall framework yet to guide our understanding of auditory processing. With respect to these questions, songbirds provide a very useful model. They learn to sing using auditory feedback, and show lifelong perceptual learning of other birds’ songs. They possess a hierarchical network of central auditory areas, analogous to those in mammals, which likely subserves these auditory behaviors. The combination of a rich set of behaviorally relevant auditory stimuli and tasks and of an identified neural circuit opens up the possibility of fundamental understanding of how auditory networks function in perception and learning. To this end, we propose an international collaboration to combine neurophysiological recordings of multiple neurons in awake songbirds; new theory-based approaches to extracting complex feature selectivity and analyzing population coding in these neurons; and state-of-the art anatomical techniques to map the connectivity of neurons at electrophysiologically identified sites and to create a functional wiring diagram. Our work will be guided by, and test the following hypotheses: a) there is a systematic and hierarchical organization of feature response properties across the avian forebrain, including specialized pathways for different aspects of complex sounds, such as frequency and time, b) the collective activity of neuronal populations across these areas reveals a further organization of sound encoding not evident in the properties of neurons considered individually, and c) this network can be dynamically reconfigured by bottom-up influences (such as stimulus statistics), top-down influences (behavioral task), and learning (of adult song discriminations). Our goal is a principled picture of how this circuit is organized to analyze and learn complex natural sounds, with potential relevance to all vertebrates.

Sedentary animals attach to substrates with glues that work underwater, a feat we do not master in our technology. A well-known example is the blue mussel who uses its beard, called the byssus, to attach the soft mussel tissue to the hard substrates it lives on. The blue mussel byssus is made of protein. In contrast to the multi-thread pure protein byssus of the blue mussel, its cousins called the Anomiidae attach via a byssus plug made from a single thread that is calcified, i.e. contains calcium carbonate crystals in addition to proteins. Almost nothing is known about this strange attachment system and in particular the adhesive. We will investigate this intriguing solution to sticking in place using an interdisciplinary approach where we will understand both the mechanical function and the biomolecules involved in the adhesion. This is done by joining the forces of materials chemists and biochemists.

In the project we will use state of the art tools to investigate the structure on the 1 nm to 10 µm length scale of the byssus with focus on the adhesive. In this range of distances molecules interact with each other and develop structures that have macroscopic functions. We will measure the strength of the byssus attachment system. By prodding the surface with a sharp needle, a technique called nanoindentation, we will measure the local mechanical performance on the µm scale. Thereby we will unravel the mechanical performance of the different structural elements. In particular, we can measure the mechanical properties of the adhesive layer itself. Through these experiments we will understand the function of the byssus but also how this function comes about. To do this we will investigate which molecules are involved and how they are built up from amino acids. We will also determine if the animal uses specially modified amino acids, as is sometimes the case in such systems in particular in the blue mussel. To fully understand how these biological glues function, we will measure the structure and self assembly capabilities of selected molecular moieties. We will also measure how they interact with specific surfaces to unravel their role in the adhesive process.

Through this interdisciplinary project we will develop a deep understanding of how these animals attach. This may provide inspiration for novel adhesives for use in e.g. biomedical applications.

Many times a second we engage the motor system: we evaluate evidence, plan options, decide, and act, all within 10’s of ms. Given fixed times for nerve conduction and synaptic delays there would appear to be little time for processing through multiple circuits. However, both primates and cats appear to have widespread and relatively unstructured lateral connectivity within their motor cortex. Such extensive local motor cortex connections may endow the system with flexibility needed to learn new tasks and coordinate a wide range of outputs. Our central question is how the local circuits in the primary rodent motor cortex process sensory information to produce a coherent, integrated, and specific pattern of corticospinal output. When faced with these less structured circuits we exploit genetics and develop mouse models where specific classes of neurons can be selectively excited or inhibited using light over both the timescale and landscape of the cortical area devoted to sensory-motor processing. The primary focus will be on the in vivo structure and function of single neurons and the local circuits they form in the rodent motor cortex M1, specifically in the forepaw representation that are associated with simple reaching tasks. Our hypothesis is that the motor cortex implements a decision making machine that operates in two main stages through the cortical lamina. The widely distributed connections in the superficial layers provide the circuits for broadcasting incoming patterns of sensory information for evaluation. The resulting decisions are then transmitted to the deep cortical layers, which coordinate the output patterns. While the basic input pathways and output neurons in the rodent forepaw representation of M1 are known, there are few data on functional properties of individual neurons, laminar differences in properties, or their collective activity. It is intended that the experiments outlined here will provide the functional and structural data for us to develop a biologically-realistic model of the mouse motor cortex.

