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Protein trafficking and synaptic demand configure complex and dynamic synaptome architectures of individual neurons.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Simulation and parameter optimisation ↔ simulation/CA1_3dv_identifiability_PSO_GA.py, lines 314–380 · score 0.61 · global optimum, Nelder Mead, PSO, GA, Simulation
  2. [2] § Methods › Simulation and parameter optimisation ↔ simulation/CA1_identifiability_PSO_GA.py, lines 315–410 · score 0.61 · global optimum, Nelder Mead, PSO, GA, Simulation
  3. [3] § Methods › Simulation and parameter optimisation ↔ Eddie/runDGIdentifiability.sh, the whole file · a weak match · score 0.60 · GB memory, runtime limit, python
  4. [4] § Methods › Simulation and parameter optimisation ↔ Eddie/runDG_Identifiability_10.sh, the whole file · a weak match · score 0.60 · GB memory, runtime limit, python
  5. [5] § Results › Spatiotemporal distribution of PSD95 is tuned by local transport ↔ simulation/CA1_20reg_1dv_3w_2par_PSO_GA.py, lines 234–292 · score 0.58 · CA1slm, CA1so, CA1sr, fit, degradation, simulation
  6. [6] § Results › Spatiotemporal distribution of PSD95 is tuned by local transport ↔ simulation/CA1_20reg_1dv_3w_3par_PSO_GA.py, lines 234–292 · score 0.58 · CA1slm, CA1so, CA1sr, fit, degradation, simulation

