Replicating Connectomic, Neural, and Behavioral Observations by Developing a Computational Model of Nervous and Muscular Systems in Caenorhabditis elegans
Replicating Connectomic, Neural, and Behavioral Observations by Developing a Computational Model of Nervous and Muscular Systems in Caenorhabditis elegans
Taegon Chung. (2025). Replicating Connectomic, Neural, and Behavioral Observations by Developing a Computational Model of Nervous and Muscular Systems in Caenorhabditis elegans. doi: 10.22677/THESIS.200000828733
Type
Thesis
Description
C. elegans, Neuronal network modeling, Newtonian mechanics, Simulation, Combined error function, 예쁜꼬마선충, 신경망 모델링, 뉴턴역학모델, 시뮬레이션, 결합형 에러 함수
Table Of Contents
I. Introduction 1 1.1 Caenorhabditis elegans 1 1.2 Neuronal network modeling of C. elegans 1 1.3 Structural data of C. elegans 2 1.4 Functional data of C. elegans 2 1.5 Challenges in integrating structural and functional data 2 1.6 Thesis contributions 3 II. ElegansBot: Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans 5 2.1 Motivation 5 2.2 Results 6 2.2.1 Newton's equation of motion for locomotion of Caenorhabditis elegans: How does ElegansBot work? 6 2.2.2 Can C. elegans in ElegansBot crawl or swim? 10 2.2.3 ElegansBot exhibits more complex behavior including the turn motion. 15 2.2.4 ElegansBot presents body shape ensembles of C. elegans from a shape in water en route to agar. 18 2.3 Discussion 23 2.3.1 ElegansBot is an advanced kinetic simulator that reproduces C. elegans' various locomotion. 23 2.3.2 ElegansBot will serve as a strong bridge for enhancing the knowledge in "from-synapse-to-behavior" research. 23 2.4 Methods 24 2.4.1 Frequency and wavelength of C. elegans locomotion 24 2.4.2 C. elegans locomotion videos 24 2.4.3 Obtaining kymograms from video 24 2.4.4 Program code and programming libraries 25 2.4.5 Physical constants of the ground surface 25 2.4.6 Defining behavioral categories 25 2.4.7 Worm's mass, actuator elasticity coefficient, and damping coefficient 27 2.4.8 Minimum information required to describe the motion of each rod 28 2.4.9 Preservation of linearity in friction 29 2.4.10 Frictional torque by rotational motion 30 2.4.11 Proof of muscle force 31 2.4.12 Joint force calculation method 32 2.4.13 Proof of numerical integration for the translational motion of a worm using semi-implicit Euler method 36 2.4.14 Numerical integration of the rotational motion of i-rod using semi-implicit Euler method 38 2.4.15 Correction formula for the rotational inertia of the entire worm 40 2.4.16 Proper selection of friction coefficients 46 III. Reverse-engineering of functional connectome weights of Caenorhabditis elegans from its behaviors in experiments 48 3.1 Motivation 48 3.2 Background 50 3.3 Results 52 3.3.1 Functional weights of gap junctions and chemical synapses were reversed-engineered with high correlations to anatomical connectomes. 52 3.3.2 Spatio-temporal track of locomotion and membrane potential of SMD neurons of C. elegans were reproduced using reverse-engineered functional weights of gap junctions, chemical synapses, and leaky-integrator equation. 54 3.3.3 The impact of trp-1,2 mutation and SMD(D/V) ablation observed in the proprioception experiments of C. elegans was explained and reproduced. 57 3.3.4 Central pattern generators for periodic locomotion of C. elegans were identified and their role for the locomotion was explained. 60 3.4 Discussion 62 3.4.1 CANN, the functional weights of connectome, explained and reproduced various experimental results of C. elegans. 62 3.4.2 Method evaluating the success in reverse-engineering the nervous system of C. elegans 62 3.4.3 Application of the new strategy to other nervous system 63 3.4.4 Limitations 63 3.5 Methods 65 3.5.1 Acquisition and modifications of anatomical connectome data 65 3.5.2 Inference of time constant of C. elegans neuron 65 3.5.3 Leaky-integrator equation for membrane potential calculation 66 3.5.4 Configuration of the membrane potential of command neurons 67 3.5.5 Numerical method for solution of leaky-integrator equation 68 3.5.6 Definition of the error function 68 3.5.7 Update process of functional weights for minimization of the error function 70 3.5.8 Stepwise neuron elimination 71 3.5.9 Critical neuron identification 72 3.5.10 Analysis tools 72 3.5.11 Induction of the leaky-integrator equation 72 3.5.12 Modified semi-implicit Euler method 73 3.5.13 Definition of target degree of muscle contraction 75 3.5.14 Numerical integration of leaky-integrator equation 76 3.5.15 Derivatives of the error function 77 3.5.16 Adaptive error minimization 80 3.5.17 Method to calculate body control angle 81 IV. Conclusion 82 References 84 국문요약 90
