We often read news stories telling us that certain foods or substances can cause cancer. But is it really that straightforward? How can eating a certain food increase my risk of developing cancer?
Systems biology can help answer questions like these. By combining data about different aspects of a biological system, researchers can produce computational and mathematical models to explore more deeply how the system works. For example, data on how certain environmental factors switch our cells on and off – called epigenetics – combined with information on the molecules our cells produce could provide a clearer picture than either of those datasets alone.
A new Elsevier journal, Current Opinion in Systems Biology, aims to publish articles that pool these models, giving us a comprehensive overview of what we know about how biological systems work.
Investigating the complexity of health and disease
Cancer isn’t caused – or prevented – by a single food, but a number of factors combined, right down to the level of the machinery working in your cells. To develop, cancers need genetic mutations, activation of pathways and interactions with the microenvironment, including the immune system; all these factors are important and interrelated. To improve therapies, we need a better understanding of how these factors work together.
Dr. Raul Rabadan, Associate Professor in the Department of Systems Biology at Columbia University College of Physicians and Surgeons in New York, is one of the Editors-in-Chief of Current Opinion in Systems Biology. To understand a complex system, we need insights into the various processes underpinning it, he explained:
One of the characteristics of biological systems is the interdependence of different processes. Even simple organisms are part of a complex mesh of biological processes. For instance, it is impossible to completely understand how a virus causes a disease without understanding how it interacts with its host: how it propagates, how it enters into a cell, how it highjacks the cell machinery, how it replicates and how it is recognized by the innate and adaptive immune systems.
With our increasing knowledge of biological processes, we have moved away from the concept that a single factor, or a handful of genes, can cause or prevent a disease or biological phenomenon. Researchers now have a multitude of methods for studying biological systems: genomics, epigenomics, transcriptomics, proteomics and metabolomics can all reveal what’s happening at the cellular level. But taken alone, each of these methods produces a different dataset, for example, on the RNA being transcribed, proteins being built or other molecules produced in the cell.
Systems biology is a way of pulling all this data together into one coherent picture at the cellular level. Dr. Edda Klipp, Professor of Theoretical Biophysics at Humboldt-Universität zu Berlin, is the other Editor-in-Chief of Current Opinion in Systems Biology. She explained:
Systems biology aims to describe and understand the dynamics and conditions of all aspects of life. It started with the mathematical analysis of small systems, acknowledging that even for small systems the outcome of perturbations can often not be predicted from intuition. Since then, we have witnessed a revolution in experimental techniques to study cell and molecular biology on all levels and to create massive amounts of data just summarized as “omics.”
Behind the recent surge in systems biology is technological development. Advances in high throughput technologies in biology can capture and model the complexity of biological phenomena, and the resulting information can be combined with mathematical models to study biological systems.
The rise of systems biology
Using computer models to study biological systems isn’t new. In the early 1950s, British Nobel Prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley built a mathematical model that explained how electrical signals – called action potential – move along neurons. A decade later, Denis Noble produced the first computer model of the pacemaker.
The discipline saw a dip in the 1980s, when molecular biology techniques improved and replaced – or masked – the need for computational methods. But huge volumes of data started coming out of the new functional genomics studies in the 1990s and, coupled with more computing power, this led to a resurgence of interest in systems biology.
Systems biology gained traction again, and dedicated research institutes began to appear, along with an increase in “omics” research, such as genomics and proteomics. Today the research output of systems biology is four times what it was a decade ago, at about 30,000 publications a year, according to Scopus.
It’s a vibrant discipline, with about a dozen journals covering various aspects of the research. Launched a year ago, Cell Systems – published by Elsevier’s Cell Press – is one such journal; it aims to publish articles on all systems, “from molecules, pathways, cells, and tissues to whole organisms, populations and ecosystems,” on disciplines as diverse as cancer, plant biology and synthetic biology.
We’re aiming for a different viewpoint with Current Opinion in Systems Biology. With so much data from so many different disciplines and methods all coming together, systematic review can be a powerful way to see clearly what the current knowledge on a particular topic is.
“We will provide a landscape view on how systems biology approaches are shaping our understanding of complex biological phenomena: from cancer, to drug discovery, to infectious diseases,” said Dr. Rabadan.