Paper

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The authors' code

Python · 406 lines · 14 KB · CC-BY-4.0 · 1 match

  1. # If in Jupyter or within ipython run both following lines
  2. # If in the terminal run nrnivmodl to load Neuron engine
  3. # %%bash
  4. # nrnivmodl
  6. from neuron import h
  7. import numpy as np
  8. import matplotlib.pyplot as plt
  9. import math
  10. import scipy.linalg
  11. from PyNeuronToolbox.record import ez_record,ez_convert
  12. from PyNeuronToolbox.morphology import dist_between,allsec_preorder
  14. #np.random.seed(123456789)
  16. import logging
  17. FORMAT = '%(asctime)s :: %(levelname)s :: %(message)s'
  18. logging.basicConfig(format=FORMAT,level=logging.INFO)
  19. log = logging.getLogger("GA-logger")
  21. # Load morphology and other stuff
  22. # --> SegLists: soma[2], dend[74], dend_5[37], apic[42], axon[1]
  23. # --> Files from Migliore & Migliore (2012)
  24. # --> CA1 pyramidal neuron
  25. h.load_file('stdrun.hoc')
  26. h.xopen('ri06.hoc')
  27. h.xopen('fixnseg.hoc')
  28. h.xopen('5a_nogui.hoc')
  29. h.tstop = 700.0
  31. from sko.GA import RCGA
  32. from sko.PSO import PSO
  33. from scipy.optimize import minimize, Bounds
  35. import pandas as pd
  37. import sushibelt
  38. import time
  40. def sushi_system(a, b, c, d, l):
  41. """
  42. Returns a matrix A, such that dx/dt = A*x
  44. N = # of compartments
  45. A is (2N x 2N) matrix
  46. x is (2N x 1) vector.
  47. The first N elements correspond to concentrations of u (molecules in transit)
  48. The second half correspond to concentrations of u-star (active molecules)
  49. The trafficking rate constants along the microtubules are given by the vectors "a" and "b"
  50. The rate constants for u turning into u* is given by the vector "c"
  51. The rate constants for u* turning into u is given by the vector "d"
  52. The rate constants for the degradation of u* is given by the vector "l"
  53. """
  54. # number of compartments
  55. N = len(l)
  57. ## State-space equations
  58. # dx/dt = Ax + Bu
  59. A = np.zeros((2 * N, 2 * N))
  61. # Trafficking along belt
  62. # Iterative traversal of dendritic tree in pre-order
  63. i = 0
  64. section = None
  65. parentStack = [(None, h.soma[0])]
  66. while len(parentStack) > 0:
  67. # Get next section to traverse
  68. # --> p is parent index, section is h.Section object
  69. (p, section) = parentStack.pop()
  71. # Trafficking to/from parent
  72. if p is not None:
  73. # Out of parent, into child
  74. ai = a.pop()
  75. A[p, p] += -ai
  76. A[i, p] += ai
  77. # Into parent, out of child
  78. bi = b.pop()
  79. A[p, i] += bi
  80. A[i, i] += -bi
  82. # visit all segments in compartment
  83. for (j, seg) in enumerate(section):
  84. # Deal with out/into rates within compartment, just tridiag matrix
  85. if j > 0:
  86. # Out of parent, into child
  87. ai = a.pop()
  88. A[i - 1, i - 1] += -ai
  89. A[i, i - 1] += ai
  90. # Into parent, out of child
  91. bi = b.pop()
  92. A[i - 1, i] += bi
  93. A[i, i] += -bi
  94. # move onto next compartment
  95. i += 1
  97. # now visit children in pre-order
  98. child_list = list(h.SectionRef(sec=section).child)
  99. if child_list is not None:
  100. child_list.reverse()
  101. for c_sec in child_list:
  102. parentStack.append([i - 1, c_sec]) # append parent index and child
  104. # Trafficking off the belt
  105. for i in range(N):
  106. A[i, i] += -c[i]
  107. A[i + N, i] += c[i]
  109. # Reattachment to belt
  110. # for i in range(N):
  111. # # reattachment
  112. # A[i, i + N] += d[i]
  113. # A[i + N, i + N] += -d[i]
  115. # Degradation after being taken off the belt
  116. for i in range(N):
  117. A[i + N, i + N] = -l[i]
  119. return A
  122. def trafficking_solution(utarg):
  123. """ Solve the problem by tuning trafficking rates, like Figs 1 and 2. """
  124. x = []
  126. # Iterative traversal of dendritic tree in pre-order
  127. i = 0
  128. section = None
  129. parentStack = [(None, h.soma[0])]
  130. while len(parentStack) > 0:
  131. # Get next section to traverse
  132. # --> p is parent index, section is h.Section object
  133. (p, section) = parentStack.pop()
  135. # Trafficking to/from parent
  136. if p is not None:
  137. mp = utarg[p] # concentration in parent
  138. mc = utarg[i] # concentration in child
  139. x.insert(0, mp / mc)
  141. # visit all segments in compartment
  142. for (j, seg) in enumerate(section):
  143. # Deal with out/into rates within compartment, just tridiag matrix
  144. if j > 0:
  145. mp = utarg[i - 1]
  146. mc = utarg[i]
  147. x.insert(0, mp / mc)
  149. # move onto next compartment
  150. i += 1
  152. # now visit children in pre-order
  153. child_list = list(h.SectionRef(sec=section).child)
  154. if child_list is not None:
  155. child_list.reverse()
  156. for c_sec in child_list:
