601 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Multiscale modeling of synthetic and biological supramolecular systems.
In this thesis, we exploited the synergistic combination of multiscale molecular modeling, molecular dynamics (MD), and enhanced sampling to tackle two complex systems. In the first case study, we investigated the intrinsic dynamic behavior of a Benzene 1,3,5-TricarboxAmide (BTA) supramolecular polymer in water. In the second case study, we inquired about the effect of functionalized amphiphilic gold nanoparticles (Au NPs) on the phase behavior of a multi-component lipid membrane. Through our simulations, we gained a deeper understanding of the structure and dynamics of a class of supramolecular polymers. Additionally, we identified the factors that control the exchange of monomers between the different fibers, which can be used to inform the design of novel supramolecular materials in the future. Our simulations provided insights into the mechanisms underlying the interaction between functionalized nanoparticles and lipid membranes, extrapolating the factors that influence the stability of the membrane phase separation. The acquired knowledge can be applied in drug delivery systems or to create new hybrid materials containing ordered two-dimensional NP lattices.
In particular, it is worth noting that in both studies, using coarse-grained models with the proper (sub-molecular) resolution was crucial to overcoming the limitations of classic all-atom force fields while maintaining the needed chemical specificity. Overall, the results of these studies have broad implications for materials science and biophysics and demonstrate the potential of computational modeling to inform the design of novel materials and systems
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Endogenous UMIs as quantifiable reporter elements – validation studies & applications in rAAV vectorology
In the creation of recombinant adeno-associated viral (rAAV) vectors, terminal DNA elements known ITRs (inverted terminal repeats) of the direct the intracellular synthesis and packaging of nonviral DNA. The need to clonally amplify ITR sequences in one form or another thereby underlies the existence of all rAAV clinical products and research materials worldwide. Their tendency to form strong nonduplex structures raises problems. The genetic precursors to rAAV vectors – typically prokaryotic plasmids – are known to possess heterogenous ITR sequences as a result of replicational instability, the effects of which on vector yield and efficacy are unclear and have not been systematically explored. To shed much-needed light on this decades-old problem, I utilised unique molecular identifiers (UMIs) as reporter elements for different rAAV plasmid preparations, so that massively parallel sequencing could be used to analyse their DNA and RNA derivatives through the course of production and in vivo gene transfer. The range of vector potencies observed, while not calamitous, definitively erases the notion that this problem can be further overlooked.
The success of this unconventional strategy proved to be an equally notable outcome, offering unprecedented insights into population kinetics, and achieving quantitative consistency between biological replicates comparable to q/dPCR measurement replicates of single samples. This triggered concerted efforts to formally investigate the capabilities of UMIs used in this fashion. The probabilistic principles underlying the technique were formalised and empirically validated, confirming precision capabilities akin if not superior to dPCR and qPCR at equivalent levels of stringency. Experiments also revealed a pattern of measurement bias with potentially adverse implications for other areas of count analysis including differential gene expression
Functional connectivity and dendritic integration of feedback in visual cortex
A fundamental question in neuroscience is how different brain regions communicate with each other. Sensory processing engages distributed circuits across many brain areas and involves information flow in the feedforward and feedback direction. While feedforward processing is conceptually well understood, feedback processing has remained mysterious. Cortico-cortical feedback axons are enriched in layer 1, where they form synapses with the apical dendrites of pyramidal neurons. The organization and dendritic integration of information conveyed by these axons, however, are unknown. This thesis describes my efforts to link the circuit-level and dendritic-level organization of cortico-cortical feedback in the mouse visual system. First, using cellular resolution all-optical interrogation across cortical areas, I characterized the functional connectivity between the lateromedial higher visual area (LM) and primary visual cortex (V1). Feedback influence had both facilitating and suppressive effects on visually-evoked activity in V1 neurons, and was spatially organized: retinotopically aligned feedback was relatively more suppressive, while retinotopically offset feedback was relatively more facilitating. Second, to examine how feedback inputs are integrated in apical dendrites, I optogenetically stimulated presynaptic neurons in LM while using 2-photon calcium imaging to map feedback-recipient spines in the apical tufts of layer 5 neurons in V1. Activation of a single feedback-providing input was sufficient to boost calcium signals and recruit branch-specific local events in the recipient dendrite, suggesting that feedback can engage dendritic nonlinearities directly. Finally, I measured the recruitment of apical dendrites during visual stimulus processing. Surround visual stimuli, which should recruit relatively more facilitating feedback, drove local calcium events in apical tuft branches. Moreover, global dendritic event size was not purely determined by somatic activity but modulated by visual stimuli and behavioural state, in a manner consistent with the spatial organization of feedback. In summary, these results point toward a possible involvement of active dendritic processing in the integration of feedback signals. Active dendrites could thus provide a biophysical substrate for the integration of essential top-down information streams, including contextual or predictive processing
Microscopy of spin hydrodynamics and cooperative light scattering in atomic Hubbard systems
Wechselwirkungen zwischen quantenmechanischen Teilchen können zu kollektiven Phänomenen führen, deren Eigenschaften sich vom Verhalten einzelner Teilchen stark unterscheiden. Während solche Quanteneffekte im Allgemeinen schwierig zu beobachten sind, haben sich ultrakalte, in optischen Gittern gefangene atomare Gase als vielseitige experimentelle Plattform zur Erforschung der Quantenvielteilchenphysik erwiesen. In dieser Arbeit setzten wir ein Gitterplatz- und Einzelatom-aufgelöstes Quantengasmikroskop für bosonische Rb-87 Atome ein, um Vielteilchensysteme im und außerhalb des Gleichgewichts zu untersuchen.