Take red wine, for example. Hypothetically, looking at all the systems biology studies that may have been done to answer the question “is red wine healthy?” – such as epigenetics studies to look at its impact on gene expression, or metabolomics studies to find out what molecules are produced following consumption – can help provide a more reliable answer. This, said Dr. Klipp, will ultimately help us understand life:
Do these data allow us to understand the cellular mechanisms underlying the complex process we call life – that is, proliferation, development and response to stresses? Systems biology faces a challenge: to integrate the wealth of quantitative data being produced with the classical mechanistic view that is still prevalent in biology. This will enable the creation of conceptual and computational models that cover major biological processes and allow us to make sensible, testable predictions, finally leading to a better understanding of all facets of life.
The first volume of Current Opinion in Systems Biology is scheduled to be published in early 2017. It will feature a section dedicated to the Future of Systems Biology which, will be guest edited by Prof. Arnold Levine of Princeton University. Other topics covered in the first year will include Genomics and Epigenomics, Pharmacology and Drug Discovery and Infectious Diseases and Host Pathogen Interaction.
Dr. Edda Klipp is Professor of Theoretical Biophysics at Humboldt-Universität zu Berlin. She was previously Head of the research group “Computational Systems Biology” and before that Group Leader of the Junior Research Group Kinetic Modeling at the Max Planck Department for Molecular Genetics. Her group focuses on the mathematical modeling of biological organisms at the cellular and sub-cellular level with a special interest in yeast, Bacillus subtilis and mammalian cells.
Dr. Raul Rabadan is an Associate Professor in the Department of Systems Biology and Biomedical Informatics at Columbia University. He is also director of the Center for Topology of Cancer Evolution and Heterogeneity. From 2001 to 2003, Dr. Rabadan was a fellow at the Theoretical Physics Division at CERN, the European Organization for Nuclear Research, in Geneva, Switzerland. In 2003 he joined the Physics Group of the School of Natural Sciences at the Institute for Advanced Study. Previously, Dr. Rabadan was the Martin A. and Helen Chooljian Member at The Simons Center for Systems Biology at the Institute for Advanced Study in Princeton, New Jersey. His current interest focuses on uncovering patterns of evolution in biological systems – in particular, RNA viruses and cancer.
Current Opinion in Systems Biology is a new systematic review journal that aims to provide specialists with a unique and educational platform to keep up-to-date with the expanding volume of information published in the field of systems biology. It publishes polished, concise and timely systematic reviews and opinion articles. In addition to describing recent trends, authors are encouraged to give their subjective opinion on the topics discussed. As this is such a broad discipline, we have determined 12 themed sections, each of which is reviewed once a year. This journal is published by Elsevier.
Elsevier's Current Opinion collection
There are 18 leading titles covering life sciences and adjacent fields in Elsevier’s Current Opinion journal collection. These review journals have been developed to help specialists keep up-to-date with the increasing volume of research published in their subject.
- Current Opinion in Behavioral Sciences
- Current Opinion in Biotechnology (Impact Factor 8.314)
- Current Opinion in Cell Biology (Impact Factor 8.851)
- Current Opinion in Chemical Biology(Impact Factor 7.643)
- Current Opinion in Chemical Engineering (Impact Factor 3.571)
- Current Opinion in Chemical Engineering (Impact Factor 3.571)
- Current Opinion in Colloid & Interface Science (Impact Factor 6.234)
- Current Opinion in Environmental Sustainability (Impact Factor 4.658)
- Current Opinion in Food Science
- Current Opinion in Genetics & Development (Impact Factor 5.784)
- Current Opinion in Green and Sustainable Chemistry
- Current Opinion in Immunology (Impact Factor 7.126)
- Current Opinion in Insect Science (Impact Factor 2.719)
- Current Opinion in Microbiology (Impact Factor 6.234)
- Current Opinion in Neurobiology (Impact Factor 6.373)
- Current Opinion in Pharmacology (Impact Factor 4.769)
- Current Opinion in Plant Biology (Impact Factor 6.780)
- Current Opinion in Psychology
- Current Opinion in Solid State & Materials Science (Impact Factor 5.111)
- Current Opinion in Structural Biology (Impact Factor 6.713)
- Current Opinion in Systems Biology
- Current Opinion in Toxicology
- Current Opinion in Virology (Impact Factor 5.313)