  157. parentStack.append([i - 1, c_sec]) # append parent index and child
  159. # return calculated guesses (flip, up/down since get_deriv pops from start)
  160. return np.array(x)
  163. def get_sys_matrix(utarg, F=0.5, Ctau=1e-3, dscale=0.1, dv=1e-7):
  164. # F is a mixing factor between 0 and 1
  165. K = np.sum(utarg) / N
  166. x = trafficking_solution(F * utarg + (1 - F) * K)
  167. a = (1 / (1 + x))
  168. a = list(a)
  169. b = list((1 / (1 + x ** -1)))
  170. l = list(np.ones(N) * dv)
  171. c = list(Ctau * utarg / (F * utarg + (1 - F) * K))
  172. d = list([ci * dscale for ci in c])
  173. A = sushi_system(a, b, c, d, l)
  174. return A
  176. def solve_u(u0,w,V,Vinv,t):
  177. D = np.diag(np.exp(w*t)) # diagonal matrix exponential
  178. PHI = np.real(V.dot(D.dot(Vinv))) # state transition matrix
  179. return PHI.dot(u0) # calculate u(t)
  181. def sim_time(A,u0,time,nframes=10):
  182. # Run a simulation (log time)
  183. # --> this is a linear system; thus, matrix exponential provides exact solution
  184. utrace = [u0]
  185. w,V = scipy.linalg.eig(A)
  186. Vinv = np.linalg.inv(V)
  187. t = np.logspace(-0.5,math.log10(time),nframes)
  188. for t_ in t: utrace.append(solve_u(u0,w,V,Vinv,t_))
  189. return np.array(utrace).T
  191. bgSignal = 1e-5
  193. def calcUtrace(par,delta=bgSignal):
  194. F = par[0]
  195. Ctau = 10 ** par[1]
  196. mProp = par[2]
  197. dv = np.zeros(N)
  198. utarg = delta*np.ones(N)
  199. for k in range(N):
  200. if itarg[k] > 2:
  201. dv[k] = 10 ** par[itarg[k]]
  202. utarg[k] = par[itarg[k]+3]
  203. utarg /= np.sum(utarg)
  204. K = np.sum(utarg) / N
  205. x = trafficking_solution(F * utarg + (1 - F) * K)
  206. a = (1 / (1 + x))
  207. a = list(a)
  208. b = list((1 / (1 + x ** -1)))
  209. l = list(dv)
  210. c = list(Ctau * utarg / (F * utarg + (1 - F) * K))
  211. d = list(np.zeros(N))
  212. A = sushi_system(a, b, c, d, l)
  213. u0 = np.concatenate((mProp * dinit, (1 - mProp) * dinit))
  214. utrace = sim_time(A, u0, day7)
  215. return utrace
  218. log.info("function defined")
  220. ##### Read data ######
  221. #seglist in pre-order
  222. sec_list = allsec_preorder(h)
  223. seg_list = []
  224. for sec in sec_list:
  225. locs = np.linspace(0,1,sec.nseg+2)[1:-1]
  226. for loc in locs:
  227. seg_list.append(sec(loc))
  228. N = len(seg_list)
  229. tdf=pd.read_csv('../data/seg_mapping.csv')
  230. abbCA1=tdf['abb']
  231. abbT={}
  232. segIdx={}
  233. for i in range(N):
  234. abbT[abbCA1[i]] = 1+ abbT.get(abbCA1[i],0)
  235. ll=segIdx.get(abbCA1[i],[])
  236. ll.append(i)
  237. segIdx[abbCA1[i]] = ll
  239. expD=pd.read_csv('../data/CA1_gradient.csv')
  240. subreg = ['CA1so', 'CA1sr', 'CA1slm']
  242. cname0='D0M3'
  243. d0w = -1 * np.ones(N)
  244. for i in range(expD.shape[0]):
  245. abb = expD['Abbreviation'][i]
  246. sidx= segIdx[abb]
  247. d0w[sidx] *= -1*expD[f"{cname0}_MEAN"][i]/len(sidx)
  248. for i in range(N):
  249. if d0w[i]<0:
  250. d0w[i] = bgSignal
  251. dinit = d0w/np.sum(d0w)
  253. cname7='D7M3'
  254. d7w = -1*np.ones(N)
  255. for i in range(expD.shape[0]):
  256. abb = expD['Abbreviation'][i]
  257. sidx= segIdx[abb]
  258. d7w[sidx] *= -1 * expD[f"{cname7}_MEAN"][i]/len(sidx)
  259. for i in range(N):
  260. if d7w[i]<0:
  261. d7w[i] = bgSignal
  262. target = np.array(expD[f"{cname7}_MEAN"])/np.sum(expD[f"{cname0}_MEAN"]) #norm target to Day0 sum to take into accound degradation
  263. targSD = np.array(expD[f"{cname7}_SD"])/np.sum(expD[f"{cname0}_MEAN"]) #measurement errors
  264. tnorm = np.sum(target ** 2)
  265. day7 = 7 * 24 * 3600 # final time point
  267. itarg = np.ones(N, dtype=int)
  268. for i in range(expD.shape[0]):
  269. abb = expD['Abbreviation'][i]
  270. sidx = segIdx[abb]
  271. itarg[sidx] *= [j + 3 for j in range(len(subreg)) if subreg[j] == expD['Subregion'][i]][0]
  273. log.info("data read")
  275. cfiCounter = 0
  276. cfCounter = 0
  277. dumpCSV = True
  278. def costFunction(par):
  279. initTime = time.time()
  280. global cfCounter, dumpCSV
  281. cfCounter += 1
  282. log.info(f'Cost function starts: {cfCounter}')
  283. log.info(f'{par}')
  284. mProp = par[2]
  285. utrace = calcUtrace(par)
  286. resM, resF = sushibelt.aggregate_segments(utrace[:, -1], segIdx, expD['Abbreviation'], fun=np.sum)
  287. cost=np.sum(((resF/(1-mProp) - target)/targSD) ** 2)
  288. FinalTime = time.time() - initTime
  289. if dumpCSV:
  290. best_line = np.append(cfCounter, par)
  291. best_line = np.append(best_line, cost)
  292. df = pd.DataFrame(best_line).T
  293. #log.info(f'best df={df}')
  294. df.to_csv('CA1_3dv_cfi.csv',header=False,mode='a')
  295. log.info(f'Cost function done: cost={cost}. ({FinalTime})')
  296. return cost
  298. lowb=np.array([0, -18, 1e-7, -18, -18, -18, 1e-3, 1e-3, 1e-3])
  299. upbga=np.array([1, -1, 1-1e-7, 1, 1, 1, 1, 1, 1])
