Understanding Synaptic Wiring Rules in the C. Elegans Brain

Abstract: Ongoing advances in brain imaging and single-cell RNA sequencing have produced a massive amount of data on the genetic identity of neurons and their synaptic connections. However, these advances set up a complexity bottleneck: We need guiding frameworks to integrate and conceptualize this data, distill the key emergent patterns and aid new biology discovery. Addressing this knowledge gap, we will focus on the following critical questions: i) What are the connection rules of neural networks, governing the emergent network structure and wiring mechanisms? ii) How can we integrate the existing connectomics, proteomics and transcriptomics datasets into a coherent and predictive theoretical framework? To start, we need to decode the genetic programs behind synapse formation and maintenance to make sense of the data and gain insight into the network organization and functional circuitry of the brain. As a solution, we propose a scalable modeling framework building upon our recently pioneered Spatial Connectome Model (SCM). The central hypothesis of the SCM is that synapses emerge due to an underlying wiring rule network that connects pre-synaptic and post-synaptic neuron features. First, we will develop scalable solutions to the SCM, using two alternative approaches, a Bayesian framework and an expectation maximization route. While the original model was linear, the underlying biological rules are highly non-linear and we aim to introduce and solve the non-linear SCM. In addition, we will introduce and solve a local SCM, allowing for the wiring rules to vary over different parts of the network. We will also develop a novel mathematical framework to debias experimental data for protein-protein interactions, extending the maximum entropy framework. Our research strategy focuses on the C. elegans as a model organism and combines tools from neuroscience, molecular biology, network science, and statistical physics to capture complex wiring mechanisms as well as key biological constraints. We will provide a series of falsifiable predictions, starting with i) neuron wiring rules, and ii) inferring missing synapses from the input data, as well as iii) inferring changes in the connectome upon genetic perturbations. We will then experimentally validate the key predictions of our computational framework using in vivo and in vitro approaches in the C. elegans.