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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Modeling traffic jam and growth process of neurons
  using isogeometric analysis and physics-informed 
 neural network - Yongjie Jessica Zhang (Carnegie M
 ellon University)
DTSTART;TZID=Europe/London:20230802T111500
DTEND;TZID=Europe/London:20230802T121500
UID:TALK202426AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/202426
DESCRIPTION:The motor-driven intracellular transport plays a c
 rucial role in supporting a neuron cell&rsquo\;s s
 urvival and function\, with motor proteins and mic
 rotubule (MT) structures collaborating to promptly
  deliver the essential materials to the right loca
 tion in neuron. The disruption of transport may le
 ad to the onset of various neurodegenerative disea
 ses. To study how neurons regulate the material tr
 ansport process and have a better understanding of
  the traffic jam formation\, we develop a PDE-cons
 trained optimization model and an isogeometric ana
 lysis (IGA) solver to simulate traffic jams induce
 d by MT reduction and swirl. We also develop a nov
 el IGA-based physics-informed graph neural network
  (PGNN) to quickly predict normal and abnormal tra
 nsport phenomena in different neuron geometries. T
 he IGA-based PGNN model contains simulators to han
 dle local prediction of both normal and two MT-ind
 uced traffic jams in pipes\, as well as another si
 mulator to predict normal transport in bifurcation
 s. B&eacute\;zier extraction is adopted to incorpo
 rate the geometry information into the simulators 
 to accurately compute the physics informed loss fu
 nction with PDE residuals. Moreover\, a GNN assemb
 ly model is adopted to tackle different neuron mor
 phologies by assembling local prediction into the 
 entire geometry. The well-trained model effectivel
 y predicts the distribution of transport velocity 
 and material concentration during traffic jam and 
 normal transport with an average error less than 1
 0% compared to IGA simulations.\n&nbsp\;\nTo model
  neuron growth\, we develop a new computational fr
 amework and an open-source software package "Neuro
 nGrowth_IGAcollocation&rdquo\; based on the phase 
 field method. Neurons consist of a cell body\, den
 drites\, and axons. Axons and dendrites are long p
 rocesses extending from the cell body and enabling
  information transfer to and from other neurons. T
 here is high variation in neuron morphology based 
 on their location and function\, thus increasing t
 he complexity in mathematical modeling of neuron g
 rowth. We propose a novel phase field model with i
 sogeometric collocation to simulate different stag
 es of neuron growth by considering the effect of t
 ubulin. The stages modeled include lamellipodia fo
 rmation\, initial neurite outgrowth\, axon differe
 ntiation\, and dendrite formation considering the 
 effect of intracellular transport of tubulin on ne
 urite outgrowth. By incorporating neurite features
  from experiments\, we can demonstrate similar rep
 roduction of neuron morphologies at different stag
 es of growth and allow extension towards the forma
 tion of neurite networks. Based on the IGA simulat
 ion data\, a CNN model is also built to efficientl
 y predict the growth process.\n&nbsp\;\nREFERENCES
 \n\nLi\, Y. J. Zhang.&nbsp\;Isogeometric Analysis-
 Based Physics-Informed Graph Neural Network for St
 udying Traffic Jam in Neurons.&nbsp\;Computer Meth
 ods in Applied Mechanics and Engineering\, 403:115
 757\, 2023.\nLi\, Y. J. Zhang.&nbsp\;Modeling Intr
 acellular Transport and Traffic Jam in 3D Neurons 
 Using PDE-Constrained Optimization.&nbsp\; Journal
  of Mechanics\, 38:44-59\, 2022.\nLi\, Y. J. Zhang
 .&nbsp\;Modeling Material Transport Regulation and
  Traffic Jam in Neurons Using PDE-Constrained Opti
 mization.&nbsp\;Scientific Reports\, 12:3902\, 202
 2.\nQian\, A. Pawar\, A. Liao\, C. Anitescu\, V. W
 ebster-Wood\, A. Feinberg\, T. Rabczuk\, Y. J. Zha
 ng.&nbsp\;Modeling Neuron Growth Using Isogeometri
 c Collocation Based Phase Field Method.&nbsp\;Scie
 ntific Reports\, 12:8120\, 2022.\nQian\, A. S. Lia
 o\, S. Gu\, V. Webster-Wood\, Y. J. Zhang. Biomime
 tic IGA neuron growth modeling with neurite morpho
 metric features and CNN-based prediction. Computer
  Methods in Applied Mechanics and Engineering\, 20
 23.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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