A combinatorial approach for mapping the interactions of multiple inputs to a biological system in a tractable number of experiments
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1
Icahn School of Medicine at Mount Sinai, Department of Neuroscience, United States
Multiple simultaneous inputs (e.g., neurochemical agents, drugs, therapeutic interventions) act together to affect the output of complex biological systems such as neurons and neural circuits or organs such as the heart. Describing how all these inputs act together on a system is difficult because the inputs often interact in ways that cannot be predicted from their individual actions. This happens because biological systems are nonlinearly coupled networks of many components through which the presence of one input can change the effect of another on the output. Full experimental mapping of all interactions between inputs is currently impractical for nontrivial input sets because of the combinatorial explosion: there are simply too many combinations of inputs to test each combination individually.
To solve the problem, we have developed a block-design approach in which we construct a small number of test sets, each containing a number of pairs (or higher tuples) of the inputs, such that together all the test sets contain all possible pairs multiple times. Similar algorithms are used in communications and computer systems testing, but our algorithm must satisfy the additional need for repeated testing to deal with inter-preparation variability seen in biological systems. Our algorithm furthermore adaptively reduces the number of experiments required depending on the results obtained so far. Finally, it incorporates statistical tests to evaluate how nonlinearly “unexpected” each pairwise interaction is, relative to the prediction from the linear combination of effects of the two (or more) inputs alone. The statistic ranks all of the pairwise interactions from the most to the least unexpected. The algorithm thus functions as a global screen for the most unexpected interactions in the input set. Even large numbers of inputs require only small, experimentally tractable, numbers of test sets. Altogether, our approach promises to be able to guide the experimental discovery and global mapping of interactions between inputs to a system without any knowledge of the internal structure of the system. We are testing and further refining the approach using the Luo-Rudy computational model of mammalian ventricular myocytes, and then applying it experimentally to map the interactions between the many neuromodulators of a crustacean cardiac system.
Acknowledgements
Funded by NSF IOS 1146019
Keywords:
neuromodulators,
drugs,
Multiple inputs,
Combinatorial explosion,
Block design,
Computational Biology,
biological networks
Conference:
Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.
Presentation Type:
Poster, to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Brezina
V and
Fulton-Howard
B
(2015). A combinatorial approach for mapping the interactions of multiple inputs to a biological system in a tractable number of experiments.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2015.
doi: 10.3389/conf.fnins.2015.91.00051
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Received:
07 Apr 2015;
Published Online:
05 Aug 2015.
*
Correspondence:
Dr. Vladimir Brezina, Icahn School of Medicine at Mount Sinai, Department of Neuroscience, New York, NY, United States, vladimir.brezina@gmail.com