BCNNM: A Framework for in silico Neural Tissue Development Modeling

Dmitrii V Bozhko; Georgii K Galumov; Aleksandr I Polovian; Sofiia M Kolchanova; Vladislav O Myrov; Viktoriia A Stelmakh; Helgi B Schiöth

doi:10.3389/fncom.2020.588224

BCNNM: A Framework for in silico Neural Tissue Development Modeling

Front Comput Neurosci. 2021 Jan 20:14:588224. doi: 10.3389/fncom.2020.588224. eCollection 2020.

Authors

Dmitrii V Bozhko¹, Georgii K Galumov¹, Aleksandr I Polovian¹, Sofiia M Kolchanova^{1

2

3}, Vladislav O Myrov^{4

5}, Viktoriia A Stelmakh^{1

6}, Helgi B Schiöth^{7

8}

Affiliations

¹ JetBrains Research Department, Space Office Center, Saint Petersburg, Russia.
² Department of Biology, University of Puerto Rico at Mayaguez, Mayaguez, PR, United States.
³ Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, Saint Petersburg, Russia.
⁴ Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
⁵ Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
⁶ Skolkovo Institute of Science and Technology, Center of Life Sciences, Moscow, Russia.
⁷ Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden.
⁸ Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia.

Abstract

Cerebral ("brain") organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.

Keywords: axon guidance; brain organoid; neurogenesis; neuronal connectivity; simulation; tissue development.