What is Multicellularity?

• Multicellularity refers to organisms made up of multiple cells that cooperate and work together, unlike unicellular organisms that function alone.
• Multicellular organisms have a division of labor, with different cell types performing specialized tasks to ensure the organism’s survival.
• This transition from unicellular to multicellular life is one of the most significant events in evolutionary history.

Why Study Synthetic Multicellularity?

• Synthetic multicellularity involves bioengineering to create artificial multicellular systems.
• This research helps us understand complex biological processes like regeneration, disease, and cognition by building multicellular systems from scratch.
• Studying synthetic systems allows us to explore the principles of multicellular life without the constraints of natural evolutionary processes.

What Are the Different Types of Synthetic Multicellular Systems?

• Synthetic Multicellular Circuits: These are engineered cellular circuits within living cells, modified using genetic engineering to form logical circuits.
• Programmable Synthetic Assemblies: These systems rely on cell adhesion and spatial organization to build complex structures that can self-organize and form predictable patterns.
• Synthetic Morphology and Agential Materials: These involve using living materials, such as organoids and biohybrids, to create living systems capable of performing complex tasks autonomously.

How Do Synthetic Multicellular Circuits Work?

• These circuits are created by modifying individual cells to respond to specific signals in the environment, allowing them to perform logical operations like “AND” and “OR” gates.
• They are often used to study basic cellular behaviors, such as how cells interact with each other and their environment to form patterns.
• These circuits are the building blocks of synthetic multicellular organisms and can be engineered to perform tasks like sensing changes in their environment.

How Are Programmable Synthetic Assemblies Made?

• In these systems, cells are engineered to sort themselves based on their “adhesion energy,” essentially how well they stick to each other.
• This sorting mechanism allows cells to self-organize into specific structures without needing external guidance.
• Once organized, these assemblies can be used to study how complex forms and behaviors emerge from simple cellular rules.

What Are Synthetic Morphology and Agential Materials?

• These systems go beyond just modifying genes or circuits. They involve creating complex living materials capable of performing tasks autonomously, like moving or self-repairing.
• Examples include “biobots,” which are living robots made from biological tissue and engineered to complete specific tasks, such as moving objects or repairing damaged cells.
• These living materials can display behavior, adaptation, and even learning without the need for traditional programming.

What Challenges Do Scientists Face in Creating Synthetic Multicellular Systems?

• The main challenge is the unpredictability of how cells will behave when they are engineered to perform complex tasks.
• Biological systems are not like traditional machines. They are influenced by many factors, including genetic variations, cell interactions, and environmental changes, making it difficult to predict their behavior.
• Designing multicellular systems that are predictable and reliable requires understanding how different cell types communicate and coordinate with each other to form functional structures.

What Are the Open Problems in Synthetic Multicellularity?

• Synthetic Developmental Programs: How can we create programs that guide synthetic multicellular systems through development stages, similar to how natural organisms develop?
• Embodied Memory and Learning: Can we design systems that have memory and learning capabilities without relying on traditional neural networks?
• Synthetic Collective Intelligence: How can we harness the power of collective intelligence, seen in animal societies, to create synthetic systems that work together to solve complex problems?
• Synthetic Neural Cognition: Can we design synthetic systems that mimic cognitive functions, such as learning and decision-making, found in living organisms?

What Could the Future Hold for Synthetic Multicellularity?

• In the future, synthetic multicellular systems could be used in medical applications, such as creating artificial organs or tissue that can self-repair.
• These systems could also lead to advances in bioengineering, where living systems are designed to perform specific tasks, like sensing environmental changes or even interacting with human cells.
• The ultimate goal is to design synthetic organisms with the ability to learn, adapt, and solve problems on their own, pushing the boundaries of what is possible in biotechnology.

什么是多细胞性？

• 多细胞性是指由多个细胞组成的生物体，这些细胞合作共事，而不像单细胞生物那样单独行动。
• 多细胞生物体具有分工，不同类型的细胞执行专门的任务，以确保生物体的生存。
• 从单细胞到多细胞的转变是进化历史上最重要的事件之一。

为什么研究合成多细胞性？

• 合成多细胞性涉及生物工程，用来从零开始创建人工的多细胞系统。
• 这一研究帮助我们通过构建多细胞系统来理解复杂的生物过程，如再生、疾病和认知。
• 研究合成系统使我们能够在没有自然进化过程限制的情况下，探索多细胞生活的基本原理。

合成多细胞系统有哪些不同类型？

• 合成多细胞电路：这些是通过基因工程修改细胞，形成逻辑电路的合成生物学系统。
• 可编程合成集合体：这些系统依赖于细胞粘附和空间组织，通过细胞相互作用来自我组织，形成可预测的模式。
• 合成形态学和代理材料：这些系统使用生命材料，如类器官和生物机器人，创建能够执行复杂任务并具有某种自主性的生命系统。

合成多细胞电路是如何工作的？

• 这些电路通过修改单个细胞，使其响应环境中特定的信号，从而执行“与”与“或”逻辑操作等任务。
• 它们通常用于研究细胞如何相互作用并与环境形成模式的基本行为。
• 这些电路是合成多细胞生物体的构建模块，可以设计成执行感知环境变化等任务。

如何制作可编程合成集合体？

• 在这些系统中，细胞通过“粘附能量”来排序，粘附能量决定了细胞之间的连接力。
• 这一排序机制使得细胞能够自我组织成特定的结构，而无需外部引导。
• 一旦组织起来，这些集合体就可以用来研究如何从简单的细胞规则中出现复杂的形态和行为。

什么是合成形态学和代理材料？

• 这些系统不仅仅是修改基因或电路，它们涉及创建能够执行任务的复杂生命材料，如移动或自我修复。
• 一个例子是“生物机器人”，它是由生物组织构成的活机器人，设计用来完成特定任务，如移动物体或修复受损细胞。
• 这些生命材料可以表现出行为、适应，甚至在没有传统编程的情况下进行学习。

创建合成多细胞系统面临哪些挑战？

• 主要的挑战是预测工程化细胞在执行复杂任务时的行为。
• 生物系统不像传统机器那样，受多种因素的影响，包括基因变化、细胞相互作用和环境变化，使得它们的行为难以预测。
• 设计出可靠且可预测的多细胞系统需要理解不同细胞类型如何相互协调，形成功能性结构。

合成多细胞性中的开放问题有哪些？

• 合成发育程序：我们如何创建能引导合成多细胞系统经历发育阶段的程序，就像自然生物体那样？
• 具身记忆和学习：我们能否设计出没有依赖传统神经网络的记忆和学习能力的系统？
• 合成集体智能：我们如何利用群体智能的力量创建合成系统，使它们能够合作解决复杂问题？
• 合成神经认知：我们能否设计出模拟认知功能（如学习和决策）的合成系统？

合成多细胞性未来可能会怎样发展？

• 未来，合成多细胞系统可以用于医疗应用，例如创建可以自我修复的人工器官或组织。
• 这些系统还可以推动生物工程的发展，用于创建能够执行特定任务的生命系统，例如感知环境变化，甚至与人类细胞互动。
• 最终目标是设计出能够学习、适应并独立解决问题的合成生物体，推动生物技术的边界。