Research

What does "being dead" mean?

Our goal:

What distinguishes a living system from a non-living system? Both are made of atoms and governed by the same physical laws. Neither one atom nor two atoms forms a living system. But step by step, as we connect three, four, and eventually many Avogadro numbers of atoms together in just the "right" way, we obtain a complex system characterized by dynamics that we associate with life - a living system. Indeed, living systems are dynamic. We associate motion, chemical reactions, and information processing with being alive. But non-living systems can also be dynamic. How do the dynamics of living matter differ from those of non-living matter?

Long-lived, complex dynamics of living systems:

"Living matter evades the decay to equilibrium", according to Schrodinger. That is, living matter generates and maintains systems-level dynamics - involving many components and processes - that do not halt for a long time, lasting longer than the lifetimes of the molecules involved and beyond the timescales of the constituent chemical processes. These systems-level dynamics tend to be complex, exhibited by one or more cells.

Our lab seeks to quantitatively explain the *capacity* that living systems have for generating and maintaining long-lived, complex dynamics that non-living matter hardly ever exhibits . Permanently losing that capacity, such as in dormancy, would mean that the cell or a multicellular system can no longer achieve those dynamics. Moreover, even if a system is not dynamic now, having the capacity to achieve those dynamics means that it can still restart and maintain long-lived, complex dynamics that we associate with life.

To achieve our goal, we perform real and computational experiments that we interpret through the language of dynamical systems. Thus far, we have been focused on two related, long-lived dynamics that are arguably unique to living systems: 1.) dynamics of self-replication and how a non-replicating cell maintains self-replicability; and 2.) self-organization.



1. Self-replication dynamics : limits to keeping it going and when it can stop and resume

Cells maintain viability - their ability to self-replicate - for much longer than the lifetimes of their constituent molecules and the timescales of the chemical processes that coalesce to yield self-replication. Moreover, self-replication is a complex dynamics in that it appears to be functionally periodic - the cell repeatedly self-replicates - but is physically aperiodic because the cell does not have the same copy number of every molecule, each at exactly the same location, as it self-replicates again. Cells may also affect each other's replication, raising the complexity. As a dynamical system, how does a cell maintain its viability - its non-periodic self-replication dynamics - for a long time? Are there unbeatable constraints to maintaining this dynamics? To address these questions, we are using two approaches: (1) slowing down a cell's life to discover constraints to maintaing self-replication dynamics and (2) halting a cell's life and then resuming its life - examining "lifeless-to-alive" transition - to discover how a static cell can become dynamic (self-replicating) again.

Approach 1 - Slowing life to reveal limits of self-replication dynamics:

As we gradually slow down all intracellular processes, does a living system break down (stop living) at some point? Or can life progress at an arbitrarily slow speed? How do we even measure a "speed of life"? We're addressing these questions by drastically slowing a cell's self-replication dynamics by, for example, increasing or decreasing temperature to extreme values or inducing growth-slowing genetic programs (e.g., cell differentiation).


                             Macroscopic quorum-sensing ES cells
H. Daneshpour, P. van den Bersselaar, C.-H. Chao, T. G. Fazzio, and H. Youk
Macroscopic quorum sensing sustains differentiating embryonic stem cells
Nature Chemical Biology (Jan. 2023)
   Online version (.html)       News & Views on this work (.pdf)   

                                 Yeast at 1C
D. S. Laman Trip, T. Maire, and H. Youk
Slowest possible replicative life at frigid temperatures for yeast
Nature Communications (Dec. 2022)
   Online version (.html)       Article in PDF       Supplementary Info + Theory (.pdf)       Supp. Movies (.html)   
Twitter Tweets summarizing this work

           Phase diagram for cell replication (budding yeast)

D. S. Laman Trip and H. Youk
Yeasts collectively extend the limits of habitable temperatures by secreting glutathione
Nature Microbiology (April 2020)
   Online version (.html)       Article in PDF       News & Views (.pdf)   
Twitter Tweets summarizing this work

Approach 2 - Restarting halted life to understand lifeless-to-alive transition

Can we completely stop any living system and then resume its life by presssing a "button" as in sci-fi movies? If yes, then how? If not, then why can't the dynamical system (cell) restart its self-replicative dynamics? We are investigating this question with orgnanisms whose lives have halted and thus seem lifeless while maintaining the ability to resume self-replicatiion (e.g., dormant yeast spores).


