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This book demonstrates the power of mathematical thinking in understanding the biological complexity that exists within the brain. It looks at the latest research on modelling of biochemical pathways within synapses, and provides a clear background for the study of mathematical models related to systems biology. Discussion then focusses on developments in computational models based on networks linked to synaptic plasticity. The models are used to understand memory formation and impairment and they provide a mathematical basis for memory research.Computational Systems Biology of Synaptic Plasticity is a valuable source of knowledge to postgraduate students and researchers in computational systems biology, and as a reference book for various techniques that are needed in modelling biological processes.
This book demonstrates the power of mathematical thinking in understanding the biological complexity that exists within the brain. It looks at the latest research on modelling of biochemical pathways within synapses, and provides a clear background for the study of mathematical models related to systems biology. Discussion then focusses on developments in computational models based on networks linked to synaptic plasticity. The models are used to understand memory formation and impairment and they provide a mathematical basis for memory research. Computational Systems Biology of Synaptic Plasticity is a valuable source of knowledge to postgraduate students and researchers in computational systems biology, and as a reference book for various techniques that are needed in modelling biological processes.
Alzheimer's disease (AD) is the leading cause of dementia and, unfortunately, remains incurable. The social, emotional and financial implications of AD are immeasurable, and about 47 million people worldwide are affected by AD or other forms of dementia. As lifespans are improved by healthcare systems worldwide, age-associated neurodegenerative diseases are imposing an increasing challenge to science. It is becoming imperative for us to understand the causes of these diseases, AD in particular, at molecular and cellular levels. Starting with the broader picture from a biological perspective, this book takes the reader through fascinating dynamics within and outside of neurons in the brain.Alzheimer's Disease: Biology, Biophysics and Computational Models helps the reader to understand AD from mechanistic and biochemical perspectives at intra- and inter-cellular levels. It focuses on biochemical pathways and modeling associated with AD. Some of the recent research on biophysics and computational models related to AD are explained using context-driven computational and mathematical modeling and essential biology is discussed to understand the modeling research.
May 24-25, 2018 | Vienna | Austria Key Topics : Dementia-an underlying disease, Symptoms and Diagnosis of Dementia, Vascular Dementia, Alzheimer’s Diagnosis and Symptoms, Alzheimer’s Imaging and Clinical trials, Alzheimer’s Pathophysiology, Parkinson’s disease, Dementia with Lewy bodies, Frontotemporal dementia, Wernicke-Korsakoff Syndrome, Amyloid Protein in Dementia, Neurocognitive Disorder, Dementia Care Practice & Awareness, Therapeutic Targets & Mechanisms for Treatment, Animal Models & Translational Medicine, Mixed Dementia, Alzheimer’s Disease and Dementia Natural Remedies,
This is a handbook of methods and protocols for biologists. It aimed at undergraduate, graduate students and researchers originally trained in biological or medical sciences who need to know how to access the data archives of genomes, proteins, metabolites, gene expression profiles and the questions these data and tools can answer. For each chapter, the conceptual and experimental background is provided, together with specific guidelines for handling raw data, including preprocessing and analysis.The content is structured into three parts. Part one introduces basic knowledge about popular bioinformatics tools, databases and web resources. Part two presents examples of omics bioinformatics applications. Part three provides basic statistical analysis skills and programming skills needed to handle and analyze omics datasets.
This fully revised second edition provides the only unified synthesis of available information concerning the mechanisms of higher-order memory formation. It spans the range from learning theory, to human and animal behavioral learning models, to cellular physiology and biochemistry. It is unique in its incorporation of chapters on memory disorders, tying in these clinically important syndromes with the basic science of synaptic plasticity and memory mechanisms. It also covers cutting-edge approaches such as the use of genetically engineered animals in studies of memory and memory diseases. Written in an engaging and easily readable style and extensively illustrated with many new, full-color figures to help explain key concepts, this book demystifies the complexities of memory and deepens the reader’s understanding. More than 25% new content, particularly expanding the scope to include new findings in translational research. Unique in its depth of coverage of molecular and cellular mechanisms Extensive cross-referencing to Comprehensive Learning and Memory Discusses clinically relevant memory disorders in the context of modern molecular research and includes numerous practical examples
This book introduces the current concepts of molecular mechanisms in synaptic plasticity and provides a comprehensive overview of cutting-edge research technology used to investigate the molecular dynamics of the synapses. It explores current concepts on activity-dependent remodeling of the synaptic cytoskeleton and presents the latest ideas on the different forms of plasticity in synapses and dendrites. Synaptic Plasticity in Health and Disease not only supplies readers with extensive knowledge on the latest developments in research, but also with important information on clinical and applied aspects. Changes in spine synapses in different brain disease states, so-called synaptopathies, are explained and described by experts in the field. By outlining basic research findings as well as physiological and pathophysiological impacts on synaptic plasticity, the book represents an essential state-of-the-art work for scientists in the fields of biochemistry, molecular biology and the neurosciences, as well as for doctors in neurology and psychiatry alike.
