Johannes F. Knabe

Genetic regulatory network models of biological clocks: Evolutionary history matters

We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites—each involving a finite number of inhibitory and activatory binding factors—is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

The NetBuilder' project: development of a tool for constructing, simulating, evolving, and analysing complex regulatory networks

Original paper can be found at: http://www.biomedcentral.com/bmcsystbiol/archive --DOI : 10.1186/1752-0509-1-S1-P72

Regulation of gene regulation - smooth binding with dynamic affinity affects evolvability

Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. We compare the evolvability of a static network model to a more realistic regulatory model with dynamic structure. In the former model, each regulatory protein-binding site is always influenced by exactly one gene product. In the latter model, binding is only more likely to occur the better the match between site and gene product is (smooth binding) and, in addition, affinity dynamically changes under the action of specificity factors during a cellpsilas lifetime. On evolutionary timescales, this means that often the strength of influences between nodes is perturbed instead of direct changes being made to network connectivity. A main result is that for evolutionary search spaces of increasing sizes evolved performance drops much more strongly in the classical network model as compared to the smooth binding model. This effect was even greater in the case of using smooth binding together with specificity factors.

Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems

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Biological Clocks and Differentiation

[Bonner(2001)] has pointed out the key importance of inventing a stimulus-response system for the evolution of live on earth, in addition to metabolism and replication. As incessant responsiveness [West-Eberhard(2003)] and the basis of signalling it has come to be a characteristic of life on earth. Not all organismal responses to external stimuli are simple reactive responses, but behaviour generally depends also on the organism’s internal state - and this state can reflect environmental processes as is the case in biological clocks (section 4.2.1) - such internalisation can be evolutionarily advantageous in noisy environments.

Genetic Regulatory Networks

With the discovery of DNA a full understanding of the program controlling all cells1 seemed in reach, as most researchers assumed that an organism is a direct reflection of its constituent genes2.

Development and Morphogenesis

In morphogenesis1 dividing cells assemble into differentiated shapes, using decentralised control and self-organisation. The development of multicellular organisms from a single fertilised egg cell has fascinated humans at least since Aristotle’s speculations more than 2000 years ago [Wolpert et al(2007)Wolpert, Jessell, Lawrence, Meyerowitz, Robertson, and Smith]. In the more recent past our understanding of how interacting genes direct developmental processes has greatly increased [West-Eberhard(2003),Gerhart and Kirschner(1997),Wolpert et al(2007)Wolpert, Jessell, Lawrence, Meyerowitz, Robertson, and Smith, Arthur(2000)], see earlier sections on evo-devo, 2.2, and its molecular basis, section 3.1.1. Cell differentiation, the inducing effects of intercellular signalling via “morphogens”, changes in cell form like contraction, the self-organising properties of adhesion and cell sorting in animal morphogenesis [Glazier and Graner(1993)] are among the important principles better understood now. [Nehaniv(2005)] discusses GRNs as a potential computational paradigm with high evolvability. And although every cell is controlled by a Genetic Regulatory Network (GRN), the resulting multicellular dynamics are also strongly influenced by physical constraints.