Equal Rights, Special Rights, and the Nature of Antidiscrimination Law

Despite the continued belief held by most Americans that certain characteristics should not form the basis for adverse decisions about individuals in employment, housing, public accommodations, and the provision of a wide range of governmental and private services and opportunities, antidiscrimination laws have increasingly come under attack on the ground that they provide members of the group against whom discrimination is forbidden with special rights. The special rights objection has been voiced most strongly, but not exclusively, against laws that seek to prohibit discrimination on the basis of sexual orientation. This line of attack has not always been effective, but it has achieved notable success. To give one recent example, in February 1998, the people of Maine voted to repeal a relatively new state law prohibiting discrimination in employment, housing, public accomodations, and credit on the basis of sexual orientation. A leader of that repeal effort subsequently concluded that the vote demonstrated that [t]he American people rejected the notion of special rights for gay men and lesbians. This special rights argument has not been limited to public campaigns. Indeed, the rhetoric of special rights has now begun to move from popular discourse into the legal analysis of antidiscrimination law. This movement presents a threat to efforts to achieve equality in the United States, for it suggests that courts may conflate antidiscrimination laws that essentially mirror the Constitution\u27s own command4 with affirmative action provisions whose constitutionality can be determined under current law only after they have been subjected to searching judicial scrutiny

Memory effects in biochemical networks as the natural counterpart of extrinsic noise

We show that in the generic situation where a biological network, e.g. a protein interaction network, is in fact a subnetwork embedded in a larger "bulk" network, the presence of the bulk causes not just extrinsic noise but also memory effects. This means that the dynamics of the subnetwork will depend not only on its present state, but also its past. We use projection techniques to get explicit expressions for the memory functions that encode such memory effects, for generic protein interaction networks involving binary and unary reactions such as complex formation and phosphorylation, respectively. Remarkably, in the limit of low intrinsic copy-number noise such expressions can be obtained even for nonlinear dependences on the past. We illustrate the method with examples from a protein interaction network around epidermal growth factor receptor (EGFR), which is relevant to cancer signalling. These examples demonstrate that inclusion of memory terms is not only important conceptually but also leads to substantially higher quantitative accuracy in the predicted subnetwork dynamics

Michaelis-Menten dynamics in protein subnetworks

To understand the behaviour of complex systems it is often necessary to use models that describe the dynamics of subnetworks. It has previously been established using projection methods that such subnetwork dynamics generically involves memory of the past, and that the memory functions can be calculated explicitly for biochemical reaction networks made up of unary and binary reactions. However, many established network models involve also Michaelis-Menten kinetics, to describe e.g. enzymatic reactions. We show that the projection approach to subnetwork dynamics can be extended to such networks, thus significantly broadening its range of applicability. To derive the extension we construct a larger network that represents enzymes and enzyme complexes explicitly, obtain the projected equations, and finally take the limit of fast enzyme reactions that gives back Michaelis-Menten kinetics. The crucial point is that this limit can be taken in closed form. The outcome is a simple procedure that allows one to obtain a description of subnetwork dynamics, including memory functions, starting directly from any given network of unary, binary and Michaelis-Menten reactions. Numerical tests show that this closed form enzyme elimination gives a much more accurate description of the subnetwork dynamics than the simpler method that represents enzymes explicitly, and is also more efficient computationally