I. Introduction to Systems

Biology is full of complex phenomena – cells regulating themselves, organs coordinating, ecosystems evolving. Systems biology gives us tools to understand these phenomena not as isolated parts, but as interacting networks. This chapter introduces you to what a biological system is and why systems thinking matters.

1. From Parts to Wholes

Biological research has often used a reductionist approach to understanding how life works: studying one gene, one protein, one cell, one organism at a time. This method created “parts lists” of life, and over decades of research, this strategy clarified the function of  thousands of different genes, proteins, and cells. From there, it became possible to connect these functions of genes and proteins into pathways, to discover regulatory actions and signalling pathways, and ultimately start piecing together networks of interactions. More and more was learned about the organization driving cellular activities and how collaborating cells create the key functions of tissues and organs. The physiology of multicellular organisms is dependent on collaboration between organs, tissues, and cells. This is true beyond individual organisms as well – whole populations of living creatures interact to create complex ecosystems.

These insights point to life being more than a collection of parts, highlighting a limitation of the reductionist approach. Behavior and complexity emerge from how parts interact – the way a flock moves in patterns, or how an ecosystem regulates itself. Systems biology shifts the focus from single parts to the relationships among them. Systems biology is a relatively new approach to biology, with a unique perspective that we’ll be exploring in this course.

2. What Makes a System a System?

A system is a set of components interacting within defined boundaries. To analyze any biological system, we must identify at least five key features:

3. The Hierarchical Nature of Biological Systems

Biological systems do not exist in isolation. They are organized into levels of increasing complexity, each built from the levels below it. Understanding these levels – and how they interact – is a cornerstone of systems biology.

At the smallest scale, we find molecules such as DNA, RNA, proteins, lipids, and metabolites. These molecules interact to form larger structures and processes inside cells. Cells themselves do not operate as isolated bags of chemicals; they organize molecules into networks and compartments, such as metabolic pathways, signaling cascades, and structural scaffolds.

In multicellular organisms, cells then work together to form tissues, specialized for particular functions (e.g., epithelial tissue for lining surfaces, muscle tissue for contraction). Depending on the size and complexity of the organism, tissues combine into organs, such as the heart, lungs, and liver, each carrying out an integrated physiological function. Organ systems – like the circulatory, nervous, or immune systems – are groups of organs that coordinate to maintain homeostasis and respond to external changes. Finally, at the largest scale, individual organisms interact with other organisms and with the environment to form populations, communities, and ecosystems.

Systems biology encourages you to think about how processes at one level influence behaviors at another. You can view a system at one level as a component of a system at the next level up. The table below shows how nested systems affect glucose homeostasis, a topic that we will explore more in this course.

Thinking about systems as levels of organization is good  practice, because when you define a biological system for study, you must be explicit about which level of organization you’re analyzing. For example, modeling a single metabolic pathway inside a cell requires different data and tools than modeling the entire immune system or an ecosystem. Recognizing the appropriate level – and its boundaries – is essential for building meaningful models and experiments.

4. Dynamics and Regulation in Biological Systems

Biological systems are dynamic, not static. Even when an organism appears “at rest,” molecules are constantly moving, signals are fluctuating, and energy is being transformed. This constant motion is what makes life possible. A static system cannot adapt or respond to changes in its environment, but a dynamic one can.

Systems biology emphasizes time as a key dimension. Knowing which molecules are present is only the first step – you also need to know how their concentrations change over time. Two systems with identical parts can behave very differently depending on timing, feedback, and regulation.

Examples:

• The concentrations of glucose and insulin in your bloodstream rise and fall throughout the day.
• The expression of circadian clock genes oscillates roughly every 24 hours.
• Bacterial populations switch between motile and stationary states depending on nutrient availability.

These examples show that systems behavior is inherently temporal: inputs trigger changes, and outputs emerge over seconds, minutes, or days.

Control Mechanisms

One of the most important concepts in systems biology is the feedback loop, which is a form of regulation where the output of a process influences its own activity. Feedback loops can be negative (stabilizing) or positive (amplifying). Negative feedback loops tend to produce robustness – the system returns to a set point after a disturbance. Positive feedback loops can produce rapid transitions or “all-or-none” behaviors, such as cell fate decisions during development.