  300. bnds=Bounds(lb=lowb,ub=upbga)
  302. parnames=['F','Ctau','mProp','dv_CA1so','dv_CA1sr','dv_CA1slm','demand_CA1so','demand_CA1sr','demand_CA1slm']
  303. Nvals = 4 #10
  304. parvals = [1.0 * i/Nvals for i in range(Nvals+1)]
  305. numPar=len(parnames)
  307. def prepPar(cpar,pn,pval,pidx,bidx):
  308. lpar = np.zeros(numPar)
  309. lpar[pidx] = pval
  310. for j in range(numPar - 1):
  311. lpar[bidx[j]] = cpar[j]
  312. return lpar
  314. def profileChiSq(pn,pv=0,pso_iter=15,nm_iter=100,ga_cycles=200):
  315. pidx=[i for i in range(numPar) if parnames[i]==pn][0]
  316. pval=lowb[pidx]+(upbga[pidx]-lowb[pidx])*pv
  317. bidx= [i for i in range(numPar) if i != pidx]
  318. log.info(f'ChiSq starts: numPar={numPar}, pn={pn}, pidx={pidx}, pv={pv}, pval={pval}.')
  319. clowb=lowb[bidx]
  320. cupbga=upbga[bidx]
  321. cbnds=Bounds(lb=clowb,ub=cupbga)
  322. def costFunctionC(cpar):
  323. global cfiCounter
  324. cfiCounter += 1
  325. lpar = prepPar(cpar,pn,pval,pidx,bidx)
  326. pDF = pd.DataFrame(lpar).T
  327. pDF.index = [cfiCounter]
  328. pDF['ParamName'] = pn
  329. pDF['ParamVal'] = pval
  330. cost = costFunction(lpar)
  331. pDF['Cost'] = cost
  332. pDF.to_csv('CA1_3dv_cf_idnt.csv',header=False,mode='a')
  333. return cost
  335. log.info(f'{pn}={pval}, Start PSO')
  336. # Population size for the PSO should be at least 2*numPar and in ideal situation 5*numPar
  337. pso = PSO(func=costFunctionC, n_dim=(numPar - 1), pop=2*numPar, max_iter=pso_iter, lb=clowb,
  338. ub=cupbga, w=0.8, c1=0.5, c2=0.5)
  339. pso.run()
  340. log.info(f'{pn}={pval}, best_x is {pso.gbest_x}, best_y is {pso.gbest_y}')
  341. log.info(f'{pn}={pval}, Run Nelder-Mead on PSO result')
  342. result = minimize(costFunctionC, pso.gbest_x, method='nelder-mead',bounds=cbnds,options={'maxiter':nm_iter})
  344. log.info(f'{pn}={pval}, Prepare GA population')
  345. #use pso.gbest_x and pso.gbest_y to get access to the global optimum found
  346. psox = pso.pbest_x
  347. psoy=pso.pbest_y
  348. idx=np.argsort(psoy.flatten())
  349. gainit=np.vstack([psox[idx[:(2*(numPar-1)-1)]],result.x])
  350. #log.info(f'{pn}={pval}, psox: {psox.shape}, idx: {len(idx)}, pop_size={2*(numPar-1)}, gainit: {gainit.shape}')
  351. log.info(f'{pn}={pval}, GA starts, population dimensions: {gainit.shape}')
  352. ga = RCGA(func=costFunctionC, n_dim=(numPar-1), size_pop=2*(numPar-1), max_iter=50000, prob_mut=0.01, lb=clowb,
  353. ub=cupbga)
  354. ga.Chrom = (gainit-clowb)/(cupbga-clowb)
  355. bestX=result.x
  356. bestY=result.fun
  358. for cnt in range(ga_cycles):
  359. log.info(f'{pn}={pval}, Continue GA {cnt}')
  360. best_x, best_y = ga.run(10)
  361. best_x_p=prepPar(best_x,pn,pval,pidx,bidx)
  362. log.info(f'GA {pn}={pval} done {cnt}: cfCounter={cfCounter}, best CF={best_y}')
  363. log.info(f'{pn}={pval}, best par={best_x} ({len(best_x)})')
  364. best_line = np.append(cnt,best_x_p)
  365. #log.info(f'{pn}={pval}, best line={best_line} ({len(best_line)})')
  366. chrom = ga.Chrom
  367. bpar_dist = np.sum((bestX-ga.best_x) ** 2)
  368. if bpar_dist > 1e-7 :
  369. log.info(f'{pn}={pval}, Run Nelder-Mead on the best GA result {cnt}, best par distance = {bpar_dist}')
  370. result = minimize(costFunctionC, ga.best_x, method='nelder-mead', bounds=cbnds, options={'maxiter': nm_iter})
  371. chrom[np.argmax(ga.Y), :] = (result.x - clowb) / (cupbga - clowb)
  372. ga.Chrom=chrom
  373. bestX = result.x
  374. bestY = result.fun
  375. else :
  376. bestX = ga.best_x
  377. bestY = best_y
  378. log.info(f'{pn}={pval}, best par distance = {bpar_dist} and Nelder-Mead run on the best GA result is omitted.')
  379. log.info(f'GA {pn}={pval} completed {cnt}: cfCounter={cfCounter}, best CF={bestY}')
  380. return bestX, bestY
  382. chiCounter = 0
  383. i = 0
  384. jval = [1 + k for k in range(Nvals)]
  386. for pni in parnames :
  387. for pvj in parvals :
  388. cfiCounter = 0
  389. #pni = parnames[i]
  390. bestX, bestY = profileChiSq(pni,pvj,pso_iter=10,nm_iter=10,ga_cycles=10)
  391. pidx=[k for k in range(numPar) if parnames[k]==pni][0]
  392. pval=lowb[pidx]+(upbga[pidx]-lowb[pidx])*pvj
  393. bidx= [k for k in range(numPar) if k != pidx]
  394. bestX_p = prepPar(bestX, pni, pval, pidx, bidx)
  395. log.info(f'GA {pni}={pval} best found: cfCounter={cfCounter}, best CF={bestY}')
  396. log.info(f'{pni}={pval}, best found: par={bestX_p} ({len(bestX_p)}')
  397. bestLine = np.append(cfiCounter, bestX_p)
  398. log.info(f'{pni}={pval}, best found: line={bestLine} ({len(bestLine)})')
  399. bdf = pd.DataFrame(bestLine).T
  400. bdf['ParamName'] = pni
  401. bdf['ParamVal'] = pval
  402. bdf['Cost'] = bestY
  403. bdf.index = [chiCounter]
  404. log.info(f'{pni}={pval}, best found: df={bdf}')
  405. bdf.to_csv('CA1_3dv_ident.csv', header=False, mode='a')
  406. chiCounter += 1

