Introduction to Systems Biology

As we enter the post-genomics era, systems biology is emerging as a powerful formalism for identification of system structure and simulation of complex biological behaviour. A systems biology approach attempts to integrate experimental, computational, and theoretical biology to understand biological systems. The aim is to develop a system-level analysis that provides a deep understanding of system structure and dynamics. The application of systems biology depends on cross-disciplinary teams of researchers working together to develop high-throughput technologies for data acquisition and storage and sophisticated computational methods for analysis and simulation (Figure 13.1). An immediate aim of systems biology is to build a common language that links experimental biological scientists with engineers, and computer scientists, and mathematicians. The long term goal is to integrate these technologies

Life Sciences

Life Sciences

Computer Science

Fig. 13.1 Cross-disciplinary interaction between experiments, technology, computation, and theory in systems biology.

Computer Science

Mathematics, System Sciences

Fig. 13.1 Cross-disciplinary interaction between experiments, technology, computation, and theory in systems biology.

and biological knowledge to create a new paradigm for biological research, driven by a robust interaction between experiments, technology, computation, and theory [1,2].

Systems biology is a young, emerging field that aims at system-level understanding of biological systems. Since the days of Wiener [3], system-level understanding has been a long-standing goal of biological sciences. Cybernetics, for example, aims at describing animals and machines from control and communication theory. Unfortunately, molecular biology had just started at that time, so only phenomenological analysis was possible. It only recently became possible for system-level analysis to be grounded on discoveries at the molecular level. With the progress of genome sequencing projects and of other molecular biology projects that accumulate in-depth knowledge of the molecular nature of biological systems, we are now at the stage to seriously look into the possibility of system-level understanding solidly grounded on molecular-level understanding (Figure 13.2).

What does it mean to understand at the system level? Unlike molecular biology, which focuses on molecules, including the sequences of nucleotide acids and proteins, systems biology focuses on systems that are composed of molecular components. Although systems are composed of materials, the essence of a system lies in its dynamics, so it cannot be described merely by enumerating its components. At the same time, it is misleading to believe that only system structure, such as network topologies, is important without paying sufficient attention to the diversity and functionalities of components. Both the structure of the system and its components play an indispensable role in forming the state of the system as a whole. Within this context, understanding the structure of a system, such as gene regulatory or biochemical networks, as well as the physical structures; understanding the dynamics of a system, by both quantitative and qualitative analysis as well as by constructing a theory/

Fig. 13.2 System level understanding of a biological system.

model with powerful prediction capability; understanding the control mechanisms in the system; and understanding the design of the system - are all key milestones for judging how well we understand the system (Figure 13.3).

New high-throughput technologies in genomics, transcriptomics, proteomics, and metabonomics allow for deciphering the intracellular signalling machinery in great detail (Figure 13.2). In contrast to conventional approaches in toxicology that use single gene and protein expression patterns as indicators for the toxicity of well established model toxins, the genome, transcriptome, proteome, and metabolome can now be tested in parallel to explore the disruption of regulatory processes on a system-wide level. This experimental approach generates large, high-dimensional datasets for the identification of regulatory processes by data-processing techniques. A meaningful understanding of the regulatory processes and networks being identified requires subsequent mathematical modelling and simulation. Making in silico predictions requires that current knowledge from numerous public and proprietary databases be integrated. These computationally very demanding methods of data analysis and simulation require intensive use of high-performance computing. The in silico predictions need to be validated in in vivo as well as in vitro experiments to close the iterative research cycle of systems biology of toxicity testing in many cell types. The long-range perspective of the systems biology approach will lead to more rational drug design through the identification of organ-specific toxic side effects with in silico simulations. This will lead to more rational and cost-effective R&D of new drugs by helping to significantly reduce the attrition rate of new drugs in the late phase-III and post-market periods.

Many exciting and profound issues are being actively investigated, including the robustness of biological systems, network structures, and dynamics and applications to drug discovery. Systems biology is in its infancy, but this is the area that has to be explored and the area that we believe will be the mainstream of biological sciences in this century.

Fig. 13.3 Biochemical model for intracellular Ca2+ dynamics.

294 | 13 Systems Biology Applied to Toxicogenomics 13.1.1

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