Fitting structure to function in gene regulatory networks

Abstract

Cascades of transcriptional regulation are the common source of the forward drive in all developmental systems. Increases in complexity and specificity of gene expression at successive stages are based on the collaboration of varied combinations of transcription factors already expressed in the cells to turn on new genes, and the logical relationships between the transcription factors acting and becoming newly expressed from stage to stage are best visualized as gene regulatory networks. However, gene regulatory networks used in different developmental contexts underlie processes that actually operate through different sets of rules, which affect the kinetics, synchronicity, and logical properties of individual network nodes. Contrasting early embryonic development in flies and sea urchins with adult mammalian hematopoietic development from stem cells, major differences are seen in transcription factor dosage dependence, the silencing or damping impacts of repression, and the impact of cellular regulatory history on the parts of the genome that are accessible to transcription factor action in a given cell type. These different features not only affect the kinds of models that can illuminate developmental mechanisms in the respective biological systems, but also reflect the evolutionary needs of these biological systems to optimize different aspects of development.

From Gregor Mendel to Eric Davidson: Mathematical Models and Basic Principles in Biology

“…The fruitfulness of the concept of developmental GRNs has been widely acknowledged by many biological scientists, as well as by historians of biology (e.g., Fox Keller, 2002; Cameron, 2015; Rothenberg, 2016; Buckingham, 2017). It has been taken up and modified by many biologists, for example, by James Briscoe in his research on the development of the central nervous system (Gouti et al, 2017) and by Ellen Rothenberg in her research on hematopoietic stem cell differentiation (Rothenberg, 2017a). In much of the research on developmental GRNs, mathematical modeling has become crucial.…”

“…Ellen Rothenberg, who has been working on the development of the mammalian postnatal hematopoietic system and has not yet completed a fully elaborated model of her system, reflected on models' differences and on an evolutionary explanation for the explanatory powers of different GRN models: She agreed with Eric Davidson that “there is little doubt that ultimately there are gene networks that drive development.” But she also draws attention to the fact that the analytical and predictive models that capture essential features of GRNs need to be considered separately for different modes of development. This is because “the importance of quantitative effects on target gene expression as opposed to qualitative ones, the precision of timing of state changes, and the nature of negative regulation can all differ considerably between biological systems” (Rothenberg, 2017a). Thus, while “the sea urchin embryo is highly precise and accurate about both cell fate determination and timing of differentiation of every cell type in the embryo, and has evolved to accomplish this by relative insensitivity to absolute levels of expression of key regulators,” the mammalian postnatal hematopoietic system “has evolved to emphasize plasticity and environmental responsiveness at the expense of precision in both timing and cell fate determinism” (Rothenberg, 2017a).…”

“…This is because “the importance of quantitative effects on target gene expression as opposed to qualitative ones, the precision of timing of state changes, and the nature of negative regulation can all differ considerably between biological systems” (Rothenberg, 2017a). Thus, while “the sea urchin embryo is highly precise and accurate about both cell fate determination and timing of differentiation of every cell type in the embryo, and has evolved to accomplish this by relative insensitivity to absolute levels of expression of key regulators,” the mammalian postnatal hematopoietic system “has evolved to emphasize plasticity and environmental responsiveness at the expense of precision in both timing and cell fate determinism” (Rothenberg, 2017a).…”

“…She emphasizes that some of the art of network analysis is to use sufficient prior knowledge to focus on genes that are most likely to be functionally important. She holds that network models, even if they are incomplete, are useful because they are crucial to capturing large bodies of complex experimental results in a comprehensive way; they can clarify the understanding of biological relationships that do not fit a preconceived notion; they can point the way to experiments that improve understanding; their comparison with observed gene expression patterns can reveal what kinds of functions are missing (Rothenberg, 2017).…”