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Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?

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Abstract

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology ‘revolutionizes’ molecular biology and ‘transcends’ its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology’s use of modelling and bioinformatics, and by its scale enlargement.

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Notes

  1. The term systems biology was already used by systems theorists in the 1960s, referring to the application of systems theory to biology. The term started to reappear from 1999 onwards (Agrawal 1999; Ideker et al. 2001; Kitano 2002a; Wolkenhauer 2001. See also Powell (2004) and Bork (2005) for figures on the usage of the term.

  2. O’Malley and Dupré (2005) use the term pragmatic systems biology.

  3. Already in 1977 the single-stranded phage X174 was fully sequenced (Sanger et al. 1977), followed in 1982 by the double-stranded lambda phage (Sanger et al. 1982) and in 1995 by Haemophilus influenzae (Fleischmann et al. 1995). Escherichia coli and Sacharomyces cerevisiae were fully sequenced in 1997 (Blattner et al. 1997; Goffeau et al. 1996), Caenorhabditis elegans in (1998), Arabidopsis thaliana and Drosophila melanogaster in 2000 (Adams et al. 2000; Arabidopsis Genome Initiative 2000) and Mus musculus in 2002 (Waterston et al. 2002). The human genome sequence was completed in 2001 (Venter et al. 2001; McPherson et al. 2001). See www.ncbi.nlm.nih.gov/sites/entrez?db=genomeprj for a complete list of genome sequence projects available today.

  4. Some of the best known and widely used databases are NCBI for genomic information (Woodsmall and Benson 1993). Transcriptomic databases are usually clustered around organism-, pathway- or disease-related properties. Proteomics databases include Uniprot (Universal Protein Resource; Apweiler et al. 2004).

  5. For information about protein–protein interaction databases, see Hermjakob et al. (2004).

  6. An ORF or ‘open reading frame’ is a DNA sequence that encodes a protein. In prokaryotes this is a continuous stretch of DNA. In eukaryotes, the mRNA is first edited through a process of ‘splicing’ whereby introns are removed and exons remain to be translated into the protein. See Brasch et al. (2004) and Rual et al. (2004).

  7. Several consortia are developing international standards (Brazma et al. 2001; Brazma et al. 2006; Taylor et al. 2007). The proteomics standards initiative (PSI) of 2002 has already developed standards for two key areas of proteomics: mass spectrometry and protein–protein interaction data (Taylor et al. 2007). There are several other initiatives improving the reporting, use and evaluation of high-throughput data: gene ontology (GO) for the description of gene function; Minimum Information About a Microarray Experiment (MIAME) for microarray experiments; Systems Biology Markup Language (SBML) and Cell Markup Language (CellML) for bio-molecular simulations; and MIMIx, which presents the minimum information required for reporting a molecular interaction experiment (Ashburner et al. 2000; Brazma et al. 2001; Hucka et al. 2002, 2003; Lloyd et al. 2004; Orchard et al. 2007; Hermjakob et al. 2004).

  8. With the ab initio (or intrinsic) method, the DNA sequence is screened for specific features of protein-coding genes. These features are categorised as either signals or content. Signals are parts of the sequence that indicate the presence of a gene nearby, e.g. splice sites, PolyA sites. Content reflects known statistical properties of genes, which discriminate between potential coding and non-coding parts of the sequence. The extrinsic (or evidence-based) method screens for DNA sequences corresponding to known mRNA sequences or proteins. With comparative methods, the genome of an organism is compared to the genome of related species. These methods are based on the principle that biological functional sequences tend to be conserved between species (Brent 2008; Castelli et al. 2004).

  9. E.g. enzyme-centred databases that collect functional information on enzymes, such as BRENDA (Barthelmes et al. 2007) or SwissProt (Boutet et al. 2007), and pathway databases that describe the biochemistry of metabolic processes, such as EcoCyc for E. coli metabolism (Karp et al. 2007) or UM- BDD for microbial biodegradation pathways (Ellis et al. 2006).

