What Does It Mean to Say that Event X Caused Outcome Y in Biology?

• Understanding causality in biology is key to explaining how living systems function and how we can manipulate them, especially in regenerative medicine and bioengineering.
• Traditional thinking about causality focuses on “necessary” and “sufficient” causes, but this view is limited and doesn’t capture the complexity of biological processes.

From Genes to Processes in Developmental Biology

• We know a lot about genes that control tissue development. These genes form pathways that help explain how tissues are formed and what can go wrong in diseases.
• However, focusing only on genes misses the bigger picture. Development is more than a simple catalog of parts. We need to understand how these parts interact and cause the processes they do.
• New perspectives shift the focus from genes alone to the patterns of activity and connections between components in a system.
• This view reveals that biological systems are more complex and interconnected than simple hierarchical diagrams of genes suggest.
• Instead of linear cause-and-effect thinking, we must understand how relationships change over time and lead to outcomes. This is the new challenge in developmental biology.

The Problem with Lists in Modern Biology

• Modern biology has become obsessed with compiling lists, such as sequencing genomes and identifying proteins, but these lists don’t help us understand biological processes.
• Instead of focusing on lists, we need to design experiments that test alternative explanations for observed behaviors.
• These experiments should make predictions about how a model might fit with the observations, helping us distinguish between different possible causes.
• This approach, known as the “critical experiment” approach, is the opposite of merely making lists. It focuses on refining models through testing and data, which leads to deeper understanding.

Biophysical Properties as Causes

• The current Gene Regulatory Network (GRN) models don’t explain how physical factors, like spatial constraints, influence biological systems.
• Constraints act like rules that limit how a system can behave and can push the system into new states that would otherwise be impossible.
• An example is the study of mammalian cells in microgravity. Without gravity, cells show unusual behavior, and when placed back into normal conditions, they form different phenotypes (types of cells with distinct characteristics).
• This shows that physical constraints, like gravity, help cells differentiate into specific types, which would not happen without these forces.
• Constraints guide cells toward a specific state and are essential for processes like differentiation, where cells develop into specialized types (e.g., muscle cells or nerve cells).

Regenerative Biology and the Role of Constraints

• Some organisms, like salamanders, can regenerate limbs. They stop regenerating once the correct structure is formed, showing how biological systems can regulate and organize growth.
• Even when faced with drastic interventions, like abnormal body parts, organisms can still achieve normal development. For example, tadpoles with abnormal faces can still grow into normal frogs.
• This shows that regeneration is not about following a fixed blueprint, but about a flexible system that can remodel itself.
• Bioelectric signals play a critical role in this process. By modulating the electrical state in cells and tissues, researchers can influence the pattern and type of regeneration that occurs.

Cause and Constraint in Biology

• The classic “billiard ball” model of causality, which looks at individual events triggering other events in a linear fashion, is too simplistic for biological systems.
• Biological systems involve branching pathways, feedback loops, and multi-level interactions, which are not captured by linear models.
• An example is the Chladni plate experiment, where sand forms patterns on a vibrating plate. The patterns depend on factors like the plate’s size and shape, and these patterns remain consistent despite the randomness of individual sand grains.
• Similarly, in biology, the focus should be on identifying the constraints that shape patterns, rather than looking for simple cause-and-effect relationships between components.
• New approaches to causality focus on the system as a whole and how constraints guide its behavior, rather than focusing on individual molecular events.

Comparative Approaches to Causality

• Biological causality should be about understanding the function of a process, not just how individual components interact.
• For example, understanding how small GTPases (proteins that regulate cellular processes) help create cellular polarity is important, but understanding the purpose of this regulation (why polarity is needed) is more crucial.
• By comparing different species and how they evolved multicellularity, researchers can uncover fundamental mechanisms that underlie biological processes.
• This approach focuses on understanding the functions of biological systems rather than just their parts, giving insights into how and why certain biological patterns emerge.

Conclusions and Outlook

• Despite knowing about redundancy and self-organizing systems, we still don’t fully understand how complex biological patterns emerge.
• This understanding is critical for fields like regenerative medicine, where we aim to guide cells toward specific outcomes.
• Current models of causality in biology are often too simplistic and need to be rethought, especially as new technologies and data emerge.
• Advances in fields like physics and network science can help us develop better models for understanding biological causality.
• Understanding the full complexity of biological systems will require integrating different approaches, including those from physics, mathematics, and computational biology.
• Ultimately, this will lead to more effective interventions in regenerative medicine, cancer treatment, and synthetic biology.

