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ref: -2018 tags: cortex layer martinotti interneuron somatostatin S1 V1 morphology cell type morphological recovery patch seq date: 03-06-2019 02:51 gmt revision:3 [2] [1] [0] [head]

Neocortical layer 4 in adult mouse differs in major cell types and circuit organization between primary sensory areas

  • Using whole-cell recordings with morphological recovery, we identified one major excitatory and seven inhibitory types of neurons in L4 of adult mouse visual cortex (V1).
  • Nearly all excitatory neurons were pyramidal and almost all Somatostatin-positive (SOM+) neurons were Martinotti cells.
  • In contrast, in somatosensory cortex (S1), excitatory cells were mostly stellate and SOM+ cells were non-Martinotti.
  • These morphologically distinct SOM+ interneurons correspond to different transcriptomic cell types and are differentially integrated into the local circuit with only S1 cells receiving local excitatory input.
  • Our results challenge the classical view of a canonical microcircuit repeated through the neocortex.
  • Instead we propose that cell-type specific circuit motifs, such as the Martinotti/pyramidal pair, are optionally used across the cortex as building blocks to assemble cortical circuits.
  • Note preponderance of axons.
  • Classifications:
    • Pyr pyramidal cells
    • BC Basket cells
    • MC Martinotti cells
    • BPC bipolar cells
    • NFC neurogliaform cells
    • SC shrub cells
    • DBC double bouquet cells
    • HEC horizontally elongated cells.
  • Using Patch-seq

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ref: -0 tags: computational biology evolution metabolic networks andreas wagner genotype phenotype network date: 06-12-2017 19:35 gmt revision:1 [0] [head]

Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks

  • ‘’João F. Matias Rodrigues, Andreas Wagner ‘’
  • Our observations suggest that the robustness of the Escherichia coli metabolic network to mutations is typical of networks with the same phenotype.
  • We demonstrate that networks with the same phenotype form large sets that can be traversed through single mutations, and that single mutations of different genotypes with the same phenotype can yield very different novel phenotypes
  • Entirely computational study.
    • Examines what is possible given known metabolic building-blocks.
  • Methodology: collated a list of all metabolic reactions in E. Coli (726 reactions, excluding 205 transport reactions) out of 5870 possible reactions.
    • Then ran random-walk mutation experiments to see where the genotype + phenotype could move. Each point in the genotype had to be viable on either a rich (many carbon source) or minimal (glucose) growth medium.
    • Viability was determined by Flux-balance analysis (FBA).
      • In our work we use a set of biochemical precursors from E. coli 47-49 as the set of required compounds a network needs to synthesize, ‘’’by using linear programming to optimize the flux through a specific objective function’’’, in this case the reaction representing the production of biomass precursors we are able to know if a specific metabolic network is able to synthesize the precursors or not.
      • Used Coin-OR and Ilog to optimize the metabolic concentrations (I think?) per given network.
    • This included the ability to synthesize all required precursor biomolecules; see supplementary information.
    • ‘’’“Viable” is highly permissive -- non-zero biomolecule concentration using FBA and linear programming. ‘’’
    • Genomic distances = hamming distance between binary vectors, where 1 = enzyme / reaction possible; 0 = mutated off; 0 = identical genotype, 1 = completely different genotype.
  • Between pairs of viable genetic-metabolic networks, only a minority (30 - 40%) of reactions are essential,
    • Which naturally increases with increasing carbon source diversity:
    • When they go back an examine networks that can sustain life on any of (up to) 60 carbon sources, and again measure the distance from the original E. Coli genome, they find this added robustness does not significantly constrain network architecture.

Summary thoughts: This is a highly interesting study, insofar that the authors show substantial support for their hypotheses that phenotypes can be explored through random-walk non-lethal mutations of the genotype, and this is somewhat invariant to the source of carbon for known biochemical reactions. What gives me pause is the use of linear programming / optimization when setting the relative concentrations of biomolecules, and the permissive criteria for accepting these networks; real life (I would imagine) is far more constrained. Relative and absolute concentrations matter.

Still, the study does reflect some robustness. I suggest that a good control would be to ‘fuzz’ the list of available reactions based on statistical criteria, and see if the results still hold. Then, go back and make the reactions un-biological or less networked, and see if this destroys the measured degrees of robustness.

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ref: bookmark-0 tags: FPGA verilog VHDL hacking hardware prototype date: 04-09-2007 22:34 gmt revision:2 [1] [0] [head]

http://www.fpga4fun.com/

http://www.enterpoint.co.uk/

http://www.ixo.de/info/usb_jtag/ open source USB Jtag adapter, works with dragon (I think!)