Complete integrability of information processing by biochemical reactions

Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling -- based on spin systems -- has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis--Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy -- based on completely integrable hydrodynamic-type systems of PDEs -- which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions.Comment: 24 pages, 10 figures; accepted for publication in Scientific Report

Training neural networks with end-to-end optical backpropagation

Optical computing is an exciting option for the next generation of machine learning hardware that is fast, parallel and energy efficient. To create a truly all-optical neural network, it is necessary to implement both stages of deployment: inference and training. This in turn requires the ability to construct multiple linear and nonlinear layers, and implement backpropagation - the primary algorithm for training neural networks - in optics. Training with backpropagation requires information to flow forward and backward through the same network, and imposes conflicting requirements on the mathematical function of the activation layers in each direction. Although a straightforward proposition for a digital processor, implementing these functions in optics has remained elusive, and so prevented any demonstration of true end-to-end optical training to date. This thesis builds on a conceptually-simple scheme to overcome this challenge, to show the first practical demonstration of a multi-layer optical neural network that includes end-to-end optical training. Coherent Fourier optics and spatial light modulation is used to implement the linear layers of a neural network, in the form of optical matrix-vector multiplication with real-valued or complex-valued parameters. The phenomenon of saturable absorption is used to perform the nonlinear neuron activations, and backpropagation is performed optically by means of counter-propagating beams of light, which act analogously to the pump and probe beams of doppler-free saturation spectroscopy. The optical network is used to successfully perform a range of standard benchmark classification tasks, after training the network with a variety of schemes that combine the physical system and a digital model in different ways. In doing so the advantages of hardware-in-the-loop training over traditional in-silico training are shown; improved network accuracy and resilience to errors. This work helps to confirm the potential of building the next generation of hardware for machine learning with analog optics for both inference and training

Intermittency and Self-Organisation in Turbulence and Statistical Mechanics

There is overwhelming evidence, from laboratory experiments, observations, and computational studies, that coherent structures can cause intermittent transport, dramatically enhancing transport. A proper description of this intermittent phenomenon, however, is extremely difficult, requiring a new non-perturbative theory, such as statistical description. Furthermore, multi-scale interactions are responsible for inevitably complex dynamics in strongly non-equilibrium systems, a proper understanding of which remains a main challenge in classical physics. As a remarkable consequence of multi-scale interaction, a quasi-equilibrium state (the so-called self-organisation) can however be maintained. This special issue aims to present different theories of statistical mechanics to understand this challenging multiscale problem in turbulence. The 14 contributions to this Special issue focus on the various aspects of intermittency, coherent structures, self-organisation, bifurcation and nonlocality. Given the ubiquity of turbulence, the contributions cover a broad range of systems covering laboratory fluids (channel flow, the Von Kármán flow), plasmas (magnetic fusion), laser cavity, wind turbine, air flow around a high-speed train, solar wind and industrial application

A mathematical model for IL6-induced differentiation of neural progenitor cells on a micropatterned polymer substrate

Neural progenitor cells (NPC) hold potential for repairing the injured and diseased central nervous system, and because of this we would like to better understand the mechanisms of NPC migration and differentiation. Previous in vitro research has shown that adult rat hippocampal progenitor cells (AHPC) differentiate into neurons in response to hippocampal astrocyte-secreted factors, including the cytokine IL6. This work is a mathematical study of a simple mechanism for IL6-induced AHPC differentiation. We show that all experimental results under consideration can be replicated by this model. A global sensitivity analysis is performed, demonstrating that the inhibitor of this pathway does not have an effect on differentiation over the initial six day period. Steady-state solutions are then discussed. We conclude with an exploration the effects of chemotaxis on differentiation

Potential for Increased Rural Electrification Rate in Sub-Saharan Africa using SWER Power Distribution Networks

Rural electrification rate (RER) in Africa is still low to date. Several countries in Sub-Saharan Africa have tried to address this problem using conventional single- phase two-wire or three-phase three-wire systems, however at large costs due to the nature of dispersed rural load centres, low load demand, and low population density. Another solution of off-grid generation creates associated health problems. Therefore, this paper undertakes a review of a single wire earth return (SWER) network as a RER improvement solution. The paper undertakes intensive literature review to elucidate challenges and solutions to the implementation of SWER technology. Advantages of SWER technology discussed make it the choice for RER improvement in Sub-Saharan African countries. After that, a case study is selected in rural Tanzania, and a preliminary SWER network design is undertaken

To what extent do cell-penetrating peptides selectively cross the blood-brain barrier?

The blood-brain barrier protects the brain from toxic compounds. Its selective permeability is essential for the optimal function of the central nervous system. Some peptides can cross the blood-brain barrier. On the other hand, cell-penetrating peptides are able to overcome the cell membrane. During this research project, it was investigated whether these cell-penetrating peptides also can cross the blood-brain barrier. The chemical diversity of the already reported cell-penetrating peptides was investigated and a unified response for the extent of cellular uptake of peptides was introduced. Based on this study, a set of cell-penetrating peptides was rationally selected for further research. In order to more objectively compare the quantitative data on the blood-brain barrier influx of peptides, a classification system for blood-brain barrier influx was established. The purity of the selected synthetized cell-penetrating peptides was also investigated, which is essential for obtaining reliable research conclusions. Different chromatographic systems were compared for the analysis of the selected peptides. The investigated cell-penetrating peptides crossed the blood-brain barrier to a different extent. The influx varied from very low to very high and some peptides showed efflux out of the brain. There was no correlation observed between the blood-brain barrier transport kinetics and the extent of cellular uptake. During the aging process, the blood-brain barrier shows an increased permeability and, together with other age-related functional changes, should be taken into account during the development of medicines used by the elderly. Therefore, the current regulatory status of the development of geriatric medicines was investigated

Third-order Optical Nonlinearities for Integrated Microwave Photonics Applications

The field of integrated photonics aims at compressing large and environmentally-sensitive optical systems to micron-sized circuits that can be mass-produced through existing semiconductor fabrication facilities. The integration of optical components on single chips is pivotal to the realization of miniature systems with high degree of complexity. Such novel photonic chips find abundant applications in optical communication, spectroscopy and signal processing. This work concentrates on harnessing nonlinear phenomena to this avail. The first part of this dissertation discusses, both from component and system level, the development of a frequency comb source with a semiconductor mode-locked laser at its heart. New nonlinear devices for supercontinuum and second-harmonic generations are developed and their performance is assessed inside the system. Theoretical analysis of a hybrid approach with synchronously-pumped Kerr cavity is also provided. The second part of the dissertation investigates stimulated Brillouin scattering (SBS) in integrated photonics. A fully-tensorial open-source numerical tool is developed to study SBS in optical waveguides composed of crystalline materials, particularly silicon. SBS is demonstrated in an all-silicon optical platform