An Instruction Language for Self-Construction in the Context of Neural Networks

Biological systems are based on an entirely different concept of construction than human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks can also be controlled by these primitives, allowing for the design of a “genetic code” containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Final state machine representation of corticogenesis

Cortical neurogenesis is a complex process in which progenitor cells have the ability to acquire specific fates and eventually differentiate toward a final cell type. The adoption of a particular cell state is the result of intrinsic genetic programs, which regulate cell behaviour, cell-cell interactions, and the influence of environmental cues. However there is still no comprehensive understanding of the control of cell fate specification and more in general on the self-constructing principles involved in the process. Insight into the mechanisms underlying corticogenesis is provided by the genealogical history of every precursor cell (cell lineage). We use a method based on spectral clustering to identify recurrent developmental patterns on reconstructed cortical lineages. The obtained state diagram represent compact state machine description of the developmental process. We evaluate the degree of similarities between precursors types and compare it to known single-cell gene expression profiles, with good agreement between the data and the model. Additionally we recast the model into a formal gene-like language and discuss the implications for genetic regulation

Robust 3D cell segmentation by local region growing in convex volumes

Clustering of cells is based on their expression profiles (either volume, average expression, standard deviation, sum, median or Pearson correlation coefficient). As an example, the hierarchical clustergram of median expression values was computed on 8343 cells in the germinal layers of monkey developing cerebral cortex stained for Pax6 (red), Tbr2 (blue), Ki67 (green), and nuclear staining DAPI. Cells where clustered with the standard k-means algorithm, whereby the number of clusters k=6 was determined empirically

Sicherheit 2014: Aussen-, Sicherheits- und Verteidigungspolitische Meinungsbildung im Trend

ISSN:1424-569

A Gene Regulatory Model of Cortical Neurogenesis

Sparse data describing mouse cortical neurogenesis were used to derive a model gene regulatory network (GRN) that is then able to control the quantitative cellular dynamics of the observed neurogenesis. Derivation of the network begins by estimating from the biological data a set of cell states and transition probabilities necessary to explain neurogenesis. We show that the stochastic transition between states can be implemented by the dynamics of a GRN comprising only 36 abstract genes. Finally, we demonstrate using detailed physical simulations of cell mitosis, and differentiation that this GRN is able to steer a population of neuroepithelial precursors through mitotic expansion and differentiation to form the quantitatively correct complex multicellular architectures of mouse cortical areas 3 and 6. We find that the same GRN is able to generate both areas though modulation of only one gene, suggesting that arealization of the cortical sheet may require only simple improvisations on a fundamental gene network. We conclude that even sparse phenotypic and cell lineage data can be used to infer fundamental properties of neurogenesis and its organization

Long-term results of therapeutic local anesthesia (neural therapy) in 280 referred refractory chronic pain patients

BACKGROUND: Can the application of local anesthetics (Neural Therapy, NT) alone durably improve pain symptoms in referred patients with chronic and refractory pain? If the application of local anesthetics does lead to an improvement that far exceeds the duration of action of local anesthetics, we will postulate that a vicious circle of pain in the reflex arcs has been disrupted (hypothesis). METHODS: Case series design. We exclusively used procaine or lidocaine. The inclusion criteria were severe pain and chronic duration of more than three months, pain unresponsive to conventional medical measures, written referral from physicians or doctors of chiropractic explicitly to NT. Patients with improvement of pain who started on additional therapy during the study period for a reason other than pain were excluded in order to avoid a potential bias. Treatment success was measured after one year follow-up using the outcome measures of pain and analgesics intake. RESULTS: 280 chronic pain patients were included; the most common reason for referral was back pain. The average number of consultations per patient was 9.2 in the first year (median 8.0). After one year, in 60 patients pain was unchanged, 52 patients reported a slight improvement, 126 were considerably better, and 41 pain-free. At the same time, 74.1 % of the patients who took analgesics before starting NT needed less or no more analgesics at all. No adverse effects or complications were observed. CONCLUSIONS: The good long-term results of the targeted therapeutic local anesthesia (NT) in the most problematic group of chronic pain patients (unresponsive to all evidence based conventional treatment options) indicate that a vicious circle has been broken. The specific contribution of the intervention to these results cannot be determined. The low costs of local anesthetics, the small number of consultations needed, the reduced intake of analgesics, and the lack of adverse effects also suggest the practicality and cost-effectiveness of this kind of treatment. Controlled trials to evaluate the true effect of NT are needed

Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ

Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology features including morphology, stemness, and proliferative behavior. Here we use an unsupervised machine learning method for Hidden Markov Trees (HMTs), which can be applied to large datasets to classify precursors on the basis of morphology, cell-cycle length, and behavior during mitosis. The unbiased lineage analysis automatically identifies cell types by applying a lineage-based clustering and model-learning algorithm to a macaque corticogenesis dataset. The algorithmic results validate previously reported observer classification of precursor types and show numerous advantages: It predicts a higher diversity of progenitors and numerous potential transitions between precursor types. The HMT model can be initialized to learn a user-defined number of distinct classes of precursors. This makes it possible to 1) reveal as yet undetected precursor types in view of exploring the significant features of precursors with respect to specific cellular processes; and 2) explore specific lineage features. For example, most precursors in the experimental dataset exhibit bidirectional transitions. Constraining the directionality in the HMT model leads to a reduction in precursor diversity following multiple divisions, thereby suggesting that one impact of bidirectionality in corticogenesis is to maintain precursor diversity. In this way we show that unsupervised lineage analysis provides a valuable methodology for investigating fundamental features of corticogenesis