Probabilistic reachability analysis of the tap withdrawal circuit in caenorhabditis elegans

We present a probabilistic reachability analysis of a (nonlinear ODE) model of a neural circuit in Caeorhabditis elegans (C. elegans), the common roundworm. In particular, we consider Tap Withdrawal (TW), a reflexive behavior exhibited by a C. elegans worm in response to vibrating the surface on which it is moving. The neural circuit underlying this response is the subject of this investigation. Specially, we perform bounded-time reachability analysis on the TW circuit model of Wicks et al. (1996) to estimate the probability of various TW responses. The Wicks et al. model has a number of parameters, and we demonstrate that the various TW responses and their probability of occurrence in a population of worms can be viewed as a problem of parameter uncertainty. Our approach to this problem rests on encoding each TW response as a hybrid automaton with parametric uncertainty. We then perform probabilistic reachability analysis on these automata using a technique that combines a δ-decision procedure with statistical tests. The results we obtain are a significant extension of those of Wicks et al. (1996), who equip their model with fixed parameter values that reproduce a single TW response. In contrast, our technique allow us to more thoroughly explore the models parameter space using statistical sampling theory, identifying in the process the distribution of TW responses. Wicks et al. conducted a number of ablation experiments on a population of worms in which one or more of the neurons in the TW circuit are surgically ablated (removed). We show that our technique can be used to correctly estimate TW response-probabilities for four of these ablation groups. We also use our technique to predict TW response behavior for two ablation groups not previously considered by Wicks et al.

[1]  Cori Bargmann,et al.  Laser killing of cells in Caenorhabditis elegans. , 1995, Methods in cell biology.

[2]  Evan L Ardiel,et al.  An elegant mind: learning and memory in Caenorhabditis elegans. , 2010, Learning & memory.

[3]  Theodore H. Lindsay,et al.  Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans , 2015, Cell.

[4]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[5]  Carolyn Talcott,et al.  Executable Symbolic Models of Neural Processes , 2007 .

[6]  P. Erdös,et al.  Theory of the locomotion of nematodes: Dynamics of undulatory progression on a surface. , 1991, Biophysical journal.

[7]  S. R. Wicks,et al.  Integration of mechanosensory stimuli in Caenorhabditis elegans , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  Ashish Tiwari,et al.  Analyzing a Discrete Model of Aplysia Central Pattern Generator , 2008, CMSB.

[9]  Elizabeth Cherry,et al.  Models of cardiac cell , 2008, Scholarpedia.

[10]  Thomas M. Morse,et al.  The Fundamental Role of Pirouettes in Caenorhabditis elegans Chemotaxis , 1999, The Journal of Neuroscience.

[11]  Radu Grosu,et al.  Model Checking Tap Withdrawal in C. Elegans , 2015, HSB.

[12]  Edmund M. Clarke,et al.  SReach: A Probabilistic Bounded Delta-Reachability Analyzer for Stochastic Hybrid Systems , 2015, CMSB.

[13]  Chuchu Fan,et al.  Bounded Verification with On-the-Fly Discrepancy Computation , 2015, ATVA.

[14]  S. Lockery,et al.  Active Currents Regulate Sensitivity and Dynamic Range in C. elegans Neurons , 1998, Neuron.

[15]  Aravinthan D. T. Samuel,et al.  C. elegans locomotion: small circuits, complex functions , 2015, Current Opinion in Neurobiology.

[16]  Matthias Durr,et al.  Methods In Neuronal Modeling From Ions To Networks , 2016 .

[17]  N. Munakata [Genetics of Caenorhabditis elegans]. , 1989, Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme.

[18]  Wei Chen,et al.  Delta-Complete Analysis for Bounded Reachability of Hybrid Systems , 2014, ArXiv.

[19]  Paolo Zuliani,et al.  ProbReach: verified probabilistic delta-reachability for stochastic hybrid systems , 2014, HSCC.

[20]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[21]  C. Rankin,et al.  Analyses of habituation in Caenorhabditis elegans. , 2001, Learning & memory.

[22]  E. Kandel,et al.  Molecular biology of learning: modulation of transmitter release. , 1982, Science.

[23]  Cori Bargmann,et al.  A circuit for navigation in Caenorhabditis elegans , 2005 .

[24]  C. Kenyon,et al.  The nematode Caenorhabditis elegans. , 1988, Science.

[25]  S. Brenner,et al.  The structure of the ventral nerve cord of Caenorhabditis elegans. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[26]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[27]  William S. Ryu,et al.  An Imbalancing Act: Gap Junctions Reduce the Backward Motor Circuit Activity to Bias C. elegans for Forward Locomotion , 2011, Neuron.

[28]  W. Rall Cable theory for dendritic neurons , 1989 .

[29]  S. R. Wicks,et al.  A Dynamic Network Simulation of the Nematode Tap Withdrawal Circuit: Predictions Concerning Synaptic Function Using Behavioral Criteria , 1996, The Journal of Neuroscience.

[30]  Wei Chen,et al.  dReach: δ-Reachability Analysis for Hybrid Systems , 2015, TACAS.