In response to nutrient starvation, yeast cells enter a quiescent state. This state is defined by a marked increase in the viscosity of the cell cytoplasm, a phenomenon we term “cell-freezing”. Here we propose a detailed structural and functional study of cell-freezing in fission yeast (S. pombe). Cell-freezing is a novel phenomenon discovered only recently through measurements of the viscoelastic properties of the cytoplasm of fission yeast cells. It leads to a dramatic immobilization of all visible sub-cellular structures and implies the presence of a mechanically stable network throughout the cell, we thus hypothesize that such a dense and homogeneous network develops when the cell enters the starvation state and that this network is sufficiently fine to immobilize all major cell components.

The four objectives of the proposed research are A: characterize details of cell-freezing, B: identify and dissect its molecular machinery, C: analyze its function, and D: search for cell-freezing in higher organisms. To address these objectives, we will follow an interdisciplinary approach that includes methods from cell and molecular biology, structural biology and (bio-)physics.

Somatic nuclei can be reprogrammed to a pluripotent or embryonic stage. Somatic cell reprogramming has been shown to be possible via animal cloning, cell fusion, and ectopic expression of a few embryonic stem cell (ESC) factors. The reprogramming time of a cell nucleus is normally relatively long and it depends upon cell type, methods of reprogramming, and culture conditions used. These observations led us to envisage that the process according to which somatic cells change their fate involves many factors and sequential events. Thus, transcription factors, chromatin-modifying complexes and signalling molecules are likely to operate in a specific order to induce and maintain the reprogramming of a somatic nucleus. We have recently shown that the periodic activation of Wnt/beta-catenin signalling controls fusion-mediated somatic-cell reprogramming. Only this periodic and limited nuclear accumulation of beta-catenin allows ESCs to reprogramme somatic cells after fusion. Furthermore, in ESCs, either overexpression of Axin2, which is part of the beta-catenin destruction complex, or deletion of GSK3, which is devoted to the phosphorylation of beta-catenin, inhibits ESC-fusion-mediated somatic-cell reprogramming. In contrast, deletion of a specific beta-catenin modulator, in ESCs strongly enhances their reprogramming ability. Our hypothesis is that signalling dependent genes encode for key reprogramming factors, which we aim to identify. We will perform transcriptome, microRNAome and epigenome analyses of ESCs pre-treated with positive and negative modulators of the Wnt pathway. In addition, we will perform the same type of analysis using wild-type and mutant ESCs which carry the activated or inhibited Wnt signalling pathway. All of the collected data will be analysed by ARACNe, MINDy and MRA reverse engineering algorithms, to reconstruct the reprogramming regulatory network and to identify pathways and master reprogrammer genes. Finally, the reprogrammer candidates will be cloned and validated for their ability to induce fusion-mediated reprogramming or direct reprogramming.

Cycle-Quant: Developing Cell Cycle Profiles to Classify Responses and Regulators

Readily detectable changes in DNA content and cellular morphology are used to assign conventional cell cycle phases. However, cell division requires coordination of cell size, organelle duplication, cytoskeletal rearrangements, and changes in key proteins. Here, an interdisciplinary team of researchers will develop new methods for advanced cell cycle profiling. Novel landmarks will be identified in the continuum of the mammalian cell cycle, and in cycles of organelles and cell cycle proteins. An integrated and multi-disciplinary team of biologists, chemists, and computer scientists will establish new genetic and chemical cellular labels and machine learning-based computational techniques applicable to time-lapse microscopy.

The main aims are:

1. To develop advanced cell cycle profiling using a standardised and expandable set of non-transformed human cell lines. Different classes of genetically encoded fluorescent biological markers track, directly or indirectly reflecting cellular changes related to the cell cycle, will be tracked by time lapse fluorescence microscopy. Acquired image data will be processed by new computer-based data extraction methods for automated image recognition and adequate description of a spectrum of ‘sub-cellular cycles’ such as those of dividing organelles.