因果关系在细胞生物学中的重要性

• 在生物学中理解因果关系对于解释生命系统如何运作，以及如何有效地操控它们，尤其是在再生医学和生物工程中，是至关重要的。
• 传统的因果关系思维过于简单，专注于“必要”和“充分”原因，但这种观点不足以解释生物过程的复杂性。

从基因到发育生物学中的过程

• 我们已经确定了控制特定组织发育的基因，并将这些基因组织成通路，帮助我们理解组织是如何形成的以及在疾病中出了什么问题。
• 然而，单纯关注基因忽视了更大的图景。发育不仅仅是零件的简单目录，我们需要理解这些部分如何相互作用并引起它们的过程。
• 新的视角将焦点从基因本身转移到活动模式和系统中各个部分之间的关系。
• 这种视角揭示出生物系统比简单的基因层次图更加复杂和相互关联。
• 我们需要超越线性的因果关系思维，理解这些关系如何随时间变化并导致结果。这是当今发育生物学面临的新挑战。

现代生物学中列举问题的难题

• 现代生物学过于迷恋编制基因组序列和蛋白质识别等“清单”，但这些清单无法帮助我们理解生物过程。
• 我们应该设计实验来测试观察到的行为的不同解释。
• 这些实验应该预测模型如何与观察结果相匹配，帮助我们区分不同的可能原因。
• 这种方法叫做“关键实验”方法，专注于通过测试和数据不断改进模型，从而实现更深的理解。

生物物理特性作为因果因素

• 目前的基因调控网络（GRN）模型无法解释空间约束等物理因素如何影响生物系统。
• 约束就像是限制系统行为的规则，它们促使系统进入新的状态，而这种状态在没有这些约束的情况下是无法获得的。
• 例如，在微重力下培养哺乳动物细胞的研究显示，缺乏重力的环境导致细胞表现出不寻常的行为，而当细胞再次置于正常条件下时，它们形成了不同的表型（具有不同特征的细胞）。
• 这表明物理约束，如重力，帮助细胞向特定类型分化，而没有这些物理约束，细胞无法有效地分化。
• 约束通过提供一个确定性的输出，推动细胞分化成为特定的类型。

再生生物学和约束的角色

• 一些生物，如水蛭，能够再生四肢，并且它们会在完成正确的结构时停止再生，这显示了生物系统如何调控和组织生长。
• 即使面临剧烈的干预，比如异常的身体部位，生物体仍然可以实现正常的发展。例如，面部异常的蝌蚪仍然能长成正常的青蛙。
• 这表明，再生不仅仅是遵循固定的蓝图，而是一个灵活的系统，可以自我重塑。
• 生物电信号在这一过程中发挥着至关重要的作用。通过调节细胞和组织中的电气状态，研究人员可以影响再生过程中的模式和类型。

生物学中的因果关系与约束

• 经典的“台球”因果模型把生物学家引导到使用基因敲除和过表达方法来测试线性、单向的生物系统模型，这些模型实际上并不是按这种方式构建的。
• 生物系统中的因果关系被分支路径、反馈回路和多层次的相互作用打破，无法简单地用线性模型解释。
• 例如，在Chladni板实验中，我们可以在振动板上撒上沙子，沙子形成的图案取决于板的大小、形状以及振动频率的变化。
• 同样，在生物学中，重点应该是确定形状这些模式的约束，而不是寻找单一成分之间的因果关系。
• 新的因果方法关注系统整体及其行为，而不是个别的分子事件。

因果关系的比较方法

• 生物学中的因果关系应该关注一个过程的功能，而不仅仅是组件如何相互作用。
• 例如，了解小型GTP酶如何帮助细胞建立极性是重要的，但理解这种调节的目的（为什么需要极性）更为关键。
• 通过比较不同物种如何进化出多细胞性，研究人员可以揭示支配生物过程的基本机制。
• 这种方法专注于理解生物系统的功能，而不是它们的组成部分，从而为生物学模式的产生提供了洞见。

结论与展望

• 尽管我们知道冗余和自组织系统的存在，我们仍然无法完全理解复杂生物模式如何出现。
• 这种理解对于再生医学等领域至关重要，我们希望引导细胞走向特定的结果。
• 生物学中的因果关系模型目前往往过于简单，随着新技术和数据的出现，需要重新思考这些模型。
• 物理学、网络科学等领域的进展有助于我们发展更好的生物因果模型。
• 理解生物系统的复杂性将需要整合不同的方法，包括物理学、数学和计算生物学等方法。
• 最终，这将导致更有效的干预措施，用于再生医学、癌症治疗和合成生物学等领域。