3S Biology Summer School Systems, Synthetic, and, Semantic Biology
Systems biology is a discipline that aims at explaining the dynamic behaviour of complex biological systems like molecules, cells, organisms or entire species. Systems biology rejects reductionist approaches and applies experimental, theoretical, and modeling techniques to the study of biological processes in a holistic perspective. This discipline makes use of mathematical and computational models to represent and simulate the time evolution of biological systems. One of the main goals of systems biology is to discover yet-unknown properties at the intra-, inter-cellular, tissue, organism and population level understanding the multiplicity of elementary interactions that characterize the dynamic of biological processes.
Diverse theoretical approaches from different branches of mathematics, physics and computer science are being used to achieve these goals, e.g. probability theory, statistics, linear algebra, calculus, graph theory, topology, abstract algebras, and coding theory. The master representation of a biological system is often a graph (i.e. the network depicting the components of a system as nodes and their interactions as arcs between nodes) as consistent with the conceptualization of biological processes as networks (e.g. gene network, metabolic networks, protein-protein network, signalling network).
Theoretical holistic approaches are currently used also in synthetic biology in the context of the study and the design of living systems at the micro- and meso-scale.
The construction of living systems that could not have arisen without direct human intervention has come to be known as synthetic biology. This exciting field combines engineering and molecular biology to build organisms to carry out desired tasks and to deepen our understanding of the basis of life. To achieve such goals, a variety of approaches have been developed. For example, much effort is expended in standardizing biological parts so that complex genetic circuitry can be built with less trial and error. In a similar vein, living organisms are reduced in complexity to generate simpler chassis upon which new genetically encoded functions can be inserted with less fear of complications arising from crosstalk. From here, many interesting behaviors have been engineered, including coordinated cellular activity across populations, cell based computation, logic gates, seek and destroy cellular activity, among others. Synthetic biology is a quite creative discipline where building something new, as opposed to describing things that already exist, is the norm. Perhaps for this reason, synthetic biology groups typically consist of people with a wide range of backgrounds, including artists, engineers, sociologists, chemists, philosophers, and biologists.
Semantic biology is a discipline that seeks to structure the information provided by the scientific literature together with the data generated by experimental omics approaches. Semantic biology develops techniques to formalize this knowledge across multiple domains of biology and medicine to facilitate the formulation and validation of hypotheses and new knowledge. An effective use of multiple sources of data (e.g. protein structures and sequences, expression patterns, genetic, metabolic and signaling networks) is the core of the integrative approaches both to wet biological research and to computational biology. Biological data experimental sources developed by different academic and industrial groups differ with respect to the assumptions concerning the properties of the biological system components, the relationships among them, and the level of abstraction at which these components and their properties are described. As a consequence, semantic differences among data sources are unavoidable and need to be reconciled to solve the problem of data integration.
Ontologies, semantic objects, semantic data models, graphs, typed relationships, contexts, semantic web technologies are used by semantic biology to integrate multiple data sources and to represent complex systems.
The use of semantic concepts and technologies is increasingly widespread both in systems and in synthetic biology. The convergence of semantic biology and systems biology originates semantic systems biology that is a systems-level approach that uses semantic description of biological information to pursue integrated data analysis. Semantic methods are being used also to address challenges in synthetic biology concerning semantic mining and management of information.