2. To develop novel, and uniform tools to identify flexible key cell-cycle landmarks, bypassing the requirement for the elaborate, intrusive introduction of genetic markers. This would create unprecedented flexibility for cell cycle profiling in any given cell type. We will a) use rich information from non-fluorescent cell images and, b) develop and validate permeable fluorescent cell-cycle-markers.

3. We will test cell cycle profiling by quantitatively comparing functions of key regulators of the G2-M transition and classify new regulators in focussed pathways.

We will combine our expertises to re-define cell cycle progression in a quantitative way, with the goal of assigning complex cell-cycle progression profiles. This coordinated effort will provide unprecedented opportunities to quantitatively compare genetic and chemical perturbants, essential for assigning functions to newly identified cell cycle regulators, and the development of robustly predictive models of cell cycle progression.

Stem cells possess two defining features; the capacity to maintain their identity during extended cell division (referred to as self-renewal) and the capacity to alter their identity to form specialised cells (referred to as differentiation). Preservation of a functional stem cell population requires a balance between self-renewal and differentiation. Although functionality operates on the stem cell population as a whole, it is the choices of individual cells that must be controlled to maintain such functionality. Self-renewal of embryonic stem (ES) cells is controlled by the action of a gene regulatory network centred around the transcription factors Oct4, Sox2 and Nanog. However, the mechanisms controlling operation of this network are not well understood. Recently, we demonstrated an heterogeneity in the expression of Nanog, which is strongly and directly correlated with self-renewal efficiency. This leads us to hypothesise that the cross-regulatory interactions between Oct4, Sox2, Nanog proteins and their target genes are only partly stable. This may allow a dynamic range of states to be explored by ES cells, only some of which enable differentiation. Here, our objective is to test this hypothesis by watching how the Oct4/Sox2/Nanog transcription factor network operates in individual stem cells. To do this we will put differently coloured “reporters” into each of the transcription factor genes (e.g. red at Oct4, blue at Sox2 and yellow at Nanog). These reporters will tell us when, where and to what extent each gene is switched on. We will then use precision engineering to allow ES cells, the developmentally more advanced epiblast stem cells and early mouse embryos to be cultured in small chambers with a surface area of less than 1 mm2 over which we can flow culture fluid with varying levels of “switch” chemicals. These “switch” chemicals will be used to alter the action of modified transcription factor genes that we have also inserted into the cells. Changes in expression of the network components can then be followed microscopically using the coloured reporters in real time. These studies will deliver insight into the regulatory dynamics underpinning stem cell identity. They will allow us to see the dynamic changes as they occur in stem cells that are maturing during development and will enhance our ability to manipulate stem cells behaviour at will.

Pheromones are chemicals that are produced by individual organisms, and that elicit highly stereotyped behavioral and developmental responses in other individuals of the same species. The chemical composition of pheromones is often complex, consisting of many small molecules of related or unrelated structures. These molecules can elicit distinct responses when acting individually or in combination with other pheromone components. Pheromones are recognized by members of large families of seven transmembrane domain receptor proteins in vertebrates and invertebrates, the ligand specificities of only a small subset of which have been described. The challenge of isolating and defining the chemical composition of pheromones, together with the presence of large numbers of putative pheromone receptor proteins, has made it difficult to describe the coding strategy used to respond to these biologically and ecologically important signaling molecules.

The goal of this project is to generate a comprehensive map of pheromone-receptor interactions in the C. elegans model system. Over twenty-five years ago, it was demonstrated that C. elegans produces a mixture of small molecules called dauer pheromone, high concentrations of which trigger entry into the stress-resistant and long-lived dauer larval stage. Recently, we and others have begun to elucidate the chemical composition of dauer pheromone, and have identified two G protein-coupled receptors required for the responses to a subset of pheromone components. The ability to cultivate C. elegans in sufficiently large quantities to extract, identify and synthesize pheromone components, and the genetic and genomic power of C. elegans together with its compact sensory nervous system, makes the generation of a complete pheromone-receptor interaction map a feasible goal in this organism.

