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J Theor Biol. 2017 Mar 21;417:51-60. doi: 10.1016/j.jtbi.2017.01.027. Epub 2017 Jan 19.

In silico predicted reproductive endocrine transcriptional regulatory networks during zebrafish (Danio rerio) development.

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Department of Marine Biology, Texas A&M University at Galveston, 200 SeaWolf Parkway, Galveston, TX, 77553, USA. Electronic address:


The interconnected topology of transcriptional regulatory networks (TRNs) readily lends to mathematical (or in silico) representation and analysis as a stoichiometric matrix. Such a matrix can be 'solved' using the mathematical method of extreme pathway (ExPa) analysis, which identifies uniquely activated genes subject to transcription factor (TF) availability. In this manuscript, in silico multi-tissue TRN models of brain, liver and gonad were used to study reproductive endocrine developmental programming in zebrafish (Danio rerio) from 0.25h post fertilization (hpf; zygote) to 90 days post fertilization (dpf; adult life stage). First, properties of TRN models were studied by sequentially activating all genes in multi-tissue models. This analysis showed the brain to exhibit lowest proportion of co-regulated genes (19%) relative to liver (23%) and gonad (32%). This was surprising given that the brain comprised 75% and 25% more TFs than liver and gonad respectively. Such 'hierarchy' of co-regulatory capability (brain<liver<gonad) indicated presence of highly gene-specific TRNs in the brain, alluding to its role as 'master controller' of endocrine function. Second, TRN models were constrained with varying TF availabilities during zebrafish development. Normalized numbers of genes active during development showed concomitant activations between brain and gonad from 10 to 12 hpf (embryonic life stage) up to 30-90 dpf (adult life stage). This indicated a putative 'syncing' between the brain and gonad, and initiation of an early reproductive endocrine developmental program. Finally, comparison of in vivo active genes with those predicted in silico showed relatively good agreement for brain (49%), liver (27%) and gonad (32%). The multi-tissue TRN models presented can lend diagnostic insights into the effects of changing environmental and/or genetic constraints on reproductive endocrine function.


Boolean rules; Developmental programming; Extreme pathway analysis; Stoichiometric matrix; Transcriptional network

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