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@ARTICLE{Damjanovic:1052363,
      author       = {Damjanovic, Ana and Carnevale, Vincenzo and Hater, Thorsten
                      and Sultan, Nauman and Rossetti, Giulia and Diaz, Sandra and
                      Carloni, Paolo},
      title        = {{F}rom {A}toms to {N}euronal {S}pikes: {A} {M}ultiscale
                      {S}imulation {F}ramework},
      journal      = {Journal of chemical theory and computation},
      volume       = {22},
      number       = {2},
      issn         = {1549-9618},
      address      = {Washington, DC},
      publisher    = {[Verlag nicht ermittelbar]},
      reportid     = {FZJ-2026-00962},
      pages        = {783-793},
      year         = {2026},
      abstract     = {Understanding how molecular events in ion channelsimpact
                      neuronal excitability, as derived from the calculation of
                      thetime course of the membrane potentials, can help
                      elucidate themechanisms of neurological disease-linked
                      mutations and supportneuroactive drug design. Here, we
                      propose a multiscale simulationapproach which couples
                      molecular simulations with neuronalsimulations to predict
                      the variations in membrane potential andneural spikes. We
                      illustrate this through two examples. First,molecular
                      dynamics simulations predict changes in current
                      andconductance through the AMPAR neuroreceptor when
                      comparingthe wild-type protein with certain
                      disease-associated variants. Theresults of these simulations
                      inform morphologically detailed modelsof cortical pyramidal
                      neurons, which are simulated using the Arborframework to
                      determine neural spike activity. Based on these multiscale
                      simulations, we suggest that disease associated
                      AMPARvariants may significantly impact neuronal
                      excitability. In the second example, the Arbor model is
                      coupled with coarse-grainedMonte Carlo gating simulations of
                      voltage-gated (K+ and Na+) channels. The predicted current
                      from these ion channels altered themembrane potential and,
                      in turn, the excitation state of the neuron was updated in
                      Arbor. The resulting membrane potential wasthen fed back
                      into the Monte Carlo simulations of the voltage-gated ion
                      channels, resulting in a bidirectional coupling of current
                      andmembrane potential. This allowed the transitions of the
                      states of the ion channels to influence the membrane
                      potentials and viceversa. Our Monte Carlo simulations also
                      included the crucial, so far unexplored, effects of the
                      composition of the lipid membraneembedding. We explored the
                      influence of lipidic compositions only using the Monte Carlo
                      simulations. Our combined approaches,which use several
                      simplifying assumptions, predicted membrane potentials
                      consistent with electrophysiological recordings
                      andestablished a multiscale framework linking the atomistic
                      perturbations to neuronal excitability},
      cin          = {JSC / INM-9},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5254 -
                      Neuroscientific Data Analytics and AI (POF4-525) / 5251 -
                      Multilevel Brain Organization and Variability (POF4-525) /
                      EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / eBRAIN-Health - eBRAIN-Health - Actionable
                      Multilevel Health Data (101058516)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)POF4-5251 / G:(EU-Grant)101147319 /
                      G:(DE-Juel1)Helmholtz-SLNS / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(EU-Grant)101058516},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1021/acs.jctc.5c01793},
      url          = {https://juser.fz-juelich.de/record/1052363},
}