Deconstructing complexity to reveal quantitative systems-level principles that enable multicellular systems to coordinately regulate genes over space and time

Project Information

MultiCellSysBio

Grant agreement ID: 677972

DOI

10.3030/677972

Project terminated on 31 December 2020

EC signature date 29 February 2016

Start date 1 April 2016

End date 31 March 2021

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

TECHNISCHE UNIVERSITEIT DELFT
Netherlands

Periodic Reporting for period 4 - MultiCellSysBio (Deconstructing complexity to reveal quantitative systems-level principles that enable multicellular systems to coordinately regulate genes over space and time)

Reporting period: 2020-10-01 to 2021-03-31

Summary of the context and overall objectives of the project

The survival of many organisms depends on cells at different locations communicating with each other so that each cell performs the correct action at the right time and place. For example, for an embryo to develop into a fully-formed organism, different cells inside the embryo must divide the right number of times - this ensures that enough cells exist to form a fully developed organism - and communicate with each other through signalling molecules for the cells at the right locations to form the right body parts. Central to embryonic development and functioning of multicellular entities - such as tissues and biofilms - are cells located at multiple places secreting signalling molecules to communicate with each other in order to collectively decide which genes should be expressed at what time and in which cell. But the common rules that govern these diverse systems and the strategies that cells use for cell-cell coordination of gene expressions have so far been elusive. A major goal of my laboratory's ERC-funded project is tying together seemingly disparate multicellular systems under a common set of quantitative rules that govern their operation. These systems all use two fundamental forms of cell-cell communication. First, in “autocrine signalling” cells produce both a signalling molecule and its receptor. Second, in “paracrine signalling” cells without a receptor secrete a molecule that another cell with a receptor detects. Revealing the common strategies that cells from different organisms use to coordinate their gene expressions requires systematically disentangling the complex web of cell-cell communication to determine which cell talks to which other cell at the single-cell level. It also requires quantifying how individual components of the multicellular system, such as the location of each cell and genetic circuits inside individual cells, affect each cell’s gene expression. This has been challenging because the effects of numerous components of multicellular systems are often intertwined in complex ways. Using my expertise at the interface of physics and systems biology, I will disentangle and measure these effects to find quantitative principles that enable cell-cell coordination of gene expression. We will also investigate how cell-growth and cell-division - both of which are fundamental for development of organisms and functioning of other multicellular entities - are coupled to cell-cell communication via diffusing signalling molecules. To achieve these aims, we will build mathematical models, develop new theoretical frameworks for studying cell-cell communication, and perform experiments on engineered yeast cells (and potentially mammalian cells as an extension). As a fundamental research, this work will provide insights into diverse multicellular systems.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We published 2 research papers and 1 review paper. Additionally, my group has published another review paper on the topic of cell-cell communication via diffusing molecules with ERC's support. In the two research papers, we have developed a new theoretical framework for understanding how spatial patterns arise in a field of cells that communicate through diffusing molecules. Spatial patterns arise from cells coordinating their gene expressions by sending signalling molecules to each other. Existing models - namely those using the Turing-patterning / reaction-diffusion equation - explain how spatial patterns form in a continuous field of cells (i.e. field of infinite number of cells) but they cannot account for fields with finite numbers of cells (e.g. 100s to 1000s of cells) in which the "grainy-ness" of cells must be taken into account. In two research papers (Maire and Youk; Olimpio, Dang, and Youk), we developed a theoretical framework to study fields with finite numbers of cells that form spatial patterns. In particular, Maire and Youk (Cell Systems, 2015) introduced a quantity called "entropy of population", which is the total number of static spatial patterns - which are patterns that remain still after being formed - that an arbitrary number of cells can create by communicating amongst them to coordinate their gene expressions. The entropy of population applies to any type of cells, whether they be bacteria or mammalian cells, since the work treats generic, arbitrary cells that possess a common circuit motif that is found across different species. In the second theoretical research-paper (Olimpio, Dang, and Youk, iScience (2018)), we further developed the framework that Maire and Youk introduced. The developed framework can now account for fields of cells that use more than one specie of signalling molecule and consisting of multiple types of cells. We have also developed the theoretical framework so that it can account for noisy sensing of the signalling molecule(s). We found that moderate amount of noise in gene-expression - which causes each cell to sense slightly different concentration of the signalling molecule from each other - to lead to more spatially organized patterns. We developed an analytical method - a method involving only pen and paper - that can replace exhaustive, complex computer simulations that involve many parameters for understanding spatial-pattern formation by a field of finite number of cells. We developed an analytical framework that uses a "pseudo-energy landscape" which is like the potential-energy landscapes in physics. Our theoretical framework provides insights into spatial pattern formations that complex computer simulations cannot provide. On the experimental side, we have been working with yeast cells with natural and engineered gene circuits to understand how cell-cell communication can control cell-proliferation, and in turn, how this affects the population dynamics.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The theoretical framework that we developed is a new approach to understanding how cells form spatial patterns by communicating amongst themselves (Maire and Youk (Cell Systems 2015); Olimpio, Dang, and Youk (iScience 2018)). This framework provides insights into the principles that govern self-organization of spatial patterns by fields of cells that complex, exhaustive computer simulations that have been used thus far could not provide. There was no theoretical framework to understand this process of field of finite numbers of cells, prior to our work. In the future, we expect to further develop this framework to include dynamic spatial-patterning (e.g. travelling waves formed by a field of cells) in which gene-expression of each cell constantly changes over time, in a coordinated manner, such that spatial patterns can change in a predictable manner over time. We have already made progress in this direction, which we hope to report in a publication next year. We are also working on disseminating this framework and relevant computer simulations accessible to the general public and other researchers by releasing an open-source computer code that lets one visualize spatial-patterning processes (see URL below). On the experimental side, we have discovered novel features in cell-cell communication that control cell-population dynamics. For example, we found that autocrine and paracrine signalling controls death and proliferation rates of cells. We hope to publish these results next year and follow up on them in mammalian cells.

graphical-abstract-180218v3.jpg