Artificial synaptic plasticity is a programming approach used in artificial neural simulations to replicate the change in efficacy between two synapses observed in biological neurons. This biological synaptic plasticity is thought to enable neurons to control the connections between them. This control is then thought to lead to complex behaviour such as path integration. This stochastic process of activity dependent biological synaptic plasticity forces groups of neurons to operate together. The operation of large subsets of neurons underlies the cognition and memory formation in animals, without which life could not flourish. The most well studied region in the brain for synaptic plasticity is the hippocampus. This region was the first to display both long term potentiation, as well as long term depression. It has also been implicated in memory retention and has been shown to display spatial tuning. Furthermore, the discovery of place cells, and the more recent discovery of grid cells has created a surge of interest in the region. Entirely plausible models for grid field, place field, and memory formation have been suggested. The hippocampus could very well be the first brain region to be understood which does not represent purely sensory input. This thesis applies the rules of activity dependent synaptic plasticity to the hippocampus by modelling the region in silicon. This model focuses on the head direction cells, grid cells, and place cells. The head direction cells are generated using rotational inputs. The grid cells are then generated using both head direction input and forward motion inputs. Finally, the place cells are created using grid cell inputs. To facilitate the construction of this model, a simulator has also been created.
In this thesis, theoretical models with increasing levels of abstraction are developed to address questions arising from neuroscience experiments. They are studied using numerical and analytical approaches. With Laurent Venance's laboratory (Paris), we have developed an ITDP (input-timing-dependent plasticity) protocol model for the plasticity of cortico- and thalamo-striatal synapses. The model has been calibrated with ex vivo data and will be used to determine the presence of synaptic plasticity in vivo, in behavioral experiments aimed at determining the role of cortical and thalamic inputs in motor learning. At the level of neuronal populations, I have studied the modulation of neuronal collective behaviors by astrocytes, in particular Up-Down synchronization, a spontaneous alternation between periods of high collective activity and periods of silence. I have proposed rate and spiking neural network models of interconnected populations of neurons and astrocytes. They offer explanations of how astrocytes induce Up-Down transitions. Astrocytes are also probably involved in the generation of epileptic seizures, during which neuronal synchronization is impaired. Based on the above models, I have developed a neuron-astrocyte network with a cluster connectivity, showing the transition between Up-Down dynamics and events of very high activity mimicking an epileptic seizure. Finally, at the level of the brain itself, I studied the standard theory of consolidation, according to which short-term memory in the hippocampus enables the consolidation of long-term memory in the neocortex. I have sought to explain this phenomenon by integrating biological hypotheses - the size of the neocortex explaining the slowness of learning, and neurogenesis in the hippocampus explaining the erasure of its memory - into a model of interconnected neural fields that well reproduces the main features of the theory.
Until about a decade ago, the non-coding part of the genome was considered without function. RNA sequencing studies have shown, however, that a considerable part of the non-coding genome is transcribed and that these non-coding RNAs (nc-RNAs) can regulate gene expression. Almost on weekly basis, new findings reveal the regulatory role of nc-RNAs exert in many biological processes. Overall, these studies are making increasingly clear that, both in model organisms and in humans, complexity is not a function of the number of protein-coding genes, but results from the possibility of using combinations of genetic programs and controlling their spatial and temporal regulation during development, senescence and in disease by regulatory RNAs. This has generated a novel picture of gene regulatory networks where regulatory nc-RNAs represent novel layers of regulation. Particularly well-characterized is the role of microRNAs (miRNAs), small nc-RNAs, that bind to mRNAs and regulate gene expression after transcritpion. This message is particularly clear in the nervous system, where miRNAs have been involved in regulating cellular pathways controlling fundamental functions during development, synaptic plasticity and in neurodegenerative disease. It has also been shown that neuronal miRNAs are tightly regulated by electrical activity at the level of transcription, biogenesis, stability and specifically targeted to dendrites and synapses. Deregulation of expression of miRNAs is proposed not only as potential disease biomarker, but it has been implicated directly in the pathogenesis of complex neurodegenerative disease. This so-called RNA revolution also lead to the exploitation of RNA interference and the development of related tools as potential treatment of a vast array of CNS disease that could benefit from regulation of disease-associated genes. In spite of these advancements, the relatively young age of this field together with the inherent high molecular complexity of RNA regulation of biological processes have somewhat hindered its communication to the whole of the neuroscience community. This Research Topic aims at improving this aspect by putting around the same virtual table scientists covering aspects ranging from basic molecular mechanisms of regulatory RNAs in the nervous system to the analysis of the role of specific regulatory RNAs in neurobiological processes of development, plasticity and aging. Furthermore, we included papers analyzing the role of regulatory RNAs in disease models from neuromuscular to higher cognitive functions, and more technically oriented papers dealing with new methodologies to study regulatory RNA biology and its translational potential.