**Introduction**

__Systems biology__ is the study of the interactions and behaviour of complex biological entities through computational and mathematical analysis. The beauty of this subject is that it is completely interdisciplinary and that this approach is required to help us understand biological interactions and dynamics within cells, tissues, organs, and organisms.

Surprisingly, systems biology as a field of study is actually quite young in the grand scheme of all sciences. Indeed, this discipline emerged after the __Genomics Revolution__ (around 2000), largely characterised by the __Human Genome Project__ which finally provided scientists with large datasets of the basic components that form these complex biological systems (e.g. genes, RNA, and proteins).

**Why the need for it?**

Although a classical biochemical understanding of genes and proteins is important, the impetus now is to understand a system’s structure and dynamics (i.e. how these different parts interact).

On that note, it is well documented that biological organisms are complex, integrated systems for which they house multiple parts (proteins, genes, enzymes, signals, etc.) at multiple levels of organisation that interact in a multitude of ways. In fact, such complexity is technically beyond the realm of what the human brain can comprehend. While one could just simply try to draw a diagram of how these different parts interact using arrows or so, this would be analogous to drawing a static image of all the cars on a road map. Essentially, what systems biology looks to determine is the __traffic patterns__, why they emerge, and how they can be controlled.

To give you another example, just knowing what parts are present is not enough and akin to knowing all the parts that make up an airplane. What you would want to know, and would be more insightful, are how these parts come together to build the plane, how they dynamically interact when in flight as a fully integrated system, and whether the system can be modified for improvement. This integrated systems approach, when applied to the study of biological cells and organisms, is therefore known as systems biology.

**How is it done?**

To begin with, the systems-level analysis of biological organisms requires detailed information about their components at different levels.

This is where __omics technologies__ come in. “Omics” is a collective term for technologies that explore the roles and relationships of a large family of cellular molecules such as genes (__genomics__), proteins (__proteomics__), mRNA (__transcriptomics__), and metabolites (__metabolomics__). They provide vast datasets that can be analysed and combined into a comprehensive list of cellular components or parts.

Now that the components have been obtained, we need to figure out which components interact with each other to produce a specific function or __phenotype__ (i.e. the organism’s observable traits) in the cell. This is achieved by connecting the components into pathways and networks.

__Signalling pathways__ are the main way a cell processes information. In essence, they receive a signal from outside the cell and control cellular physiology to produce a response. Naturally, these pathways would require several components to carry out this function. Information, in the form of cellular signals, occurs in time and space and can therefore be studied using mathematical models.

**Biological models**

Given the difficulty we face when trying to comprehend complex biological systems, the most viable option is to construct computational mathematical models.

These models allow us to visualise and predict how systems behave and can be given real-world physiochemical constraints to make them as realistic as possible. In short, by simplifying complex systems into equations, we can gain a better understanding of how they behave.

**Figure 1: **A simple example of a reaction model where A and B react to make C with unique rate constant *k*. The law of mass action can be used to derive a differential equation to represent the reaction.

Researchers typically begin by choosing a biological pathway and representing all protein interactions using a diagram (as in Figure 1). Once the interactions are captured, we need to model the speed of reactions using __chemical kinetics__ (the rate of a chemical reaction is directly proportional to the concentration of reactants). To convert this into a mathematical model, chemical kinetics (mass action kinetics) are used to form __differential equations__.

**Quick aside: differential equations**

To properly understand biological models, we need to have some grasp of differential equations. As such, a short explanation is provided.

A __differential equation__ relates a __function__* f(x)* to its __derivative__ *dy/dx*. In practice, a function usually represents a physical quantity (e.g. the concentration of Protein A) and the derivative represents its rate of change.

A simple example would be:

To which the solution via __integration__ would be:

Now try:

To solve this we need to use a technique called __Separation of Variables__ by putting *y* with *dy* and *x *with *dx*:

Now we can integrate both sides and solve:

We can use differential equations to represent growth and decay. For example, if the constant in front of the variable is positive, it indicates that the amount of that variable is increasing. Vice versa, if the constant in front of the variable is negative, it implies that the amount of that variable is decreasing.

While I could go further into the theory behind differential equations, it would be more interesting to see how they can be applied to biology.

**Modelling the **__central dogma__

We can use differential equations to model the processes of __transcription__ and __translation__ over time.

