The mechanical control of nervous system development.

The development of the nervous system has so far, to a large extent, been considered in the context of biochemistry, molecular biology and genetics. However, there is growing evidence that many biological systems also integrate mechanical information when making decisions during differentiation, growth, proliferation, migration and general function. Based on recent findings, I hypothesize that several steps during nervous system development, including neural progenitor cell differentiation, neuronal migration, axon extension and the folding of the brain, rely on or are even driven by mechanical cues and forces.This work was supported by the Medical Research CouncilThis is the accepted version of the original publication available at http://dev.biologists.org/content/140/15/3069

Ein Wachstumsgrammatikinterpreter für neuroanatomische Simulationen

Im Rahmen dieser Diplomarbeit ist ein Programm zu entwickeln, das die Erzeugung und Analyse anatomisch realistischer Neuron-Analoga ermöglicht. Das Programm soll den Aufbau von Dendriten, mittels modifizierter L-Systeme realisieren, so dass ein breites Spektrum von Verzweigungsstrukturen modelliert werden kann

Mathematical modelling and numerical simulation of the morphological development of neurons

BACKGROUND: The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. METHODS: A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. RESULTS: Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. CONCLUSION: A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity

A Framework for Modeling the Growth and Development of Neurons and Networks

The development of neural tissue is a complex organizing process, in which it is difficult to grasp how the various localized interactions between dividing cells leads relentlessly to global network organization. Simulation is a useful tool for exploring such complex processes because it permits rigorous analysis of observed global behavior in terms of the mechanistic axioms declared in the simulated model. We describe a novel simulation tool, CX3D, for modeling the development of large realistic neural networks such as the neocortex, in a physical 3D space. In CX3D, as in biology, neurons arise by the replication and migration of precursors, which mature into cells able to extend axons and dendrites. Individual neurons are discretized into spherical (for the soma) and cylindrical (for neurites) elements that have appropriate mechanical properties. The growth functions of each neuron are encapsulated in set of pre-defined modules that are automatically distributed across its segments during growth. The extracellular space is also discretized, and allows for the diffusion of extracellular signaling molecules, as well as the physical interactions of the many developing neurons. We demonstrate the utility of CX3D by simulating three interesting developmental processes: neocortical lamination based on mechanical properties of tissues; a growth model of a neocortical pyramidal cell based on layer-specific guidance cues; and the formation of a neural network in vitro by employing neurite fasciculation. We also provide some examples in which previous models from the literature are re-implemented in CX3D. Our results suggest that CX3D is a powerful tool for understanding neural development

The Interplay between Branching and Pruning on Neuronal Target Search during Developmental Growth: Functional Role and Implications

Regenerative strategies that facilitate the regrowth and reconnection of neurons are some of the most promising methods in spinal cord injury research. An essential part of these strategies is an increased understanding of the mechanisms by which growing neurites seek out and synapse with viable targets. In this paper, we use computational and theoretical tools to examine the targeting efficiency of growing neurites subject to limited resources, such as maximum total neural tree length. We find that in order to efficiently reach a particular target, growing neurites must achieve balance between pruning and branching: rapidly growing neurites that do not prune will exhaust their resources, and frequently pruning neurites will fail to explore space effectively. We also find that the optimal branching/pruning balance must shift as the target distance changes: different strategies are called for to reach nearby vs. distant targets. This suggests the existence of a currently unidentified higher-level regulatory factor to control arborization dynamics. We propose that these findings may be useful in future therapies seeking to improve targeting rates through manipulation of arborization behaviors

Novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 64-70).Axons exhibit a rich variety of behaviors, such as elongation, turning, branching, and fasciculation, all in service of the complex goal of wiring up the brain. In order to quantify these behaviors, I have developed a system for in vitro imaging of axon growth cones with time-lapse fluorescence microscopy. Image tiles are automatically captured and assembled into a mosaic image of a square millimeter region. GFP-expressing mouse cortical neurons can be imaged once every few minutes for up to weeks if phototoxicity is minimized. Looking at the data, the trajectories of axon growth cones seem to alternate between long, straight segments and sudden turns. I first rigorously test the idea that the straight segments are generated from a biased random walk by analyzing the correlation between growth cone steps in the time and frequency domain. To formalize and test the intuition that sharp turns join straight segments, I fit a hidden Markov model to time series of growth cone velocity vectors.(cont.) The hidden state variable represents the bias direction of a biased random walk, and specifies the mean and variance of a Gaussian distribution from which velocities are drawn. Rotational symmetry is used to constrain the transition probabilities of the hidden variable, as well as the Gaussian distributions for the hidden states. Maximum likelihood estimation of the model parameters shows that the most probable behavior is to remain in the same hidden state. The second most probable behavior is to turn by about 40 degrees. Smaller angle turns are highly improbable, consistent with the idea that the axon makes sudden turns. When the same hidden Markov model was applied to artificially generated meandering trajectories, the transition probabilities were significant only for small angle turns. This novel application of stochastic models to growth cone trajectories provides a quantitative framework for testing interventions (eg. pharmacological, activity-related, etc.) that can impact axonal growth cone movement and turning. For example, manipulations that inhibit actin polymerization increase the frequency and angle of turns made by the growth cone. More generally, axon behaviors may be useful in deducing computational principles for wiring up circuits.by Neville Espi Sanjana.Ph.D

