Abstract
A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.
Highlights
Systems biology studies how a biological system evolves to perform certain function(s), i.e., the design principles of the system [1,2,3]
The dynamic property we study is the resettable bistability of an Rb-E2F gene network that controls the mammalian cell cycle entry
In this work we present a thorough comparison of two different reverse engineering approaches, CPSS and individual topology search (ITS)
Summary
Systems biology studies how a biological system evolves to perform certain function(s), i.e., the design principles of the system [1,2,3]. As a common reverse engineering procedure, one can model each network topology against a collection of random parameter sets, and evaluate the robustness of each network topology (i.e., the proportion of the parameter sets allowing each topology to produce the desired dynamic feature). This procedure, which we name as ITS (individual topology search), has been successfully adopted to analyze several important biological processes including segment polarity [7], perfect adaption [8], and bistability [13]. Suppose that the parameter-space dimension is N, and that for each parameter, half of the parameter range falls into the good region, the maximum fraction of the good region with the parameter space is 1/2N
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