CA1_3dv_identifiability_PSO_GA.py at commit 4bfbe08, under CC-BY-4.0 · at the source

Overview

Authors: Oksana Sorokina1, Edita Bulovaite2,3, Anatoly Sorokin4, Seth G N Grant2,3,5,6, J Douglas Armstrong1,6,7
ORCID iDs: Seth G N Grant, J Douglas Armstrong
  1. School of Informatics, Institute for Machine Learning, University of Edinburgh, Edinburgh, EH8 9AB UK
  2. Genes to Cognition Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB UK
  3. Institute of Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, EH16 4SB UK
  4. Okinawa Institute of Science and Technology, Okinawa, 904-0497 Japan
  5. Euan MacDonald Centre, University of Edinburgh, Edinburgh, EH16 4SB UK
  6. Simons Initiative for the Developing Brain (SIDB), Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, EH8 9XD UK
  7. Computational Biomedicine Institute (IAS-5 / INM-9), Forschungszentrum Jülich, Jülich, 52425 Germany
Institutions: University of Edinburgh (United Kingdom); Okinawa Institute of Science and Technology Graduate University (Japan); Simons Initiative for the Developing Brain; Euan MacDonald Centre for Motor Neuron Disease Research (United Kingdom); Forschungszentrum Jülich (Germany)
Journal: Scientific reports, volume 16, issue 1, article 11541
Dates: received 24 September 2025; accepted 13 February 2026; published online 2 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-40513-7 · PMID 41771946 · PMCID PMC13057022 · OpenAlex W7133197733
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), mouse (organism), cellular / molecular (subfield)
Keywords: Synapse, Synaptome, Dendrite, Neuron, Simulation, Biophysics, Computational biology and bioinformatics, Neuroscience
MeSH: Neurons*, Synapses*, Animals, Brain, Computer Simulation, Dendrites, Mice, Models, Neurological, Protein Transport (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (885069 SYNAPTOME); Biotechnology and Biological Sciences Research Council (BB/X009343/1)
Citations: cited by 1 paper (Europe PMC); 32 references in the paper

