Formal Model of Living Organisms
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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.
Aalto University
q-bio.PE
Submitted: 2013-04-18
Updated: 2013-06-13
Comments: Submitted to the 12th European Conference on Artificial Life (Ecal 2013), manuscript revised before final extended submission deadline
DOI: 10.1162/978-0-262-31709-2-ch106
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Importance score: 69/100
The gist: 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
Key concepts
- Systems View
- This approach seeks a universal language to describe all living things. Instead of relying on vague lists of life properties, it aims to find fundamental principles that connect the laws governing simple cells to complex organisms and artificial systems.
- Minimal Model Approach
- To manage complexity, the model starts with the simplest case: unicellular life. By understanding this minimal system first, researchers can then propose how more complex forms of life, like multicellular organisms, can be modeled using the same foundational principles.
- Active and Survival States
- A living system is modeled as existing in two states: 'active,' where it uses energy to function and reproduce, or 'survival,' where it maintains its existence. The transition between these states is discrete and irreversible, dictated by energy availability.
- Adaptive Evolution Modeling
- The framework allows the model to simulate how life adapts. Entities can evolve their behavior based on whether reproduction or survival is more advantageous in their current environment, mimicking Darwinian selection through mutation and fitness changes.
Summary
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.
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. 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. Currently there is neither commonly approved scientific vocabulary nor practical conceptual tools for forming uniform descriptions of life and living systems. Researchers in many different fields currently find themselves pondering what is the essence of life and living. The question is: How do artificial and engineered systems and their properties relate to the prototypic biological organisms and natural life? How to test their “life-likeness” and living potential? How to predict the evolutionary trajectories of organism-like or life-like innovations that may reside outside the biochemistry-based internal world of cells and organisms, or even be completely information-based.
Lists of Life
Currently a typical way to define life is to form a list of properties that a system should have in order to be classified as living one. For example Farmer and Belin (1990, 1991) proposed the following list of properties admitting at the same time that any such list is bound to be both imprecise and incomplete. Another list (Koshland, 2002) provides seven key principles as basis for life. These two examples already demonstrate that terminology is neither universal nor self-explanatory.
Minimal Model Approach
The task of choosing an appropriate level of observation is of central importance here – as always when modeling complex phenomena (Checkland, 2000, Flood and Carson, 1993) My solution is a two-step approach: First only unicellular life is considered. Then by comparing the resulting minimal model view of unicellular life to what is generally known about real more complex forms of cellular life including the structure function and evolution of different kinds of life cycles and multicellular organisms (plants animals and fungi) it is possible to propose how they too can be modeled within the same principle formalism.
System Outline
Based on this I propose the following minimal formalism for describing unicellular life (summarized in Figure 1) There are two states in which the conceptual living system (cell or organism) can exist. In the active state the organism is in the active state and it comprises all of typical functions of metabolically active cellular life. Maintaining this state requires energy and the cell must obtain it from the environment or from its own internal energy stores. If the cell runs out of energy while it is in the active state, it will die. The minimal model survival transition is assigned to be discrete and irreversible.
The Opposite Ends of Life
An organism that relies entirely only on reproductive active-state living corresponds to a biphasic system case where the probability for entering survival state is zero. An extreme case of the opposite type could be presented by an organism that has demonstrably lived subsequently entered survival state and then appears to remain there indefinitely thereby approaching the borderline of even being a living system anymore.
Evolution In-between Reproduction and Survival
The ability to undergo adaptive evolution is a defining feature of biological organisms. Starting from the minimal-model case the formal reproduction-survival state-transition framework may adapt and evolve to its environment in many different ways just like a cell can undergo Darwinian adaptive phenotypic evolution with mutation and selection. In the simplest possible minimal model scenario the survival transition may be direct and discrete but in reality it seldom is. It appears that the cells of most real organisms spend most of their time in states where they invest simultaneously and in varying ratios to both reproduction ability and survival probability.
Multicellular Organisms
Uncontrolled reproduction is no longer the sought-for state for the primary existence of this organism’s cells as demonstrated by cancerous cells. Multicellular individuals are considered to be organizational entities of lower level unit-structures. As individuals they become subject to the same system principles of reproduction survival state transition dynamics as are the cells on the lower level of organization. The next level of complexity expectedly arises when these multicellular individuals form social groups.
Improvements for AI systems
- Bold header: Minimal Model Implementation
The AI system will operate based on a discrete reproduction-survival state-transition system,
allowing it to model entities through two states: Active state
and Survival state.
This enables the AI to formally describe how an entity transitions between these states based on environmental cues, such as energy availability, rather than simply executing pre-defined actions.
- Bold header: Adaptive Evolution Modeling
The system will incorporate mechanisms for adaptive evolution by allowing the formal framework to adapt and evolve to its environment in many different ways,
mimicking Darwinian selection. This means the AI can simulate how entities undergo Darwinian adaptive phenotypic evolution with mutation and selection
based on fitness derived from reproduction versus survival probabilities.
- Bold header: Intermediate State Dynamics Simulation
The system will model complex life cycles by utilizing intermediate metastable transition states (m-states)
to describe the progression of change, such as in yeast spore formation or parasite differentiation. This allows the AI to capture the balancing act between cellular reproduction and survival functions
as a stage-wise realization of survival transition biology.
- Bold header: Multicellular Organization Modeling
The formalism can be extended to model multicellularity by treating individual cells as intermediate metastable survival transition cells
that adopt roles within a larger entity. This allows the AI to study how "the more layers of living organization there are in the formal description of an individual organism, the more complex and multilayered the interaction patterns within and between reproduction-survival state-transition dynamics on all levels of its organization."
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