                                   Dormancy spectrum

T. Maire, T. Allertz, M. A. Betjes, and H. Youk
Dormancy-to-death transition in yeast spores occurs due to gradual loss of gene-expressing ability
Molecular Systems Biology (November 2020)
   Online version (.html)       Article in PDF   
Twitter Tweets summarizing this work

2. Self-organization dynamics : Computational experiments to reveal dynamics of spatial-order generation by cells

Living systems excel at forming ordered structures from disorganized components, as exemplified by cell replication. To understand self-organization dynamics, particularly spatial pattern formation, we use cellular automata as computational experiments. These automata employ discrete approximations, such as binary actions, to represent biological processes. Binary actions like "protein A is either bound or not bound to protein B" and "gene is either highly or lowly expressed" can effectively capture essential cellular rules. Incorporating non-nearest-neighbor interactions in our cellular automata helps reveal large-scale and long-lasting dynamics characteristic of spatial-pattern-forming cells.

We analyze known and hypothetical gene regulatory rules using cellular automata to identify those that enable cells to form spatially ordered configurations, such as spiral waves. Additionally, we develop methods to quantify the evolving spatial order in a field of communicating cells forming patterns. While certain spatial patterning mechanisms are well-understood, our knowledge of pattern formation through non-local interactions, like diffusing molecules used for gene regulation, remains limited.

Our research shows that non-locally interacting cells can generate complex, spatially organized dynamics, some of which are observed in nature, while others await discovery. Our objective is to investigate all potential cellular dynamics generated by biologically relevant discrete rules using cellular automata, offering a comprehensive perspective on living systems as discrete, dynamical systems and differentiating them from non-living systems.


Wave formed by cells
L. Koopmans and H. Youk
Predictive landscapes hidden beneath biological cellular automata
Journal of Biological Physics (Nov. 2021)
   Online version (.html)       review (.pdf)   

                 Wave formed by cells
Y. Dang, D. A. J. Grundel and H. Youk
Cellular dialogues: cell-cell communication through diffusible molecules yields dynamic spatial patterns
Cell Systems (January 2020)
   Online version (.html)       Article in PDF   

Mapping cellular automaton to a drifting-diffusing particle

E. P. Olimpio*, Y. Dang*, and H. Youk (*Co-first authors)
Statistical dynamics of spatial-order formation by communicating cells
iScience (April 2018)
   Online version (.html)       Article + Supp. Info (.pdf)   

                                                                      Positive feedbacks and controlling signal-range


T. Maire and H. Youk
Molecular-level tuning of cellular autonomy controls collective behaviors of cell populations
Cell Systems (November 2015)
   Online version (.html)       Article in PDF       supplementary material (.pdf)   

Funding sources

  • We thank the following organizations for funding our work, currently or in the past:

    • NIGMS MIRA ERC (European Research Council)

    NWO

    EMBO YIP

    CIFAR


















The same dynamical system - the same equations that (A) represents - explains the self-replication dynamics and permanent loss of self-replicability for budding yeast cells at high temperatures (in B) and frigid temperatures (in D) and for differentiating ES cells (in C). Red curves in (B) and (D) represent populations that become extinct (note: dead cells were detected as “particles” in these graphs). Click on the image to learn more.





























In Maire et al. (2020), we used yeast spores as a model for eukaryotic cell-dormancy to show that some dormant spores can express genes without any nutrients (e.g., in water). In fact, we discovered that dormant yeast spores must express genes to remain viable (i.e., to have the ability to self-replicate when nutrients appear). How can dormant spores even express genes for months to years without any external nutrients? What are the principles of gene expression in dormancy? We’re quantitatively addressing these questions in dormant yeast spores. Click on the image to learn more.











“Computational experiments” revealed both static and dynamic spatial patterns (e.g., spiral waves) and the spatial-ordering process through which they form, starting from a field of cells that have no spatial order. Here, each cell secretes and senses one or two molecules, thereby enabling cells to control each other’s gene expression over a long distance (i.e., beyond nearest neighbors). Click on the image to learn more.
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