The specific aims of this collaborative project are: 1) to define and chemically synthesize all components of C. elegans dauer pheromone; 2) to identify the neurons and chemoreceptors required for the responses to each of the components in vivo; and 3) to characterize the ligand response properties, selectivity, and affinity of the receptors via heterologous expression assays. These interdisciplinary aims integrate the expertise of J. Clardy (USA) in small molecule chemistry, of P. Sengupta (USA) in neurogenetics of C. elegans, and of K. Touhara (Japan) in biochemical mapping of ligand-receptor interactions.

Neurons have to perform a wide variety of complex morphogenetic events in order to functionally wire the developing brain. In the adult brain, this process continues with changes in morphology that alters their functional connectivity upon learning and experience, or during the repair of injured neural tissues. Specific signaling events to the cytoskeleton are essential for the control of these processes and are likely to be highly regulated at fine spatiotemporal scales. Accordingly, novel cell biological tools such as fluorescent biosensors that enable dissection of these signaling events in time and space, have given novel insights in signaling complexity. The aim of this proposal is to visualize the dynamics of the activation of specific signaling molecules in a panel of prototypical neuronal behaviors with a strong emphasis on the axon. We will study: axonal specification (the establishment of an axon and a dendrite), axonal guidance (the response of a growth cone to attractive and repulsive cues) and inhibition of axonal regeneration after injury (the response of the axon to a glial scar). Our hypothesis is that these different morphogenetic behaviors involve distinct signaling programs with a tight crosstalk between the signaling molecules of interest. Comparing these in the prototypical cell behaviors should give essential insights in how a neuron can perform this diverse set of tasks using the same signaling machinery. We propose to engineer a new generation of highly sensitive fluorescent probes to monitor the spatio-temporal activation profiles of the specific signaling molecules in primary E18 embryonal hippocampal neurons. Microfluidic technology will be used to engineer highly defined extracellular environments to induce and orient the different axonal behaviors. This will evoke the asymmetric nature of extracellular cues observed in vivo, and recapitulate the associated signal amplification mechanisms that will lead to robust signaling. Finally, computer vision techniques will be devised that allow the pooling of measurements from many cells into statistically significant datasets for identification of the spatiotemporal relationships among different signaling programs and cell behavior. Multivariate time-series analysis methods will be devised to inform the model on the cross-coordination between the activation of the signaling molecules of interest and neuronal morphogenesis.

The architecture of cells and subcellular structures can show remarkable variability between tissues and organisms, but there is currently little understanding of the evolutionary basis of this diversity. Thus it is unclear why metaphase spindles in different Eukaryotes exhibit a range of morphologies and the volumes of these spindles vary over one thousand fold. Here we propose a comparative study to investigate the evolutionary forces shaping the spindle. Using one cell stage nematode embryos as a model system, we will characterize spindle structure in several strains of ~60 species exhibiting a range of cell sizes, karyotypes, and life histories. A more detailed analysis of microtubule dynamics and spindle architecture from ~10 select species will reveal the biophysical basis of variation in spindle morphology. This comparative data will be interpreted using phylogenies and quantitative models of phenotypic evolution. We will then test the developed biophysical and evolutionary models with perturbation. This unique comparative approach combines molecular methods, quantitative microscopy, and mathematical models, to gain insight into how the mitotic spindle is shaped through evolution by selection, drift, and biophysical constraints.

Our goal is to examine deliberative decision-making in rats from neurophysiological, cognitive, and mathematical perspectives. Deliberative decision-making entails behaviors in which agents explicitly consider multiple possibilities before acting. Early experiments identified behaviors in which a rat paused, looking back and forth, before acting (Tolman1948). This behavior occurred during learning and after changes in experimental contingencies, and was suggested to create expectancies of the consequences of the available choices, but the computational and experimental techniques needed to address this question were not available at the time. An interest in this behavior has been revived in the past few years based on new computational models of decision-making systems (Daw2005, Niv2006, Redish2007a, Redish2008), on the availability of new behavioral measures (e.g. Lauwereyns2006), and on recent discoveries of prospective encoding in the hippocampus (Wood2000; Johnson2007a) and other structures (Ramus2007, vanderMeer2009, vanDuuren2009). Our team brings together expertise in behavioral neuroscience, neurophysiology, computational neuroscience, cognitive psychology, and mathematics. Experiments will examine prediction, evaluation, and action-selection mechanisms in rats making deliberative decisions. Computational projects will examine mechanisms by which these systems can shift suddenly from consideration of one possibility to the other, based on models of chaotic attractors (Tsuda2001a).

