Systems biology
Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research. This multifaceted research domain necessitates the collaborative efforts of chemists, biologists, mathematicians, physicists, and engineers to decipher the biology of intricate living systems by merging various quantitative molecular measurements with carefully constructed mathematical models. It represents a comprehensive method for comprehending the complex relationships within biological systems. In contrast to conventional biological studies that typically center on isolated elements, systems biology seeks to combine different biological data to create models that illustrate and elucidate the dynamic interactions within a system. This methodology is essential for understanding the complex networks of genes, proteins, and metabolites that influence cellular activities and the traits of organisms. One of the aims of systems biology is to model and discover emergent properties, of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. By exploring how function emerges from dynamic interactions, systems biology bridges the gaps that exist between molecules and physiological processes.
As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm, although it is consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "the reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term."
As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.
A comprehensive systems biology approach necessitates: a thorough characterization of an organism concerning its molecular components, the interactions among these molecules, and how these interactions contribute to cellular functions; a detailed spatio-temporal molecular characterization of a cell ; and an extensive systems analysis of the cell's 'molecular response' to both external and internal perturbations. Furthermore, the data from and should be synthesized into mathematical models to test knowledge by generating predictions, uncovering new biological mechanisms, assessing the system's behavior derived from, and ultimately formulating rational strategies for controlling and manipulating cells. To tackle these challenges, systems biology must incorporate methods and approaches from various disciplines that have not traditionally interfaced with one another. The emergence of multi-omics technologies has transformed systems biology by providing extensive datasets that cover different biological layers, including genomics, transcriptomics, proteomics, and metabolomics. These technologies enable the large-scale measurement of biomolecules, leading to a more profound comprehension of biological processes and interactions. Increasingly, methods such as network analysis, machine learning, and pathway enrichment are utilized to integrate and interpret multi-omics data, thereby improving our understanding of biological functions and disease mechanisms.
History
Holism vs. ReductionismIt is challenging to trace the origins and beginnings of systems biology. A comprehensive perspective on the human body was central to the medical practices of Greek, Roman, and East Asian traditions, where physicians and thinkers like Hippocrates believed that health and illness were linked to the equilibrium or disruption of bodily fluids known as humors. This holistic perspective persisted in the Western world throughout the 19th and 20th centuries, with prominent physiologists viewing the body as controlled by various systems, including the nervous system, the gastrointestinal system, and the cardiovascular system. In the latter half of the 20th century, however, this way of thinking was largely supplanted by reductionism: To grasp how the body functions properly, one needed to comprehend the role of each component, from tissues and cells to the complete set of intracellular molecular building blocks.
In the 17th century, the triumphs of physics and the advancement of mechanical clockwork prompted a reductionist viewpoint in biology, interpreting organisms as intricate machines made up of simpler elements.
Jan Smuts, naturalist/philosopher and twice Prime Minister of South Africa, coined the commonly used term holism. Whole systems such as cells, tissues, organisms, and populations were proposed to have unique properties. It was impossible to try and reassemble the behavior of the whole from the properties of the individual components, and new technologies were necessary to define and understand the behavior of systems.
Even though reductionism and holism are often contrasted with one another, they can be synthesized. One must understand how organisms are built, while it is just as important to understand why they are so arranged. Each provides useful insights and answers different questions. However, the study of biological systems requires knowledge about control and design paradigms, as well as principles of structural stability, resilience, and robustness that are not directly inferred from mechanistic information. More profound insight will be gained by employing computer modeling to overcome the complexity in biological systems.
Nevertheless, this perspective was consistently balanced by thinkers who underscored the significance of organization and emergent traits in living systems. This reductionist perspective has achieved remarkable success, and our understanding of biological processes has expanded with incredible speed and intensity. However, alongside these extraordinary advancements, science gradually came to understand that possessing complete information about molecular components alone would not suffice to elucidate the workings of life: the individual components rarely illustrate the function of a complex system. It is now commonly recognized that we need approaches for reconstructing integrated systems from their constituent parts and processes if we are to comprehend biological phenomena and manipulate them in a thoughtful, focused way.
Origin of systems biology as a field
In 1968, the term "systems biology" was first introduced at a conference. Those within the discipline soon recognized—and this understanding gradually became known to the wider public—that computational approaches were necessary to fully articulate the concepts and potential of systems biology. Specifically, these techniques needed to view biological phenomena as complex, multi-layered, adaptive, and dynamic systems. They had to account for transformations and intricate nonlinearities, thereby allowing for the smooth integration of smaller models into larger, well-organized assemblies of models within complex settings. It became clear that mathematics and computation were vital for these methods. An acceleration of systems understanding came with the publication of the first ground-breaking text compiling molecular, physiological, and anatomical individuality in animals, which has been described as a revolution.
Initially, the wider scientific community was reluctant to accept the integration of computational methods and control theory in the exploration of living systems, believing that "biology was too complex to apply mathematics." However, as the new millennium neared, this viewpoint underwent a significant and lasting transformation. More scientists started working on integration of mathematical concepts to understand and solve biological problems. Now, systems biology has been widely applied in several fields including agriculture and medicine'''.'''