Eli Meir

The segment polarity network is a robust developmental module

All insects possess homologous segments, but segment specification differs radically among insect orders. In Drosophila, maternal morphogens control the patterned activation of gap genes, which encode transcriptional regulators that shape the patterned expression of pair-rule genes. This patterning cascade takes place before cellularization. Pair-rule gene products subsequently ‘imprint’ segment polarity genes with reiterated patterns, thus defining the primordial segments. This mechanism must be greatly modified in insect groups in which many segments emerge only after cellularization. In beetles and parasitic wasps, for instance, pair-rule homologues are expressed in patterns consistent with roles during segmentation, but these patterns emerge within cellular fields. In contrast, although in locusts pair-rule homologues may not control segmentation, some segment polarity genes and their interactions are conserved. Perhaps segmentation is modular, with each module autonomously expressing a characteristic intrinsic behaviour in response to transient stimuli. If so, evolution could rearrange inputs to modules without changing their intrinsic behaviours. Here we suggest, using computer simulations, that the Drosophila segment polarity genes constitute such a module, and that this module is resistant to variations in the kinetic constants that govern its behaviour.

Robustness, Flexibility, and the Role of Lateral Inhibition in the Neurogenic Network

BACKGROUND Many gene networks used by developing organisms have been conserved over long periods of evolutionary time. Why is that? We showed previously that a model of the segment polarity network in Drosophila is robust to parameter variation and is likely to act as a semiautonomous patterning module. Is this true of other networks as well? RESULTS We present a model of the core neurogenic network in Drosophila. Our model exhibits at least three related pattern-resolving behaviors that the real neurogenic network accomplishes during embryogenesis in Drosophila. Furthermore, we find that it exhibits these behaviors across a wide range of parameter values, with most of its parameters able to vary more than an order of magnitude while it still successfully forms our test patterns. With a single set of parameters, different initial conditions (prepatterns) can select between different behaviors in the networks repertoire. We introduce two new measures for quantifying network robustness that mimic recombination and allelic divergence and use these to reveal the shape of the domain in the parameter space in which the model functions. We show that lateral inhibition yields robustness to changes in prepatterns and suggest a reconciliation of two divergent sets of experimental results. Finally, we show that, for this model, robustness confers functional flexibility. CONCLUSIONS The neurogenic network is robust to changes in parameter values, which gives it the flexibility to make new patterns. Our model also offers a possible resolution of a debate on the role of lateral inhibition in cell fate specification.

Variation Thresholds for Extinction and Their Implications for Conservation Strategies

We examine the degree to which fitting simple dynamic models to time series of population counts can predict extinction probabilities. This is both an active branch of ecological theory and an important practical topic for resource managers. We introduce an approach that is complementary to recently developed techniques for estimating extinction risks (e.g., diffusion approximations) and, like them, requires only count data rather than the detailed ecological information available for traditional population viability analyses. Assuming process error, we use four different models of population growth to generate snapshots of population dynamics via time series of the lengths commonly available to ecologists. We then ask to what extent we can identify which of several broad classes of population dynamics is evident in the time series snapshot. Along the way, we introduce the idea of “variation thresholds,” which are the maximum amount of process error that a population may withstand and still have a specified probability of surviving for a given length of time. We then show how these thresholds may be useful to both ecologists and resource managers, particularly when dealing with large numbers of poorly understood species, a common problem faced by those designing biodiversity reserves.

How do the design of monitoring and control strategies affect the chance of detecting and containing transgenic weeds

Monitoring is a compromise response to environmental debates Environmentalists are concerned with the risks associated with transgenic crops, whereas many promoters of biotechnology can see only benefits [1]. As with many such debates, the solution is a compromise, which may satisfy neither side. One key to making both parties agree to such a compromise can be an effective monitoring program. From an environmentalist’s perspective monitoring can serve to alert the public that there is indeed an ecological problem, in spite of our best hopes. From a biotechnologist’s point of view, monitoring can substantiate that no ecological problems have been forthcoming, and that thus regulations should be relaxed. Monitoring needs to be sensitive enough that it triggers an alarm before it is too late. On the other hand, monitoring must not be so unrealistically sensitive to the point that it could never yield a verdict of safety. In this paper we explore the promise and limitations of monitoring for escaped transgenic plants that are on the verge of becoming serious weed problems. More specifically, the ecological risk at which our monitoring program is aimed is simply the establishment and spread of a novel plant. Although this is a simplistic “risk”, it might well be a practical target for transgenic monitoring, since without spread and proliferation transgenes are unlikely to produce substantial impacts.

Contributions of Spatially Explicit Landscape Models To Conservation Biology

The practice of conservation is often a form of land management. One of the most powerful approaches for connecting the needs of a particular species with land usage is the linking of biologically-detailed models of that species dispersal and demography with geographic information systems (GIS). For example, juvenile spotted owls must depart their birthplace in search of unoccupied expanses of old growth forest. Maps that detail the scarcity, fragmentation, and location of remnant old growth stands dramatize how difficult a search these juvenile owls may face in heavily logged portions of the Pacific Northwest. By connecting these spatially detailed maps with a model of how owls disperse and reproduce, managers can construct logging plans that make the best of what little old growth might remain. We call such approaches spatially explicit population models (or SEPMs) because they assign habitats and owls to particular locations in space, and depending upon the number and placement of individuals, they predict population change as a result of dispersal, mortality, and reproduction. The emergence of user-friendly GIS software, the maturing of ecological theory pertaining to population dynamics in fragmented habitats, and the increased popularity of individual behavior simulation models have combined to produce a tremendous enthusiasm for SEPM’s (see Ecological Applications, issue #1, volume 7, 1995).