7. The lawless pursuit of biological systems

The laws of physics are a prerequisite for us to make reliable predictions regarding our surroundings. By extension, making reliable predictions in biology requires laws of biology. The problem is that such laws are almost non-existent, because biological systems are hugely complex and diverse. As a consequence, it is difficult to make true statements covering all organisms on Earth—or even large classes of organisms. This difficulty translates directly into the challenge of identifying rules that govern biological systems. What would such biological rules or laws even look like? ‘The lawless pursuit of biological systems’ considers the future of systems biology and discusses how it might evolve as it matures as a field of investigation.

6. Simulators

Computational models can serve many purposes, but a particularly powerful application of a model is its use as a system simulator. An emerging branch of computational systems biology strives to develop simulators for complex systems in biology and medicine, the premier example being a disease simulator. ‘Simulators’ discusses the nascent efforts towards the development of simulators for practical applications. Disease simulators will deepen our understanding of the physiology of human diseases and their treatment. Simulators in metabolic engineering have the goal of improving the microbial production of bulk materials and of valuable organic compounds. Relatively simple simulators for the production of biofuels and for crop development, such as the Soybean Growth Simulation Model (SoySim), are already in use.

2. Exciting new puzzles

Systems biologists want to understand how biological systems operate within their natural surroundings. These ‘systems’ may be whole cells, organisms, or even populations, but they are more often comprised of biological molecules and their interactions. Even seemingly simple systems in biology are complex, and often enormous amounts of information are involved. ‘Exciting new puzzles’ explains how computational systems biologists, or systems modellers, depend on mathematics and computing to extract information from available data and then piece this information together, thereby generating genuinely new insights and narrowing gaps in knowledge. Systems biology is in its infancy and the complexity of nature offers ambitious researchers truly tantalizing puzzles with potentially huge rewards and worldwide implications.

4. Computational systems biology

The new methods of —omics biology, combined with more traditional experiments, have the capacity of generating more high-quality data than ever before. So, why isn’t that sufficient? What is missing? The missing aspects arise from subtle, but important differences between data, information, knowledge, and understanding. ‘Computational systems biology’ explains how laboratory experiments generate data, whereas understanding additionally requires significant human intelligence and knowledge. Computational systems biology (CSB) attempts to bridge the gap between data and understanding. It uses a pipeline from data to understanding that consists of two toolsets: machine learning and mathematical models. The most useful of these models in CSB fall into two categories: static networks and dynamic biological systems.

5. Interdependencies of biological systems

Even the simplest of organisms rely on uncounted molecular processes that allow them to thrive, propagate, and respond to threats from predators, parasites, and the environment. They possess thousands of genes defining each organism’s repertoire of functions and features; proteins that provide structure, control biochemical reactions, and serve as means of transporting molecules; metabolites for energy and a myriad of other purposes; as well as numerous signalling mechanisms that regulate all aspects of life. ‘Interdependencies of biological systems’ explains how despite the wide variety of differences, there are also very strong similarities in the manner in which cells and organisms are organized. At the molecular level, much of this organization is captured in the central dogma of molecular biology.

3. The —omics revolution

In the past, experiments were time consuming and expensive, and data were therefore often scarce. The so-called —omics revolution has changed this situation to a point where we often have so many data that we cannot make sense of them and need to resort to sophisticated computing methods. ‘The —omics revolution’ explains how this shift has changed how we perform experiments and think about science. The —omics revolution—with fields of study such as genomics and proteomics—has not only generated huge datasets, it has turned the tried-and-true scientific method on its head. The central position of a strong hypothesis has all but vanished, and the new mindset is exploratory data analysis.

1. What is systems biology all about?

Systems biology is a new specialty area that has exactly the same goals and purposes as general biology, namely, to understand how life works. But in contrast to traditional biology, systems biology pursues these goals with a whole new arsenal of tools that come from mathematics, statistics, computing, and engineering, in addition to biology, biochemistry, and biophysics. ‘What is systems biology all about?’ explains how these tools are utilized to determine the specific roles of the many different components—from atoms and molecules to nuclei, cells, and organs—that we find in living organisms, how these components interact with each other, and how they all collaborate to create and support life.