Formal Model of Living Organisms

Introduction to the show: ident: Genomics Radio. Generated commentary on the latest computational biology and genomics papers.

Ines: We're looking at the paper: "Formal Model of Living Organisms".

Marcus: Here's the gist of the paper. The gist The proposed modeling approach enables introduction of general systems formalism for the description and definition of very different kinds of biological systems from a common systems point of view.

Ines: Systems View The concept of an organism as a prototypic “living thing” is very intuitive for humans, but understanding life in formal terms remains a great challenge for modern science.

Marcus: The immense diversity and complexity of biological organisms has made it difficult to find general answers as to what the “laws of living systems” may be and how they relate to the laws of physics and chemistry.

Ines: Currently there is neither commonly approved scientific vocabulary nor practical conceptual tools for forming uniform descriptions of life and living systems.

Title and authors: Marcus: So we’re digging into the title and authors of this paper, "Formal Model of Living Organisms," and what that actually means for us in the long run. It’s not just a fancy name; it’s about building a structure for life itself.

Ines: Yes, and when you look at who wrote it, Margareta Segerståhl from the Department of Biomedical Engineering and Computational Science at Aalto University is setting up this conceptual modeling formalism based on the real life and evolution of biological organisms.

Yuki: From a population genetics standpoint, I see that her focus on reproduction and survival as the core functions really grounds it in evolutionary biology, which is where we usually look when we try to understand how these systems have adapted over time.

Marcus: And what this paper does is propose a modeling approach that uses these two fitness components to determine the system framework, starting with a very simple model of a discrete reproduction-survival state-transition system.

Ines: It’s about taking those two core functions and applying them systematically to describe living and life-like systems across multiple levels of organization, aiming for a holistic study from one common systems point of view.

Yuki: So it’s not just describing a single cell, but building a language that can connect the dynamics happening at the cellular level to the bigger picture of how organisms survive and evolve.

Marcus: That’s the big picture, Yuki. It tries to bridge that gap between pure physics and chemistry by focusing on these fundamental life-like operations.

The paper's summary: Ines: To summarize what this paper says, it uses this two-state system where the conceptual living system—like a cell or an organism—can exist in either an active state or a survival state.

Marcus: In the active state, the organism is doing all the typical functions of metabolically active cellular life, and maintaining that requires energy from either the environment or its own internal stores. If it runs out of energy while being active, it dies.

Yuki: And that transition between those two states is assigned as discrete and irreversible in this minimal model survival transition framework.

Ines: The paper then explores the opposite ends of life, showing how an organism relying only on reproductive active-state living is a biphasic system case where the probability for entering the survival state is zero.

Marcus: And they touch on an extreme case too, suggesting that some organisms might have demonstrably entered a survival state and then stay there indefinitely, almost like they’re no longer really living systems anymore.

Yuki: This model structure lets them look at how adaptive evolution works in between reproduction and survival, showing that the framework can adapt to different environments just like a cell can evolve with mutation and selection.

Ines: They argue that in reality, most cells spend most of their time investing in both reproduction ability and survival probability simultaneously, not just one or the other.

The paper's improvements: Marcus: Now for what they suggest improving about the initial minimal model, which is where things get more interesting. They propose incorporating mechanisms for adaptive evolution into this framework so that it can evolve to its environment in many different ways.

Ines: That means the AI system we are thinking about could simulate how entities undergo Darwinian adaptive phenotypic evolution with mutation and selection based on fitness derived from reproduction versus survival probabilities within this state-transition framework.

Yuki: I also see a point where they suggest using intermediate metastable transition states, or m-states, to describe complex life cycles, like spore formation in yeast or how parasites differentiate.

Marcus: That lets the AI capture that balancing act between cellular reproduction and survival functions as a stage-wise realization of survival transition biology, rather than just a single switch between active and survival.

Ines: And for multicellular organisms, the formalism can be extended by treating individual cells as intermediate metastable survival transition cells that adopt specific roles within a larger entity.

Yuki: So if you have more layers of living organization in your description of an organism, the model suggests there will be more complex and multilayered interaction patterns happening between those reproduction-survival dynamics at every level.

Conclusion: Marcus: So to wrap up, the paper "Formal Model of Living Organisms" provides this framework based on two core functions, reproduction and survival, defining a discrete system that can be extended for evolution and complex organization.

Ines: The implication is that this modeling approach offers abstract system basis and conceptual tools for studying life at multiple levels of organization from a unified perspective.

Yuki: It connects the fundamental dynamics of cell survival to the broader history of species by allowing us to see how those basic survival transitions drive evolutionary trajectories.

Marcus: And they also laid out ways to make this framework more robust by including intermediate states and modeling multicellularity as a hierarchy of these transition cells.

Ines: It’s a way to move beyond just listing properties and instead build a dynamic system where the behavior emerges from those reproduction and survival rules.

Yuki: That really gives us something concrete to look at when we try to understand the essence of life, even if it’s still a bit far away from our current tools.