Zunächst betrachteten wir den quantenmechanischen Phasenübergang zwischen dem suprafluiden und dem Mott-isolierenden Zustand im Bose-Hubbard-Modell, das nativ durch kalte Atome in optischen Gittern realisiert wird, und zeigten, dass sich die Brane-Parität eignet, um nichtlokale Ordnung im konventionell als ungeordnet erachteten zweidimensionalen Mott-Isolator zu identifizieren. Mithilfe eines mikroskopischen Ansatzes zur Realisierung einstellbarer Gittergeometrien und programmierbarer Einheitszellen implementierten wir Quadrats-, Dreiecks-, Kagome- und Lieb-Gitter und beobachteten die Skalierung des Phasenübergangspunkts mit der mittleren Koordinationszahl des Gitters.
In einem eindimensionalen Gitter untersuchten wir zudem den Hochtemperatur-Spintransport im Heisenberg-Modell, das durch Superaustausch in der Mott-isolierenden Phase eines zwei-Spezies Bose-Hubbard-Modells realisiert wurde. Durch Betrachten der Relaxationsdynamik eines als Domänenwand präparierten Anfangszustandes fanden wir eine superdiffusive Raum-Zeit-Skalierung mit einem anomalen dynamischen Exponenten von 3/2. Anschließend untersuchten wir die theoretisch vorhergesagten mikroskopischen Voraussetzungen für Superdiffusion, indem wir reguläre Diffusion im nicht-integrablen, zweidimensionalen Heisenberg-Modell und ballistischen Transport für SU(2)-Symmetrie-gebrochene magnetisierte Anfangszustände nachwiesen. Weiterhin maßen wir die Zählstatistik der durch die Domänenwand transportierten Spins; die sich daraus ergebende schiefe Verteilung deutete auf einen nichtlinearen zugrundeliegenden Transportprozess hin, der an die dynamische Kardar-Parisi-Zhang Universalitätsklasse erinnert.
Mittels Mott-Isolatoren im Limit tiefer Gitter konnten wir darüber hinaus die durch Photonen vermittelten Wechselwirkungen in einem Spinsystem untersuchen, das aus zwei über einen geschlossenen optischen Übergang gekoppelten Zuständen besteht. Durch spektroskopische Untersuchung der Reflexion und Transmission konnten wir die direkte Anregung einer subradianten Eigenmode und kohärente Spiegelung beobachten, was auf die Realisierung einer effizienten, im freien Raum operierenden, paraxialen Licht-Materie-Schnittstelle hindeutet.The interplay of quantum particles can give rise to collective phenomena whose characteristics are distinct from the behavior of individual particles. While quantum effects are generally challenging to observe, ultracold atomic gases trapped in optical lattices have emerged as a versatile experimental platform to study quantum many-body physics. In this thesis, we employed a site– and single-atom–resolved quantum gas microscope of bosonic Rb-87 atoms to explore many-body systems in and out of equilibrium.
We first considered the ground-state quantum phase transition between the superfluid and Mott-insulating state in the Bose–Hubbard model, natively realized by cold atoms in optical lattices, for which we found brane parity to be suitable for detecting nonlocal order in the conventionally unordered two-dimensional Mott insulator. Using a microscopic approach to realizing tunable lattice geometries and programmable unit cells, we implemented square, triangular, kagome and Lieb lattices, and observed the mean-field scaling of the phase transition point with average coordination number.
In a one-dimensional lattice, we furthermore studied high-temperature spin transport in the Heisenberg model, realized by superexchange in the Mott-insulating phase of a two-species Bose–Hubbard model. By tracking the relaxation dynamics of an initial domain-wall state, we found superdiffusive space–time scaling with an anomalous dynamical exponent of 3/2. We then probed the predicted microscopic requirements for superdiffusion, verifying regular diffusion for the integrability-broken two-dimensional Heisenberg model and ballistic transport for SU(2)-symmetry–broken net magnetized initial states. Subsequently, we measured the full counting statistics of spins transported across the domain wall; the resulting skewed distribution implied a nonlinear underlying transport process, reminiscent of the Kardar–Parisi–Zhang dynamical universality class.
Moving to Mott insulators in the deep-lattice limit, we could moreover study photon-mediated interactions on a subwavelength-spaced, array-ordered spin system consisting of states coupled by a closed optical transition. By spectroscopically probing the reflectance and transmittance, we demonstrated the direct excitation of a subradiant eigenmode and observed specular reflection, indicating the realization of an efficient free-space paraxial light–matter interface
Temporal Mapper: Transition networks in simulated and real neural dynamics
AbstractCharacterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method—Temporal Mapper—built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects’ behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics
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