Biologically Plausible Models of Neurite Outgrowth

The growth of a neuronal dendritic tree depends on the neuron’s internal state and the environment within which it is situated. Different types of neuron develop dendritic trees with specific characteristics, such as the average number of terminal branches and the average length of terminal and intermediate segments. A key aspect of the growth process is the construction of the microtubule cytoskeleton within the dendritic tree. Neurite elongation requires assembly of microtubules from free tubulin at the growth cone. The stability of microtubule bundles is an important factor in determining how likely it is for a growth cone to split to form new daughter branches. Microtubule assembly rates and bundle stability are controlled by microtubule-associated proteins, principally MAP2 in dendrites. Extending previous work (Hely et al, J. Theor. Biol. 210:375-384, 2001) I have developed a mathematical model of neurite outgrowth in which elongation and branching rates are determined by the phosphorylation state of MAP2 at the tips of each terminal branch. Tubulin and MAP2 are produced in the cell body and transported along the neurite by a combination of diffusion and active transport. Microtubule (dis)assembly at neurite tips is a function of tubulin concentration. The rate of assembly depends on the amount of unphosphorylated MAP2 bound to the microtubules and linking them together. Phosphorylation of MAP2 destroys its linking capability and destabilises the microtubule bundles. Each terminal has a probability of branching that depends on the phosphorylation of MAP2 which, in turn, is a function of calcium concentration. Results from this model show that changes in the (de)phosphorylation rates of MAP2 affect the topology of the final dendritic tree. Higher phosphorylation promotes branching and results in trees with many short terminal branches and relatively long intermediate segments. Reducing phosphorylation promotes elongation and inhibits branching

Quantifying activity in nascent neuronal networks derived from embryonic stem cells

PhD ThesisThe relationship between spatiotemporal patterns of spontaneous activity and functional specialisation in developing neuronal networks is complex and its study is crucial to our understanding of how network communication is initiated. This project quantifies transitions between structural and functional states in embryonic stem cell cultures during differentiation. The work also focussed on the role of γ-aminobutyric acid (GABA), known to be vital for neuronal network development. The work used many techniques, including carbon nanotube (CNT) -patterned substrates to manipulate network architecture, multi-electrode arrays (MEAs) and calcium imaging to quantify function. An embryonic stem cell line (CC9) was used to generate ‘de novo’ neuronal networks and these were monitored over 13 – 22 days in vitro (DIV), while network structure forms and stabilizes. On CNT-patterned arrays, differentiating CC9s migrated and sub-clustered on CNT islands showing that network structure could be manipulated. No spontaneous electrophysiological (unit) activity was found in these cultures. However, intracellular calcium responses were readily induced and seen spontaneously at 13-20 DIV. Activity rate, kinetics and number of active cells increased between 16-18 DIV, correlating with changes in network clustering. Post 17 DIV, activity transformed from near-random to periodic and synchronous. Many events were initiated by ‘hubs’ and degrees of critical behaviour were observed, moving towards more efficient information processing states with development. Blockade of GABAA receptors lead to elevated spontaneous activity and supercritical behaviour, depending on developmental stage. Application of exogenous GABA induced large, slow calcium transients in a developmental stage-dependent manner, suggestive of a mixed excitatory/inhibitory role. These findings begin to show how activity develops as stem cells differentiate to form neuronal networks. GABA’s role in controlling patterns of activity was more complex that previously reported for neuronal networks in situ, but GABA clearly played a vital role in shaping population behaviour to optimise information processing properties in early, developing networks

Biophysical constraints on neuronal branching

We investigate rules that govern neuronal arborization, specifically the local geometry of the bifurcation of a neurite into its sub-branches. In the present study we set out to determine the relationship between branch diameter and angle. Existing theories are based on minimizing a neuronal volume cost function, or, alternatively, on the equilibrium of mechanical tension forces, which depend on branch diameters. Our experimental results utilizing two-dimensional cultured neural networks partly corroborate both the volume optimization principles and the tension theory. Deviation from pure tension forces equilibrium is explained by an additional force exerted by the anchoring of the junction to the substrate. (C) 2004 Elsevier B.V. All rights reserved