Applying systems biology approaches to cancer research makes it possible to better characterize and understand a disease. This table is taken from a study conducted by the team of Guy Sauvageau who analyzed the gene expression of 772 genes of interest in 148 leukemia patients. Representing gene expression levels allows for the identification of a disease’s potential causes and the eventual indication of therapeutic targets. (Source: Blood Cancer J. 2016 Jun; 6(6): e431.)

Applying systems biology approaches to cancer research makes it possible to better characterize and understand a disease. This table is taken from a study conducted by the team of Guy Sauvageau who analyzed the gene expression of 772 genes of interest in 148 leukemia patients. Representing gene expression levels allows for the identification of a disease’s potential causes and the eventual indication of therapeutic targets. (Source: Blood Cancer J. 2016 Jun; 6(6): e431.)

The majority of master’s and doctoral students at IRIC are enrolled in the Molecular Biology program, Systems Biology option of the Faculty of Medicine. Now that you are familiar with the Ph.D. in molecular biology (if this is not the case, please read or re-read Samuel Rochette’s article), you may be asking yourself what is the meaning of the esoteric term systems biology.

One Concept, Several Definitions

What is “systems biology”? It must be noted from the outset that it is a difficult concept to define. In the words of journalist and author Christopher Wanjek, “Ask five different astrophysicists to define a black hole, the saying goes, and you’ll get five different answers. But ask five biomedical researchers to define systems biology, and you’ll get 10 different answers… or maybe more.” However, there seems to be a consensus around one concept: systems biology is an approach seeking to get the best overall picture of a biological mechanism or phenomenon to gain a better understanding of it. It is based on the well-established principle that “the whole is greater than the sum of its parts.” For the anecdote, it is interesting to note that this a philosophical concept (called “holism”, which gave birth to the better well-known “holistic” adjective) laid down at the beginning of the last century but already evident in the works of the Greek philosophers, notably Aristotle. The conceptual roots of systems biology thus stretch back to classical antiquity!

To understand this concept clearly, it is useful to compare it to an automobile. An automobile represents an assembly of parts (a motor, a steering wheel, four wheels, etc.) which, combined, become a vehicle. In this case, the whole is greater than the sum of its parts because a car enables us to travel whereas, separately, the motor or the wheels would not make travel possible. The same goes for biology: a cell is the assembly of thousands of genes and proteins constantly interacting with one another. A cell’s normal function (or dysfunction in the case of disorder) is the result of this assembly of interactions.

The novel aspect of systems biology is the possibility to study the whole rather than parts independently from one another. Traditionally, research in cell or molecular biology would focus on the study of one or several genes specifically. However, this approach, driven by the technologies available at the time, has its limitations. Going back to the automobile comparison, it is still possible to study the wheels and understand their function. But to realise that the wheels are turning because they are powered by the motor and steered through the action of the steering wheel, but also that all the components are combined to create a vehicle capable of taking us to a destination, we must have a holistic view, which is only made possible by a “system” approach. We are faced with a similar issue in biology. To understand complex problems, such as malfunctions in a cell leading to the development of cancer, it is more useful to get an overview of all components and their interactions. In the case of cancer for example, a biologist could study how networks of genes working together are disturbed in tumors.

Biology in the Big Data Era

Systems biology makes it possible to map complex biological systems into networks to understand how they work. The diagram below shows a map of interactions between various proteins carrying specific modifications known as ubiquitination and SUMOylation. The diagram was published in Nature Communications by a research group led by Pierre Thibault. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5253644/

Systems biology makes it possible to map complex biological systems into networks to understand how they work. The diagram below shows a map of interactions between various proteins carrying specific modifications known as ubiquitination and SUMOylation. The diagram was published in Nature Communications by a research group led by Pierre Thibault.

Although systems biology does not have an official birthdate, its origins can usually be traced back to the dawn of the new millennium and the great full genome sequencing projects. Indeed, in 1996, the whole yeast genome of S.

Cerevisiae (a model organism that is a tool a choice in systems biology due to its simplicity was sequenced. What was considered a technological feat at the time led to identifying and mapping the whole set of genes of this organism for the first time. A few years later, the Human Genome Project (HGP), initiated in the early 1990’s through an international consortium, was completed. Those types of projects laid the conceptual foundations and generated the data necessary for the birth of modern systems biology. However, the early days of systems thinking in biology date back to the beginning of the 20th century. As early as 1917, Scottish biologist and mathematician D’Arcy Thompson combined developmental biology, physics, and mechanics to study the forms and functions of biological organisms. By the 1960s, many cell biologists understood that components of a cell interacted with one another. Among them were the future Nobel Prize winners Jacques Lucien Jacob and François Monod who introduced the notions of regulation module and circuit—two crucial elements that were precursors of the networks studied in modern systems biology.

