Abstract
Induction of a specific transcriptional program by external signaling inputs is a crucial aspect of intracellular network functioning. The theoretical concept of coexisting attractors representing particular genetic programs is reasonably adapted to experimental observations of “genome-wide” expression profiles or phenotypes. Attractors can be associated either with developmental outcomes such as differentiation into specific types of cells, or maintenance of cell functioning such as proliferation or apoptosis. Here we review a mechanism known as speed-dependent cellular decision making (SdCDM) in a small epigenetic switch and generalize the concept to high-dimensional space. We demonstrate that high-dimensional network clustering capacity is dependent on the level of intrinsic noise and the speed at which external signals operate on the transcriptional landscape.
Highlights
The conceptual framework of attractors in phase space representing particular transcriptional programs has been demonstrated in experimental observations of ‘‘genome-wide’’ expression profiles, e.g. in neutrophil differentiation [1,2]
One of the mechanisms reported here explores this in connection with Speed-dependent Cellular Decision Making (SdCDM) observed in low order circuit models [5], but in a high-dimensional circuit
Because the external signals end in the same values, one only has a transient asymmetry which biases the cellular decision making towards one of the available states in region IIA
Summary
The conceptual framework of attractors in phase space representing particular transcriptional programs has been demonstrated in experimental observations of ‘‘genome-wide’’ expression profiles, e.g. in neutrophil differentiation [1,2]. Clustering of Input Signal Combinations In order to understand if differences in time-dependent input signal profiles force the system to converge to different attractors, we tested the response of the high-dimensional decision switch to a batch of 100 combinations of inputs, Ik(t)~(S1(t),:::,S5(t))k, generated by randomly selecting TSi ’s (see Fig. 1B for illustration purposes) for each input Si. The maximum amplitude Smax allowed for each signal Si was 2.
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