Download PDF Introduction to Systems Biology by Sangdun Choi

Sinopsis

Systems biology aims at a system-level understanding of biological systems (1,2). The investigation of biological systems at the system level is not a new concept. It can be traced back to homeostasis by Canon (3), cybernetics by Norbert Weiner (4), and general systems theory by von Bertalanffy (5). Also, several approaches in physiology have taken a systemic view of the biological subjects. The reason why “systems biology” is gaining renewed interest today is, in my view, due to emerging opportunities to solidly connect system-level understanding to molecular-level understanding, as well as the possibility of establishing well-founded theory at the system level. This is only possible today because of the progress of molecular biology, genomics, computer science, modern control theory, nonlinear dynamics theory, and other relevant fields, which had not sufficiently matured at the time of early attempts.

However, “system-level understanding” is a rather vague notion and is often hard to define. This is because a system is not a tangible object. Genes and proteins are more tangible because they are identifiable matter. Although systems are composed of this matter, the system itself cannot be made tangible. Often, a diagram of the gene regulatory networks and protein interaction networks are shown as a representation of systems. It is certainly true that such diagrams capture one aspect of the structure of the system, but they are still only a static slice of the system. The heart of the system lies in the dynamics it creates and the logic behind it. It is science on the dynamic state of affairs.

There are four distinct phases that lead us to system-level understanding at various levels. First, system structure identification enables us to understand the structure of the systems. Although this may be a static view of the system, it is an essential first step. Structure is ultimately identified in both physical and interaction structures. Interaction structures are represented as gene regulatory networks and biochemical networks that identify how components interact within and between cells.

Physical details of a specific region of the cell, overall structure of cells, and organisms are also important because such physical structures impose constraints on possible interactions, and the outcome of interactions impacts the formation of physical structures. The nature of an interaction could be different if the proteins involved move by simple diffusion or under specific guidance from the cytoskeleton.

Second, system dynamics need to be understood. Understanding the dynamics of the system is an essential aspect of study in systems biology. This requires integrative efforts of experiments, measurement of technology development, computational model development, and theoretical analysis. Several methods, such as bifurcation analysis, have been used, but further investigations are necessary to handle the dynamics of systems with very high dimensional space.

Third, methods to control the system have to be investigated. One of the implications is to find a therapeutic approach based on system-level understanding. Many drugs have been developed through extensive effect-oriented screening. It is only recently that a specific molecular target has been identified, and leading compounds are designed accordingly. Success in control methods of cellular dynamics may enable us to exploit intrinsic dynamics of the cell, so that its effects can be precisely predicted and controlled.

Finally, designing the system—i.e., modifying and constructing biological systems with designed features. Bacteria and yeast may be redesigned to yield the desired properties for drug production and alcohol production. Artificially created gene regulatory logic could be introduced and linked to innate genetic circuits to attain the desired functions (6).

Content

1. Introduction
2. Scientific Challenges in Systems Biology
3. Bringing Genomes to Life: The Use of Genome-Scale In Silico Models
4. From Gene Expression to Metabolic Fluxes
5. Experimental Techniques for Systems Biology
6. Handling and Interpreting Gene Groups
7. The Dynamic Transcriptome of Mice
8. Dissecting Transcriptional Control Networks
9. Reconstruction and Structural Analysis of Metabolic and Regulatory Networks
10. Cross-Species Comparison Using Expression Data
11. Methods for Protein–Protein Interaction Analysis
12. Genome-Scale Assessment of Phenotypic Changes During Adaptive Evolution
13. Location Proteomics
14. Theoretical and Modeling Techniques 
15. Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State-Space Models
16. Modeling Spatiotemporal Dynamics of Multicellular Signaling
17. Kinetics of Dimension-Restricted Conditions
18. Mechanisms Generating Ultrasensitivity, Bistability, and Oscillations in Signal Transduction
19. Employing Systems Biology to Quantify Receptor Tyrosine Kinase Signaling in Time and Space
20. Dynamic Instabilities Within Living Neutrophils
21. Efficiency, Robustness and Stochasticity of Gene Regulatory Networks in Systems Biology: l Switch as a Working Example
22. Applications, Representation, and Management of Signaling  Pathway Information: Introduction to the SigPath Project
23. Methods and Software Platforms for Systems Biology
24. SBML Models and MathSBML
25. CellDesigner: A Graphical Biological Network Editor and Workbench Interfacing Simulator
26. DBRF-MEGN Method: An Algorithm for Inferring Gene Regulatory Networks from Large-Scale Gene Expression Profiles
27. Systematic Determination of Biological Network Topology: Nonintegral Connectivity Method (NICM)
28. Storing, Searching, and Disseminating Experimental Proteomics Data
29. Representing and Analyzing Biochemical Networks Using BioMaze

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