  10. Regarding the reliability of these data, the strongest evidence for the presence of a metabolic reaction is found if an enzyme has been isolated directly from the organism and its function is demonstrated. Functional assignment to ORFs based on DNA sequence homology can also be used as strong evidence for the presence of a reaction in an organism. Physiological evidence, such as the known ability of the cell to produce a certain compound, helps to include reactions into the network. When a model is established, simulation can lead to incorporating additional pathways or reactions into the reconstruction.

  11. Besides constrained-based modelling, other platforms enabling mathematical modelling of the whole cell have been developed, such as E-cell (Tomita 2001) and the Virtual Cell (Loew and Schaff 2001).

  12. ftp://ftp.cordis.europa.eu/pub/lifescihealth/docs/systems_biology_worskhop_report_jan2005.pdf.

  13. www.biosapiens.info/.

  14. The personal genome project or PGP—named in reference to the human genome project or HGP (Church 2005)—is situated in this context.

  15. These time estimates do not remain constant in Kitano’s work. In Kitano (2001), it is argued that systems biology will mature in the next few years. However, in Kitano (2005, 2), it is argued that “while creating predictive models of limited scope could be a practical target for the next 5 years, a predictive model of an entire cell is not within our foreseeable reach at this moment”.

  16. See at www.bio.vu.nl/microb/research/celbioinf.html.

  17. For an overview of classical theory reduction, i.e. whether MB can be reduced to physics, and whether Mendelian genetics can be reduced to molecular genetics, see Sarkar (2007).

  18. By extension, genome projects have been taken to bear witness of a belief in the possible reduction of the evolution, development and organization of living organisms to their genetic level, implying that the role of non-genetic factors is less important, and attributing genes with ‘essentialistic’ characteristics such as containing all necessary information to construct phenotypes or being the sole heritable or selectionable unit of life. See Sarkar (1996), Keller (2000a), Mikulecky (2001), Moss (2003) and Strohman (2002).

  19. Boogerd et al. (2007, 13) state that “the essential difference between a mechanistic explanation and a reductionistic explanation lies at that heart of systems biology”. Mechanistic explanations try to make clear how the combined behaviour of parts, while embedded in the system, brings about so-called ‘emergent’ behaviour of the system. The goal would be “to develop a more enhanced understanding of both mechanism and emergentism, one which moves beyond the more standard mechanism/reductionism and mechanism/eliminativism dichotomies” (Richardson and Stephan 2007, 126).

  20. In this context, Boogerd et al. (2007) argue in favour of metabolic and hierarchical control theory. This allows constructing a hierarchical view of the organisation of the molecular reaction network of cells. As a result, subsystems are seen as modules that can be analysed in isolation of each other. Based on the analysis of regulatory interactions between these subsystems, they can be reintegrated. A major contribution of this approach is the concept of distributed control and regulation, referring to the idea that in a specific pathway not one rate-limiting enzyme can be identified. Instead, the degree of influence of an enzyme on a particular pathway has to be measured on a continuous scale.

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Acknowledgments

This research was financially supported by Ghent University (GOA Project 01GA0105) and the Fund for Scientific Research, FWO-Flanders. The authors thank Marcelle Holsters and Geert De Jaeger (UGent/VIB, Department of Plant Systems Biology), the editor Thomas Reydon and two anonymous reviewers for providing insightful and stimulating comments on earlier drafts.

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  1. VIB, Department of Molecular Genetics/Department of Plant Systems Biology, Ghent University, Technologiepark 927, 9052, Ghent, Belgium

    Philippe De Backer

  2. Department of Philosophy and Moral Science, Ghent University, Blandijnberg 2, 9000, Ghent, Belgium

    Danny De Waele

  3. FWO Flanders, Department of Philosophy and Moral Science, Ghent University, Blandijnberg 2, 9000, Ghent, Belgium

    Linda Van Speybroeck

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De Backer, P., De Waele, D. & Van Speybroeck, L. Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?. Acta Biotheor 58, 15–49 (2010). https://doi.org/10.1007/s10441-009-9089-6

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