Cross-talk between pathways (one signaling network influencing another) is also common in cells and organisms. This creates networks of regulation, allowing complex decisions, such as immune responses or developmental patterning.

5. Emergent Properties: More Than the Sum of Parts

Emergent properties are system-level behaviors that arise from interactions among components. An emergent property is a characteristic of a system that arises from the interactions among its individual parts. Rather than being a simple sum of the components’ behaviors, it depends on how those components work together and influence one another. Crucially, emergent properties cannot be fully predicted by studying the parts of a system in isolation. Instead, they appear only when the system is considered as a whole, manifesting at a higher level of organization than the individual components themselves.

For example, the wetness of water is an emergent property: a single water molecule is not “wet,” but when millions of molecules interact, the collective behavior produces the sensation of wetness. In biology, the rhythmic beating of the heart emerges from the coordinated contraction of individual cardiac cells. Likewise, consciousness is often described as an emergent property of networks of neurons in the brain, and the structure and function of an ecosystem emerge from interactions among its many organisms and their environment. These examples illustrate how properties at one level of organization often cannot be inferred simply by examining the pieces at a lower level.

Modeling Emergent Properties

Because emergent properties cannot be fully understood by simply looking at a list of parts, systems biologists rely on tools that show how interactions among components give rise to complex behaviors. Instead of treating a system as a static diagram, they build models that simulate how all the pieces work together over time.

A network diagram is like a map of a city: it shows which “roads” (interactions) connect which “buildings” (components such as genes, proteins, or cells). By studying the structure of this map – which nodes are highly connected, which pathways form loops, and so on – scientists can predict which components are most important for the system’s behavior.

Agent-based models go a step further. Here, individual “agents” (cells, molecules, or organisms) are each given simple rules about how to act, and then a simulation is performed. When thousands of agents follow their rules at once, surprising large-scale patterns can emerge, just as a flock of birds forms a shape in the sky even though each bird is only following its neighbors.

Computational simulations let researchers test how small, local interactions produce large-scale effects. For example, a model might specify how a single cell moves toward a chemical signal; by running the simulation, researchers can see whether a whole tissue forms a pattern or migrates in a coordinated way. This approach allows scientists to explore “what if” scenarios that would be difficult or impossible to test directly in the lab.

Modeling is powerful, but it needs real-world data. High-throughput experiments – such as genome-wide expression analyses, proteomics, or large-scale imaging – provide vast amounts of information about how systems behave as a whole. These data sets help scientists detect patterns, test model predictions, and discover unexpected emergent properties.

Taken together, network diagrams, agent-based models, computational simulations, and high-throughput experiments give systems biologists a way to move beyond studying isolated parts. They reveal how local rules and interactions scale up to the global behaviors that define life.

6. Systems Biology as a Method

Systems biology is not just a topic but a way of doing science. Traditional biology often focuses on one variable at a time. Systems biology embraces complexity and connectivity, allowing researchers to see how perturbations ripple through the system and to identify leverage points for intervention (in medicine, agriculture, environmental science, and more). It combines measurement, modeling, and iteration to understand how systems work as a whole. A systems biologist might approach a problem like this:

1. Define the system – boundaries, components, interactions.
2. Measure – collect high-quality, often large-scale data.
3. Model – build mathematical or computational representations.
4. Predict – use the model to forecast behavior under new conditions.
5. Test and Refine – perform experiments to check predictions, then improve the model.

This cycle repeats, refining your understanding at each iteration.

Key Takeaways from This Chapter

• A biological system consists of components, interactions, boundaries, inputs, and outputs.
• Systems exist in a hierarchy, from molecules to ecosystems, with new properties at each level.
• Biological systems are dynamic – time, feedback, and regulation shape their behavior.
• Emergent properties arise from interactions and cannot be deduced by examining parts in isolation.
• Systems biology integrates data, models, and experiments to understand and predict system-level behavior.

Licenses and Attributions

“Introduction to Systems” by Craig Stephens was outlined and partially written by ChatGPT5, with prompting, fact-checking, editing, and supplementation by the authors. “Introduction to Systems” is licensed under CC BY-NC 4.0.