The impact of landscape sparsification on modelling and analysis of the invasion process

Climate change is a major threat to species, unless their populations are able to invade and colonise new landscapes of more suitable environment. In this paper, we propose a new model of the invasion process using a tool of landscape network sparsification to efficiently estimate a duration of the process. More specifically, we aim to simplify the structure of large landscapes using the concept of sparsification in order to substantially decrease the time required to compute a good estimate of the invasion time in these landscapes. For this purpose, two different simulation methods have been compared: full and R-local simulations, which are based on the concept of dense and sparse networks, respectively. These two methods are applied to real heterogeneous landscapes in the United Kingdom to compute the total estimated time to invade landscapes. We examine how the duration of the invasion process is affected by different factors, such as dispersal coefficient, landscape quality and landscape size. Extensive evaluations have been carried out, showing that the R-local method approximates the duration of the invasion process to high accuracy using a substantially reduced computation time

The Impact of Landscape Sparsification on Modelling and Analysis of the Invasion Process

Climate change is a major threat to species, unless their populations are able to invade and colonise new landscapes of more suitable environment. In this paper, we propose a new model of the invasion process using a tool of landscape network sparsification to efficiently estimate a duration of the process. More specifically, we aim to simplify the structure of large landscapes using the concept of sparsification in order to substantially decrease the time required to compute a good estimate of the invasion time in these landscapes. For this purpose, two different simulation methods have been compared: full and R-local simulations, which are based on the concept of dense and sparse networks, respectively. These two methods are applied to real heterogeneous landscapes in the United Kingdom to compute the total estimated time to invade landscapes. We examine how the duration of the invasion process is affected by different factors, such as dispersal coefficient, landscape quality and landscape size. Extensive evaluations have been carried out, showing that the R-local method approximates the duration of the invasion process to high accuracy using a substantially reduced computation time

2020 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Twelfth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: October 31 - November 1, 2020Location: The 2020 conference was conducted remotely due to COVID-19 concerns, utilizing the sococo platform that allows personal avatars to move between rooms and sessions, interact in small groups and also participate in zoom sessions.Keynote Speaker: Gerardo Chowell, Population Health Sciences, Georgia State Univ. School of Public Health, AtlantaFeatured Speaker: Olivia Prosper, Mathematics, Univ. of Tennessee, Knoxvill

Atomistic graph analysis of protein dimers in disease

Proteins are fundamental components of biological processes thus, they are often termed the molecular machinery of life. They commonly form dimers, in a process that is often essential for their functionality. Given the ubiquitous nature of protein regulation, many diseases are based on malfunctioning proteins and inhibiting them by binding to the active site is a widely chosen approach in drug development. However, due to acquired resistance mechanisms or high off-target effects, the active site might not always be a viable approach. This work presents an atomistic, structural investigation of dimeric proteins in the context of major disease processes, where we provide insights into potential alternative drug targeting approaches. In this Thesis, novel diffusion-based methods are applied to characterise the intra-structural connectivity and signalling of protein dimers. The basis of our methods is the description of proteins as atomistic, energy-weighted graphs, where every atom represents a node, and every bond or interaction is encoded as a weighted edge. These graphs facilitate the study of connectivity and signal propagation within the protein through diffusion processes on the atom (node) and bond (edge) space. Two complementary methodologies are applied here, Markov Transients and bond-to-bond propensities, which have been successfully used in the context of allosteric site detection, the study of protein-protein interactions and the investigation of allosteric signalling on an atomistic level. This work explores the extension of these methodologies onto protein dimers and presents the investigation of allosteric mechanisms in three disease-relevant study systems: 1. Estrogen receptor alpha (ERα) is a homodimer and the main driver in breast cancer (BC) development and progression. Current chemotherapies based on inhibiting ERα become ineffective when recurrent tumours develop resistance against anti-estrogens. Our methodologies validate the molecular mechanism in ERα, and we further establish the prevalent role of the dimer interface in the inhibition process. 2. The main protease (Mpro) of the coronavirus SARS-CoV-2 is essential for virus replication in an early step of the viral life cycle. Since the beginning of 2020, we have seen this virus causing a global pandemic of COVID-19, with over 285 million cases of infection and over 5.5 million deaths by the end of 2021. To aid in combating COVID-19, we predict highly connected allosteric hotspots and provide insights into how the disruption of the obligatory Mpro dimerisation presents a fruitful approach. 3. Cyclin-dependent kinases (CDKs) 4 and 6 are two essential cell cycle regulators that are often associated with cancer development, and in BC, their inhibition is part of an effective combinatorial treatment. This work contributes to understanding their activation process in complex with D-type cyclins and sheds light on the differential inhibitor patterns seen for CDKs. By exploring these three systems with atomistic graph analysis, we describe intra-complex communication essential for activation in all three proteins. We further present implications for the respective dimer interface connectivities and how they could be a fruitful drug target. We conclude that ERα, the SARS-CoV-2 Mpro and CDK4/6 can be disrupted over allosteric mechanisms that include their dimer interfaces. These results provide scope for targeted drug development and provide a valuable contribution to the ongoing efforts to find efficient treatments for BC and COVID-19.Open Acces

The role of excitation and inhibition in learning and memory formation

The neurons in the mammalian brain can be classified into two broad categories: excitatory and inhibitory neurons. The former has been historically associated to information processing whereas the latter has been linked to network homeostasis. More recently, inhibitory neurons have been related to several computational roles such as the gating of signal propagation, mediation of network competition, or learning. However, the ways by which excitation and inhibition can regulate learning have not been exhaustively explored. Here we explore several model systems to investigate the role of excitation and inhibition in learning and memory formation. Additionally, we investigate the effect that third factors such as neuromodulators and network state exert over this process. Firstly, we explore the effect of neuromodulators onto excitatory neurons and excitatory plasticity. Next, we investigate the plasticity rules governing excitatory connections while the neural network oscillates in a sleep-like cycle, shifting between Up and Down states. We observe that this plasticity rule depends on the state of the network. To study the role of inhibitory neurons in learning, we then investigate the mechanisms underlying place field emergence and consolidation. Our simulations suggest that dendrite-targeting interneurons play an important role in both promoting the emergence of new place fields and in ensuring place field stabilization. Soma-targeting interneurons, on the other hand, are suggested to be related to quick, context-specific changes in the assignment of place and silent cells. We next investigate the mechanisms underlying the plasticity of synaptic connections from specific types of interneurons. Our experiments suggest that different types of interneurons undergo different synaptic plasticity rules. Using a computational model, we implement these plasticity rules in a simplified network. Our simulations indicate that the interaction between the different forms of plasticity account for the development of stable place fields across multiple environments. Moreover, these plasticity rules seems to be gated by the postsynaptic membrane voltage. Inspired by these findings, we propose a voltage-based inhibitory synaptic plasticity rule. As a consequence of this rule, the network activity is kept controlled by the imposition of a maximum pyramidal cell firing rate. Remarkably, this rule does not constrain the postsynaptic firing rate to a narrow range. Overall, through multiple stages of interactions between experiments and computational simulations, we investigate the effect of excitation and inhibition in learning. We propose mechanistic explanations for experimental data, and suggest possible functional implications of experimental findings. Finally, we proposed a voltage-based inhibitory synaptic plasticity model as a mechanism for flexible network homeostasis.Open Acces

Retrieval and Annotation of Music Using Latent Semantic Models

PhDThis thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences Research Counci

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described