The aim of this project is to use mouse Embryonic Stem (ES) cells and preimplantation embryos, from which ES cells are derived, to understand how interactions between signal transduction pathways and gene regulatory networks regulate processes of cell fate assignment. We propose to take advantage of the operational simplicity of these developmental systems to test whether fate transitions are guided by digital additive genetic systems or by analog dynamical integrative devices. Specifically we shall focus on the emergence of the pluripotent epiblast (EPI) from a precursor population, the Inner Cell Mass (ICM). Ultimately we shall try to recreate this process in culture and thus determine the parameters that influence the decision to be one or the other cell type.

The brain is a constantly adapting organ, with connections between brain cells and the physiology of individual cells changing to reflect learning and life events. This dynamic reshaping is a critical element of human behavior and disease, but it has been difficult to study at the molecular level because, for most important ‘signaling’ molecules in brain cells, there are no tools to alter molecular dynamics in real time as changes in brain shape and interactions are taking place. In a recent development, Hahn lab demonstrated a new molecular engineering approach capable of rendering the activity of signaling molecules sensitive to light, and therefore subject to study during dynamic events. A protein fragment used by plants to turn their leaves towards the sun was engineered into a completely unrelated protein, a ‘small GTPase’ used by cells to control shape and movement. The fragment of light-sensitive plant protein could be used to control the activity of the GTPase. This enabled photomanipulation of protein activity in living cells, and was quite valuable in studies of metastasis and cell movement. Perhaps more importantly it promises a new general approach that can potentially make light sensitive versions of many molecules for their study in living cells. In this proposal, we combine the forces of a theoretician focused on understanding how protein structures can be altered to generate new function, Dr. Kuhlman, with the cell biologist and protein biochemist who developed the photoactivatable GTPase, Dr. Hahn. These two will devlope new caged proteins capable of shedding light on important and previously inaccessible questions in neurobiology. This will be accomplished via the development of generally applicable methods that can be used by other researchers to control protein activity with light. The selection of target proteins was guided by Dr. Kasai, a pioneer in the role of neuronal dynamics in brain function. He will use the new photoactivatable proteins to study brain tissues both in vitro and in vivo. The new effective PA-probes produced by our collaboration will have wide applications in various fields of biology, as these small GTPase are key to diverse cellular functions and diseases.

Synapses are highly complex and dynamic sites of intercellular communication, playing a pivotal role for learning and memory formation in the mammalian brain. As they are very small - usually smaller than what diffraction-limited light microscopy can resolve - and densely packed in light-scattering brain tissue, it has been extremely difficult to study their physiology in mechanistic terms. As a result, we still lack a clear understanding of the basic dynamic organization of neurotransmitter receptors and their molecular partners at mammalian synapses. While electron microscopy provided us with detailed snapshots of where glutamate receptors are located inside synapses, it does not convey dynamic information about their function. Therefore, a functional assay is required that non-invasively probes receptor localization and activation with nanoscale resolution. Since existing optical approaches such as 2-photon glutamate uncaging do not have sufficient spatial resolution, progress in this area relies on fundamental breakthroughs in live-cell-compatible techniques relying on focused visible light.

We seek to establish a new paradigm for controlling physiological processes, which reconciles instant non-invasive molecular control with nanoscale resolution in arbitrary locations inside a cell. Specifically, we will (1) engineer photosensitive compounds for superresolution-based photo-activation, (2) advance microscopy technology to concurrently activate and image beyond the diffraction limit, and (3) use this new methodology to probe synaptic physiology in brain slices with unprecedented resolution, addressing timely questions about the dynamic behavior of neurotransmitter receptors in synapses.

