Project:	Growing brain cells on a computer: multimodal data-driven cellular model optimization and validation using gradient-based methods
Disciplines:	Multidisciplinary, Computation and Mathematical Sciences
Mentor:	Costas Anastassiou, Associate Professor, (EAS), costas@caltech.edu
Mentor URL:	Anastassioulab.org  (opens in new window)
Background:	The nervous system consists of cell classes defined by specific molecular signatures, morphologies, or electrophysiological properties. In recent years, single-cell characterization of neurons, mainly propelled by advances in single-cell RNA sequencing, has revealed a multitude of “cell types”. Despite our ever-increasing ability to detect distinguishing molecular, morphological, and electrophysiological features to differentiate between such types, unraveling causal relationships between data modalities has been difficult. How does a particular distribution of ion channel conductances dictated by gene expression manifest itself in the various electrophysiological features recorded in slice experiments? Experimentally, this process involves elaborate genetic and/or pharmacological manipulations that are difficult to scale.
Description:	Contribute to software development , testing , and deployment on hpc . Analyses of results and methodologies. Generating analysis pipelines figures for publications and presenting at internal and external meetings.
References:	“Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types” Anirban Nandi, Thomas Chartrand, Werner Van Geit, Anatoly Buchin, Zizhen Yao, Soo Yeun Lee, Yina Wei, Brian Kalmbach, Brian Lee, Ed Lein, Jim Berg, Uygar Sümbül, Christof Koch, Bosiljka Tasic, Costas A Anastassiou, Cell reports, 40:6 (2022) “Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex” Yina Wei, Anirban Nandi, Xiaoxuan Jia, Joshua H Siegle, Daniel Denman, Soo Yeun Lee, Anatoly Buchin, Werner Van Geit, Clayton P Mosher, Shawn Olsen, Costas A Anastassiou, Nature communications, 14: 1, 2344 (2024) “NOBLE--Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models” Luca Ghafourpour, Valentin Duruisseaux, Bahareh Tolooshams, Philip H Wong, Costas A Anastassiou, Anima Anandkumar, Advances in Neural Information Processing Systems (in press)
Student Requirements:	Strong computational and coding (Python) and data wrangling skills are essential.
Programs:	This AO can be done under the following programs:   Program    Available To        Amgen Scholars    Non-Caltech students only          SURF    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.