Abstract

Excitatory synapses are the most abundant synapse type in the brain. Being essential for behaviour and implicated in hundreds of brain disorders, these synapses exhibit striking structural and functional diversity. Synaptome mapping at single-synapse resolution reveals that synaptic protein diversity is spatially organised along the dendritic tree of individual neurons and varies with age and cell type. However, the cell biological mechanisms underlying the generation of these complex spatial synaptic patterns remain poorly understood. Potential mechanisms include somatic and dendritic protein synthesis, protein trafficking, and local regulatory mechanisms such as activity-dependent degradation. Here we developed computational models to test how combinations of these processes account for empirical synaptome data. We found that a combination of molecular transport mechanisms and local synaptic demand for proteins was sufficient to explain very complex profiles of synaptic protein distributions observed in young, mature and old mice and in different cell types. Our findings suggest the highly complex and dynamic synaptome architecture of the brain is an emergent property of a minimal set of cell biological processes. Our model sets the stage for simulations of brain tissue incorporating molecularly diverse neuronal and synaptic types in a synaptome and connectome architecture.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-40513-7.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

oksankas/Sushi_belt_PSD95

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4bfbe08ec8528238cc8f4ac7b1c0074714943245, 5 December 2025
Languages: Python (62), NEURON (20), Jupyter (12), Shell (9)
Size: 126 files, 103 scripts
Software Heritage: not archived
Found in: the text, “Model development”
Holds: README, environment (environment.yml), 12 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NEURON (73 files), NumPy (72 files), pandas (70 files), SciPy (48 files), Matplotlib (45 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
104 files
At the source: github.com/oksankas/Sushi_belt_PSD95

digitalresearchservices.ed.ac.uk/resources/eddie

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: digitalresearchservices.ed.ac.uk/resources/eddie

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 103 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data availability

All research code is available on a GitHub repository (https://digitalresearchservices.ed.ac.uk/resources/eddie).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 9 MeSH terms, 2 funders, 28 references.

Cite

This paper

Sorokina, O., Bulovaite, E., Sorokin, A., Grant, S. G. N., & Armstrong, J. D. (2026). Protein trafficking and synaptic demand configure complex and dynamic synaptome architectures of individual neurons. Scientific reports, 16(1), 11541. https://doi.org/10.1038/s41598-026-40513-7

BibTeX

@article{sorokina2026protein,
author = {Sorokina, Oksana and Bulovaite, Edita and Sorokin, Anatoly and Grant, Seth G N and Armstrong, J Douglas},
title = {{Protein trafficking and synaptic demand configure complex and dynamic synaptome architectures of individual neurons}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {11541},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-40513-7},
url = {https://doi.org/10.1038/s41598-026-40513-7},
pmid = {41771946},
pmcid = {PMC13057022}
}

RIS

TY - JOUR
AU - Sorokina, Oksana
AU - Bulovaite, Edita
AU - Sorokin, Anatoly
AU - Grant, Seth G N
AU - Armstrong, J Douglas
TI - Protein trafficking and synaptic demand configure complex and dynamic synaptome architectures of individual neurons
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/02
VL - 16
IS - 1
SP - 11541
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-40513-7
UR - https://doi.org/10.1038/s41598-026-40513-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-40513-7",
"type": "article-journal",
"title": "Protein trafficking and synaptic demand configure complex and dynamic synaptome architectures of individual neurons",
"container-title": "Scientific reports",
"author": [
{
"family": "Sorokina",
"given": "Oksana"
},
{
"family": "Bulovaite",
"given": "Edita"
},
{
"family": "Sorokin",
"given": "Anatoly"
},
{
"family": "Grant",
"given": "Seth G N"
},
{
"family": "Armstrong",
"given": "J Douglas"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "11541",
"DOI": "10.1038/s41598-026-40513-7",
"PMID": "41771946",
"PMCID": "PMC13057022",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-40513-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
2
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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