Download PDF Systems Biology METHODS IN MOLECULAR BIOLOGY™ by Ivan V. Maly

Sinopsis

What is systems biology? Over the past 40 years, practicing systems biologists delimited their field in a great number of ways. At times the definition was restricted to applications of the formal systems theory in biology; more recently, the tendency has been to focus on biomolecular interactions or on multivariate analysis as systems biology’s proper subject and method (1– 5) . This essay is written from a conservative biologist’s perspective and takes a less specific view of the topic. First, what should we call a system, in the context of the scientific way to parse the world into concepts? We recognize a system in a certain number of different and interacting objects. Noninteracting objects do not form a system. Also, it is hardly useful to see any substantial number of interacting, but identical objects as a system. Science has powerful methods to study aggregate behavior of identical objects. Notably, such methods tend to disregard the corpuscular nature of the individual objects and take a view of their collections as continua. In this case, biology can freely borrow methodologically from established areas of physics. In contrast, studying behavior of collections of interacting nonidentical objects remains a methodological challenge. We will, therefore, restrict the meaning of “system” to a system of interacting nonidentical parts. Study of a living object by discerning so-defined systems in it will then be called systems biology. Its status as a distinct discipline should engender no jealousy: The definition limits the subject of systems biology to what the scientific method is currently handling perhaps least confidently.

The difficulty appears to stem from the limitation of the human mind itself (6, 7) : we are nearly incapable of considering more than a few things at a time. Psychophysical experiments suggest the limit of about seven, which corresponds well to the number of the nonidentical, interacting elements that deserve to be called a system, as commonly perceived in the systems biology practice. It is important to observe that the limitation is not just to our intuition, but to rational reasoning as well. We can consider larger systems of course, but the effects resulting from interactions of more than a few elements at a time will likely be missed. To reason about systems whose complexity is beyond our immediate grasp, we must extend our mind with formal deduction, under the general rubric or mathematics. As applied to the natural world, it is termed mathematical modeling. Involving mathematics in nonsystems biological research can be necessitated by a desire of quantitative precision in understanding; in systems biology, it is indispensable for any progress whatsoever, because even the crudest qualitative effects are liable to be overlooked by the unaided mind that has evolved for rather different purposes.

That quantitative precision is rarely sought in modern systems biology is important to recognize, so as not to confuse the nature of the mathematics employed with the goals of the investigation. And certainly, those who are just considering employing systems analysis especially should not decide against it, if it is qualitative inference about their subject that they are after. We owe the quantitative, continuous-variable flavor of our most widely applied mathematics to its original development for the purposes of celestial mechanics and similarly particular problems of quantitative precision. As a consequence, the modern systemsbiological modeling has to be done most commonly in terms of dynamics of continuous quantities first, to an exceeding precision (“phosphorylation goes up by 73%”), and then the results are reinterpreted in terms of qualitative statements about discrete events (“this genotype permits cell division”), to arrive at the kind of knowledge that is actually being sought. This is no different from how experimental measurements are most commonly employed.

Content

1. Introduction: A Practical Guide to the Systems Approach in Biology
2. METHODS FOR ANALYZING BIOMOLECULAR SYSTEMS
3. Computational Modeling of Biochemical Networks Using COPASI
4. Flux Balance Analysis: Interrogating Genome-Scale Metabolic Networks
5. Modeling Molecular Regulatory Networks with JigCell and PET
6. Rule-Based Modeling of Biochemical Systems with BioNetGen
7. Ingeneue: A Software Tool to Simulate and Explore Genetic Regulatory Networks
8. SPATIAL ANALYSIS AND CONTROL OF CELLULAR PROCESSES
9. Microfluidics Technology for Systems Biology Research
10. Systems Approach to Therapeutics Design
11. Rapid Creation, Monte Carlo Simulation, and Visualization of Realistic 3D Cell Models 
12. A Cell Architecture Modeling System Based on Quantitative Ultrastructural Characteristics 
13. Location Proteomics: Systematic Determination of Protein Subcellular Location
14. METHODS FOR LARGER-SCALE SYSTEMS ANALYSIS
15. Model-Based Global Analysis of Heterogeneous Experimental Data Using gfit
16. Multicell Simulations of Development and Disease Using the CompuCell3D Simulation Environment
17. BioLogic: A Mathematical Modeling Framework for Immunologists
18. Dynamic Knowledge Representation Using Agent-Based Modeling: Ontology Instantiation and Verification of Conceptual Models
19. Systems Biology of Microbial Communities

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