After breaking the diffraction barrier in imaging, diffraction-unlimited photomanipulation will be the next paradigmatic step in neuroscience methodology. Substantially departing from the state of the art, the project brings together three research groups working at the cutting edge of probe development, superresolution optics, and neuroscience in order to reach a goal that offers a huge potential for scientific breakthroughs. Beyond addressing key questions about the molecular physiology of synapses, the project will have a substantial impact in many other areas where the arbitrary release of molecules with nanometer spatial resolution will provide a paradigmatic shift.

For any protein to function properly, the polypeptide chain produced by the ribosome, the protein-synthesizing factory of the cell, has to fold into the correct three-dimensional structure. Protein folding in the cell starts while the growing peptide chain is still bound to the ribosome. The information on the sequence of amino acids of the protein is stored in the sequence of codons in the messenger RNA (mRNA). The ribosome reads the mRNA codons one by one and translates them into the sequence of amino acids of the protein. Not all codons are read with the same speed: periods of rapid translation are separated by pauses. The role of such pauses is not known, but interfering with a fine-tuned rhythm of translation causes incorrect folding of proteins which can lead to disease. Translation rates may be changed by synonymous mutations in the mRNA, i.e. mutations that do not change the meaning of a codon in terms of which amino acid to be incorporated, but affect the speed by which the codon is translated. The goal of this project is to understand how nascent proteins fold while being synthesized on the ribosome and how the speed of translation affects their folding. Fluorescent reporters introduced at defined positions in the nascent protein will allow us to distinguish the unfolded, partially folded, and fully folded protein as it is produced by the ribosome. By determining, in real time, the hierarchy by which the structural elements in the protein are formed, we will delineate the protein´s co-translational folding pathway and compare it to the folding properties of the completed protein when it is released from the ribosome. By introducing synonymous mutations in the mRNA, we will examine how translational pausing affects folding. We will also determine the structure and dynamics of independent folding units in the growing peptide. The work requires an interdisciplinary approach that combines the expertise in molecular biology, biochemistry, biophysics, and structural biology. The results will provide important insight into the mechanism of protein folding in the cell. Furthermore, the work will contribute to better understanding the origin of human diseases caused by incorrect protein folding and give a tool to upscale the production of functional proteins for medical and biotechnological purposes.

Many clinically important antibiotics are peptide natural products of great structural complexity made by bacteria or fungi. The biosynthesis is achieved by nonribosomal peptide synthetases (NRPS), in which catalytic subunits and carrier domains form an assembly line-like mega-enzyme and interact in a coordinated way to achieve a directed product assembly via covalently bound intermediates. As such, a fundamental mechanistic understanding of these remarkable molecular machines will have direct implications in the rational design of novel synthetic pathways by biocombinatorial approaches. Large-amplitude conformational transitions have been identified and are likely to play a central role in the apparent directionality of the stepwise peptide assembly within the NRPS. Two conceptually distinct hypotheses have been proposed for triggering the conformational changes: They could be driven by chemical-reaction directed interactions between the individual domains with well-defined protein conformations or, alternatively, they could be promoted by Brownian random movements that eventually lead to productive interactions. Using these two still highly speculative models as guide, this proposal is intended to dissect the multi-step enzymatic reactions and clarify the mechanistic roles of large-amplitude conformational transitions. Specifically, we aim (1) to monitor the sub-domain dynamics in the adenylation domain, (2) to monitor the domain-domain interactions between the adenylation and the peptidyl carrier domain, and (3) to monitor the dynamical domain coordination of a minimal NRPS in real time. We will apply single-molecule FRET spectroscopy to this kind of multi-domain mega-synthetases for the first time. We will also develop new techniques for site-specific protein labeling and new data-driven statistical analysis methods to interpret the data. An international research consortium consisting of leading research groups in chemical biology (Germany), single-molecule spectroscopy (US), and mathematical physics (Japan) is formed to tackle this challenging yet fundamentally important problem. This will be a new research direction for all three participating labs and it will be impossible for any one of the groups to achieve the aims working alone.

Object recognition is a difficult computational task solved by the nervous system. For a wide range of organisms object recognition occurs predominantly using the chemical senses. Behaviorally significant odor sources must be faithfully recognized despite being composed of an often complex chemical signature and being embedded in a complex and dynamic chemical background. The present proposal aims to understand the neural mechanisms that make such computations possible.

