Workshop Topics

Recent advances in systems and synthetic biology constitute a basis for the realization of the wetware approach to Artificial Life (AL), in addition to hardware and software approaches. Developing AL systems in wetware domains requires the use of chemical and biological materials to construct tools, devices, and systems capable of displaying life-like behaviors such as growth, division, adaptation, plasticity, evolution, autonomy, and other bio-inspired patterns.  

While thermodynamic and kinetic laws governing (bio)chemical processes provide a basis to attack the complex tasks of devising systems that significantly contribute to AL, it is also important to understand the organizational structure of AL systems. It is often noted that the governing principles of organizational structures rely on a characterization of information flow. As a consequence, it is natural to suspect that models and characterizations from information theory and communication theory will be useful in the study of organization in AL. 

The combination of areas such as synthetic biology, systems chemistry, chemical reaction network theory, and chemical organization have already impacted AL, as is often reported within the AL community. On the other hand, the exploration of the so-called “bio-them-ICTs” (bio-chem-information and communication technologies), and the theories behind them, known as “Molecular Communications”, have received—to date—limited attention from the AL community. 

The workshop Molecular Communication Approaches for Wetware Artificial Life aims to fill this gap, providing an arena for discussing how current interest in chemical information and chemical communication can converge with AL, especially in the context of synthetic biology and systems chemistry approaches. The field of Molecular Communications, recently developed from an engineering perspective, can provide valuable tools for achieving a higher degree of complexity in AL systems, including: (i) Synthetic/Artificial Cells or Protocells and their assemblies; and (ii) hybrid biological/artificial systems (e.g., Synthetic Cells that can communicate with biological cells; hardware/software microsystems interfaced to biological systems; networks made of both artificial and biological entities).

Some of the questions that we would like to address in this workshop are:

• What are the principles and models of molecular information and communication in wetware AL systems? How can we measure and compare the information content and flow in such biochemical systems? 

• How can engineering of molecular communication enhance the functionality and complexity of AL systems? 

• How can we create synthetic/artificial cells or protocells that can communicate with each other or with natural cells using MC? What are the design principles and mechanisms for generating emergent behaviors in synthetic/artificial cell populations or networks towards AL? 

• How can we model and analyze the network structure and dynamics of artificial and biological components that communicate via MC? What are the effects of noise, interference, feedback, adaptation, evolution, etc. on network behavior?
  
  

We invite all interested researchers to join us at the first version of this workshop at ALIFE 2023,  and to contribute to the discussions on MC  for wetware AL, including but not limited to those topics outlined above. 

Abstracts

An Agent-based Modelling Approach to Molecular Communication and Multicellular Structure Formation
Jiewen Wang, Osaka Metropolitan University, Japan
Tadashi Nakano, Osaka Metropolitan University, Japan

Creating a large-scale structure from a group of bio-nanomachines is key for engineering applications of molecular communication. In this talk, we will present our agent-based modeling approach to molecular communication and multicellular structure formation. First, we introduce our agent-based model, where a bio-nanomachine is represented as an autonomous agent with state variables, incorporating common cellular behaviors. Next, we utilize this model to describe specific multicellular molecular communication systems. Finally, we will showcase the design and implementation of a simulator we are developing to test the agent-based models.

Construction of Autopoietic Artificial Cell –Toward Construction of Self-Reproducing Man-Made Cells–
Yutetsu Kuruma, Earth–Life Science Institute, Japan

Building cellular lives from molecules and genes is the biggest challenge in life science. Self-reproduction is the most remarkable feature within the definitions of life and is standing as a difficult problem to reproduce by assembling non-life matters. A model in Autopoiesis theory presents the self-reproduction of cells as a simple structure, but no one has developed such systems in artificial cell study. Here, we show a prototype of an artificial cell that synthesizes lipids inside lipid membrane vesicles. The constructed artificial cell faithfully embodies the autopoietic system and may promise the realization of self-reproducing artificial cells.

Invited Talk: Discovering Gene Regulatory Neural Network towards Realizing Biological AI
Sasitharan Balasubramaniam, University of Nebraska-Lincoln, USA

In recent years, we have started to witness the widespread applications of Artificial Intelligence and Machine Learning for diverse application (e.g., computer vision). This has resulted in software-based systems for AI, such as Artificial Neural Networks as well as hardware-based systems like neuromorphic processors. To date the development of AI aims to mimic the behavior and realism of neurons and their internal functionalities. However, we are also witnessing a different direction in creating AI by directly using living cells (e.g., neurons) and integrating this into conventional computing systems. This talk will present the use of bacteria for Biological AI. The talk will start with discussion on the current state-of-the-art in using living cells to realize neural network properties, before we dive deep into the concept of Gene Regulatory Neural Networks (GRNN), where we use the gene regulation process to create neural networks. The presentation will include mechanism of discovering GRNN as well as analysis on its reliability for computing within a population of cells based on their communications. We will also analyze how we can realize a single perceptron model from a population of cells through their communication process.