JigCell: A Software for Theoretical Biology
25.11.2020
As a team of researchers, we have been seeking novel approaches to better our understanding of how we remember.
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Enter Jigcell.
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"The ever-growing size and complexity of molecular network models make them difficult to construct and understand. Our approach to modeling is to build large models by combining together smaller models, making them easier to comprehend.
At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces called ports. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling" [1].
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This short overview of JigCell reads that it offers a perfect platform for analyzing biochemical processes, as perplexing as long-term-potentiation, and long-term depression (LTP and LDP, respectively). From the standpoint of how we remember, these two mechanisms, which concurrently operate in the brain, indeed afford two converse outcomes. That is, the former represents the biochemical mechanism of how we learn and thus, we remember, as the latter corresponds to how we "unlearn" in some way.
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Intriguingly, these two mechanisms are incepted through the same signal: calcium. All start with the oscillative influx of calcium (Ca) in the post-synapsis. Therein, the main target of calcium is Calmodulin kinase (CaMK), which acts as a molecular switch during the course of LTP. Upon the elevation in calcium concentration, Calmodulin kinase that is composed of ten subunits expands to be "turned on" and a sequential reaction of self-phosphorylation of subunits is triggered (Figure 1). Then, CaMK activates some other targets in downstream reactions, leading up to LTP [2].
Figure 1. "Turning On/Off" dynamics of calmodulin kinase, followed by its complexation with calcium (click to enlarge) [3].
In this work, we focused on modeling these chemical processes through Jigcell. For this, we based our methodology on constructing two modules; the first one (a.k.a., "Calcium module") to emulate the oscillating influx of calcium to the post-synapsis and the second one (a.k.a., "Calmodulin module") to model the sequential phosphorylation of CaMK subunits. Upon their construction, the modules are interconnected through calcium, as such that the output of the calcium module becomes the input of the Calmodulin module. (Figure 2). In formulating these modules, we have utilized the set of reactions that was previously reported by Zhabotinsky [2].
Figure 2. The composition of both modules; module 1 to emulate the calcium influx and module 2 to model the phosphorylation profile of calmodulin kinase subunits (click to enlarge).
Figure 3. (Left) The oscillating influx of calcium to post-synapsis and (Right) The phosphorylation profile of Calmodulin kinase subunits that is triggered upon the influx of calcium (click to enlarge).
All the equations for the species, in conjunction with the global quantities, are given in the following pdf file.
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The model is available from GitHub.
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JigCell and COPASI can be downloaded for free from the following link.
References
1) Jones Jr, T. C., Hoops, S., Watson, L. T., Palmisano, A., Tyson, J. J., & Shaffer, C. A. (2018). JigCell Model Connector: building large molecular network models from components. Simulation, 94(11), 993-1008. [Link]
2) Zhabotinsky, A. M. (2000). Bistability in the Ca2+/calmodulin-dependent protein kinase-phosphatase system. Biophysical Journal, 79(5), 2211-2221. [Link]
3) Lisman, J., Yasuda, R., & Raghavachari, S. (2012). Mechanisms of CaMKII action in long-term potentiation. Nature reviews neuroscience, 13(3), 169-182. [Link]