COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 159
cal functionality can (but does not always) arise from changes in network topology rather than
changes in the elements themselves. This functionality includes networks that exhibit the behavior
associated with negative and positive feedback loops, oscillators, and toggle switches. By showing
that functionality can change dramatically due to changes in topology, Guet et al. argue that once a
simple set of genes and regulatory elements is in place, it is possible to jump discontinuously from
one functional phenotype to another using the same “toolkit” of genes simply by modifying the
regulatory connections. Such discontinuous changes are different from the more gradual effects driven
by successive point mutations.
Such discontinuities reflect the nonlinear nature of genetic networks. Furthermore, the topology of
connectivity of a network does not necessarily determine its behavior uniquely, and the behavior of
even simple networks built out of a few well-characterized components cannot always be inferred from
connectivity diagrams alone. Because genetic networks are nonlinear (and stochastic as well), the un-
known details of interactions between components might be of crucial importance to understanding
their functions. Combinatorially developed libraries of simple networks may thus be useful in uncover-
ing the existence of additional regulatory mechanisms and exploring the limits of quantitative modeling
of cellular systems.
The system of Guet et al. uses a small number of elements restricted to a single type of interaction
(transcriptional regulation), but the range of biochemical interactions can be extended by including
other modular genetic elements. For example, the approach can be extended to include linking input
and output through cell-cell signaling molecules, such as those involved in quorum sensing. Also, this
combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors,
oscillators, and amplifiers, as well as for high-level structural properties such as robustness or noise
resistance.
5.4.3.5 Identifying Systems Responses by Combining Experimental
Data with Biological Network Information
Mawuenyega et al. have developed a method to identify specific subnetworks in large biological
networks.^84 A biological network is constructed by identifying components (genes, proteins, transcrip-
tion factors, chemicals) and interactions between components (protein-protein, protein-DNA, signal
transduction, gene expression, catalysis) from genome context information as well as from external
sources (databases, literature, and direct interaction with experimentalists). By superimposing experi-
mental data such as expression values or identified proteins, it is possible to identify a best-scored
subnetwork in the large biological network. This subnetwork is known as the response network, identify-
ing a system’s response with respect to the experimental scenario and data used.
Proteomic mass spectroscopy (MS) analysis was used to identify and characterize 1,044 Mycobacte-
rium tuberculosis (TB) proteins and their corresponding cellular locations. From these 1,044 identified, 70
proteins were selected that are known to function in lipid biosynthesis (20) and fatty acid degradation
(50). It is striking that the identified proteins involved in fatty acid degradation were distributed be-
tween the different cellular compartments in an almost exclusive fashion (e.g., in the subnetwork
centered on fadB2 and fadB3) (Figure 5.9).
In addition, Forst and colleagues performed a response network analysis of mycobacterium tubercu-
losis to isoniazid (INH) drug treatment.^85 The entirety of the FAS-II fatty acid synthase group (except
(^84) K.G. Mawuenyega, C.V. Forst, K.M. Dobos, J.T. Belisle, J. Chen, M.E. Bradbury, A.R. Bradbury, and X. Chen, “Mycobacte-
rium Tuberculosis Functional Network Analysis by Global Subcellular Protein Profiling,” Molecular Biology of the Cell 16:396-404,
2005.
(^85) L. Cabusora, E. Sutton, A. Fulmer, and C.V. Forst, “Differential Network Expression During Drug and Stress Response,”
Bioinformatics 21:2898-2905, 2005, available at http://bioinformatics.oupjournals.org/cgi/content/abstract/bti440v1.