The process can be simplified into:

Let’s assume that all mRNA is used to produce proteins and its concentration will therefore degrade to 0. We can also assume that the protein concentration will also degrade to 0 as proteins are used up over time.

Hence, we can represent these processes as model equations:

where *m* represents the concentration of mRNA, *p* being the concentration of protein, and *t* equating to time.

By relating this to the central dogma, we can see that *k1* is the rate of transcription as it is a positive term in the equation, thereby representing an increase in the concentration of mRNA. Meanwhile, *d1* is the rate of mRNA degradation as it is a negative term. Applying similar logic to the second equation, we therefore see *k2* being the rate of transcription that depends on the amount of mRNA available (*m*) and *d2* representing the rate of protein degradation.

While we can technically go even further and solve these differential equations, this brief example is already sufficient to show how differential equations can be used to represent biological pathways. And in case you are curious to know, these equations can even be extended to include the effect of __gene regulation and activators__.

Moving forward, once we have these model equations in hand, biological experiments can be used to determine the value of the parameters (such as *k* and *d*). Other than that, the models can also determine the dynamic behaviour of proteins and other components in biological systems to give us new insights at a systems-level.

**Emergent properties**

Another key concept in systems biology is __emergent properties__. It was discovered that many features of biological systems arise from the collective behaviour of individual components rather than each of them acting alone.

Emergence is therefore, by definition, the result of complexity and interaction.

A great example of an emergent property is __life itself__. A single-celled bacterium is alive, but if you were to separate the individual parts that make the whole bacterium up (e.g. macromolecules like proteins), these parts are not alive. Emergent properties help us to understand that it is not enough for us to just look at individual parts in isolation, and why taking a systems approach is vital.

**Why should you care about systems biology?**

So far, we have explored the definition of systems biology, a few approaches to creating biological models, and the idea of emergent properties. What has yet to be shown is some of its real-world applications.

While there are seemingly infinite uses for systems biology, the following are just a few that I think are really interesting.

**Turing Patterns**

__Alan Turing__ was not just an excellent mathematician, he also delved into biology. Interestingly, he wrote a paper in 1952 titled __“The Chemical Basis of Morphogenesis”__, where he suggested that chemical substances called __morphogens__ interacting with each other and diffusing through the tissue can lead to the emergence of stable patterns and structures.

Turing modelled the reaction and diffusion of these morphogens using differential equations, for example:

where X and Y are morphogens.

While there is a lot more to this, Turing mainly realised that when he solved differential equations such as these, he identified six possible states that the system can adopt depending on the value of the parameters. Amazingly, a number of these patterns can be seen in nature and could potentially explain a number of interesting phenomena.

For example, one of the patterns that emerged was a standing wave of oscillating concentration (as in Figure 2). If the morphogens are chemicals that control fur pigmentation, the behaviour could potentially be an explanation for the patterning of a racoon’s tail.

**Figure 2: **Standing wave pattern of morphogen distribution could explain the pattern seen on a racoon’s tail. This figure was adapted from __Turing (1952)__.

Other patterns that could relate to Turing patterns can be seen in fish and even fingerprint patterns on humans. It has even been used to explain __hair follicle spacing__, __left-right asymmetry__, and __lung branching__.

**Systems pharmacology and systems biomedicine**

Another reason why you should care about systems biology is that systems-level insights can serve as building blocks to more personalised and precise medicine.

In fact, systems biology is being used in the processes of __drug discovery__ and in medicine. By modelling the behaviour of a drug on human metabolism using a systems biology approach, we can effectively predict potential therapeutic responses and any adverse effects. We can also collect ‘component’ data about key enzymes that metabolise drugs and predict how they could interact with the drug. In addition to that, we could constrain models to be specific to one person, thereby allowing us to predict and adjust drugs that meet the needs of individual patients.

As you can see, systems biology goes beyond the realm of simple biology and uses an interdisciplinary approach to understand the amazing complexity of biological organisms at a holistic level. This unique approach enables us to better understand the world around us and advance our medical capabilities.

As this is just an introduction, there are so many more things that could have been mentioned about systems biology. If you are interested, take a look at these links:

**Author**

Akram Yoosoofsah

BSc Biochemistry with Management

Imperial College London

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