However, science had to wait for the technological progress of the last two decades to see the emergence of systems biology. Indeed, getting an overview of a biological phenomenon requires sophisticated techniques with which it is possible to acquire large-scale datasets. The use of such technologies generates a huge quantity of information—information that can only be properly analyzed with the help of bioinformatics tools.

The famous large-scale data, at the heart of systems biology, come in various types. Genomics, the study of the set of genes expressed by a cell or organism, is probably the most well-known field among the public because of its longevity. But the concept can come in various forms. Other examples include proteomics (the large-scale study of proteins); metabolomics (the study of all metabolites, i.e. sugars, amino acids, and other fatty acids); and also the more recent study of microbiomes: microbiomics. Those data became available thanks to the progress made in technologies enabling the miniaturization and automation of laboratory methods: this is referred to as high-throughput experiments.

However, scientists must be able to integrate and analyze this massive amount of information to draw relevant scientific findings. This is where bioinformatics, an essential part of systems biology, comes into play. Indeed, the sheer volume of data generated is so great that we now speak of “big data.” That represents such large amounts of data that it would be impossible for a human being to analyze it all without resorting to computer tools. So, let’s say, for example, that a team obtained the gene-level expression of some 22,000 genes contained in the genomes of about one hundred patients. It would be very difficult for one doctoral student, however, enthusiastic that student may be, to analyze this data set, datum by datum, and to reach any conclusion! Bioinformatics, via the use of specialized software or the development of algorithms, enable scientists to interpret the data and answer the scientific questions of the project.

By combining large-scale data acquisition and bioinformatics analysis, it is possible, for example, to gather information on DNA sequencing, protein expression, and the metabolic state of tumours of hundreds or thousands of patients to improve our understanding of disorders caused by diseases. Such strategies are essential to understanding the causes of complex conditions like cancers. In a clinical setting, those strategies also lead to the development of new therapies used in personalized medicine. Thanks to systems biology, we can now detect which gene and protein networks are affected in patients stricken by a specific type of cancer, and then use this information to develop drugs that will target those deficiencies. This approach, called targeted therapy, existed prior to the advent of systems biology. For example, it has been known for a long time that patients affected by breast cancer with HER2 gene overexpression might benefit from a drug called Herceptin. However, to be fully effective, the effects of targeted therapies must be understood as a whole. Systems biology, through the measurement and analysis of thousands of parameters in each tumour, allows scientists not only to assess the effects of a drug on all gene networks, but also to group patients and direct them towards the right therapies.

A Multidisciplinary Approach

The control mechanisms involved in cell functions are complex. This diagram shows some cell signaling pathways deregulated by certain types of cancers. With systems biology, we can get an overview of all modifications, which leads to a better understanding of the disease. (Source: KEGG pathway)

The control mechanisms involved in cell functions are complex. This diagram shows some cell signaling pathways deregulated by certain types of cancers. With systems biology, we can get an overview of all modifications, which leads to a better understanding of the disease. (Source: KEGG pathway)

Thus, systems biology requires an approach that brings together biology, the mastery of new technologies, computer science, and even in some cases mathematical modelling, engineering, and physical sciences. A biologist, however gifted, cannot possess all these skills. Systems biology is essentially a multidisciplinary approach, and research in this field is of a collaborative nature. It is in this spirit of uniting under one roof scientific teams with complementary expertise that research institutes focussed on systems biology, such as IRIC, have emerged over the last decades.

This multidisciplinarity is not without its challenges: a biologist does not speak the same language as a statistician who might have trouble following the train of thoughts of an engineer or a geneticist. In addition, biology itself is a complex issue. There are several exceptions and counterexamples to every rule: a gene can be both pro- or anti-tumoral depending on context. It is thus more difficult to model biological phenomena in comparison to physical or chemical phenomena. For this reason, the complementary expertise of individuals involved in a research project is crucial in systems biology.

At IRIC, where research is rooted in this concept, the complementarity of expertise is highlighted by 11 state-of-the-art technology platforms bringing together specialists from every field. This collaborative and multidisciplinary model creates an environment where IRIC investigators can conduct their research projects in systems biology successfully, even if they do not always agree on the definition of what systems biology is!

 

 


simon-mathien_2

Simon Mathien
Ph.D. student in molecular biology
Sylvain Meloche Laboratory

Simon’s doctoral work is focussed on a protein called ERK3 involved in tumor progression and metastases. More specifically, Simon seeks to understand how a mechanism called ubiquitination controls this protein. Characterizing this control mode could lead to the identification of future targets for the development of new anticancer therapies.

 

 

 

 

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