Increasing evidence suggests that object recognition can be understood as a process of probabilistic (Bayesian) inference. Although this idea is already being exploited in the visual system, it has yet had little impact on the field of olfaction. Our central hypothesis is that odor stimuli are represented using a probabilistic population code, and that odor representations are processed in Bayesian optimal way by the nervous system. This problem requires at least two computational processes: integration, which underlies recognition of multi-component odor sources, and marginalization, which is essential for invariance to irrelevant background odors.

Using this probabilistic framework, we will develop computational models for integration and marginalization in the olfactory system and test them using parallel experiments in rats and in Drosophila larvae. The rat will provide a model system that can be probed using psychophysics and multi-electrode recordings, allowing us to study the neural basis of marginalization in populations of olfactory cortex neurons. The Drosophila larva will provide a model system with a nervous circuit amenable to genetic manipulations: using chemotaxis as a behavioral paradigm, we will using molecular perturbations to decompose olfactory input into elementary parts mediated by single odorant receptors.

The probabilistic framework will be used to design and interpret experiments which will themselves inform refinement of the theoretical hypotheses. By comparing across species, we will test for the existence of conserved, and possibly optimal, solutions to ubiquitous problem of the odor recognition in naturalistic olfactory environments. This project is based on computational and systems neuroscience and includes strong contributions of the fields of mathematics and statistics (Pouget), biophysics and fly molecular biology (Louis), psychology and physiology (Mainen).

Two key challenges in biology are to understand how biological forms are generated, and to elucidate the basis for their diversity. We propose to address these problems by studying the development and evolutionary diversification of leaf forms. To this end, we will integrate developmental genetics and computational modeling to compare, contrast, and put in an evolutionary context the mechanisms that shape the simple leaf of the model plant Arabidopsis thaliana and the compound subdivided leaf of its close relative Cardamine hirsuta. In addition to being taxonomically related, these plants show exceptional genetic tractability, thus providing excellent opportunities for comparative studies of leaf development and diversity.

We will characterize the spatiotemporal expression of key leaf-shape regulating genes and the activity of auxin during leaf development, and the influence of these genes on cell division, tissue growth and vein patterning. On this basis, we propose to devise models of A. thaliana and C. hirsuta leaf development. These models will integrate molecular-level signaling, the pattern of growth and division of individual cells, mechanical interactions between cells, and vein pattern formation. We will iteratively test, improve and validate the models by comparing them with experimental data for A. thaliana, C. hirsuta, and their mutants and transgenic variants. To conceptualize the diversity and evolution of leaf forms we will construct a morphospace with coordinates reflecting the activity of selected genes.

We expect to gain an understanding of the development, diversification, and evolution of leaf shapes in mechanistic terms including genetic regulation, developmental signaling, cell division and differentiation, and tissue growth. We will also advance the state of the art in plant modeling by introducing the propagating-boundary method to simulate development of organs, devising a representation of cellular structures developing in 3D, and modifying finite-element methods to efficiently capture the dynamics of growing 2D and 3D tissues and structures. All aspects of the proposed research have a collaborative, interdisciplinary character, bridging developmental biology and computational modeling.

Adaptation from standing genetic variation is central to the evolution of phenotypes, yet its dependence on the molecular details that connect genotype, phenotype, and fitness is a major unsolved problem in genetics. The effect of recombination, in particular, is expected to depend on the extent of genetic interaction among alleles. While higher rate of recombination facilitates combinatorial exploration of the genotypic space, in the presence of interaction it also has a detrimental effect of breaking up synergistic combinations of alleles. We propose a multidisciplinary investigation of the roles of recombination and genetic interactions. We will study laboratory adaptation of C. elegans populations that start with high genetic variability and are engineered to have different rates of inbreeding and outcrossing. The project integrates experimentally determined genome-wide genotype distributions, multidimensional phenotype (gene expression) measurements, and fitness data across 100 generations of culture in laboratory populations that differ in their rates of outcrossing. This integration forms the empirical test bed for theoretical models of the role of genomic architecture in adaptation. In particular, we will test the predicted positive correlation among non-additive genetic effects and levels of linkage disequilibrium across the genome. The proposed work combines population genetics and high-throughput molecular biology approaches with statistical physics modeling methods, and aims to bridge population genetics with systems biology.