Biomedical subjects

O E Percus

Publications and source records attributed to O E Percus.

4 recordsLinked to original sources

PubMed2000

Random binding of dimers to chains.

We develop a probabilistic model for the binding of a small linear polymer to a larger chain. We assume that we can approximate the energy of interaction of the two chains by summing the pairwise interactions between subunits. Because the energy of interaction between a pair of subunits can depend on neighboring subunits, which we assume vary along the chain, we assign the pairwise energies of interactions according to a specified probability distribution. Thus we develop a statistical model for the binding of two molecules. While such models may not be appropriate for studying the interaction of a particular pair of molecules, they can provide insight into questions that deal with populations of molecules, such as why do MHC molecules bind peptides of a certain size? Here we analyze in detail the special case of a heterodimer binding to a polymer.

HLA Antigens↗

PubMed1999

Island length distribution in genome sequencing.

We consider the general problem of constructing a physical map of a genome by welding islands of overlapping clones. Both distribution of clone length and non-uniform probability of overlap detection are taken into account, the latter restricted to the Markov case in which only the location of the end of the developing island is required. Exact results for the distribution of island length are obtained in the special cases of fixed clone length or rigid overlap criterion, and mean and variance for the general situation. Determination of ocean length distribution permits island number and contig number distributions to be found as well.

Chromosome Mapping↗

PubMed1993

Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination.

The binding of antibody to antigen or T-cell receptor to major histocompatibility complex-peptide complex requires that portions of the two structures have complementary shapes that can closely approach each other. The question that we address here is how large should the complementary regions on the two structures be. The interacting regions are by necessity roughly the same size. To estimate the size (number of contact residues) of an optimal receptor combining region, we assume that the immune system over evolutionary time has been presented with a large random set of foreign molecules that occur on common pathogens, which it must recognize, and a smaller random set of self-antigens to which it must fail to respond. Evolutionarily, the receptors and the molecular groups that the immune system recognizes as epitopes are imagined to have coevolved to maximize the probability that this task is performed. The probability of a receptor matching a random antigen is estimated from this condition. Using a simple model for receptor-ligand interaction, we estimate that the optimal size binding region on immunoglobulin or T-cell receptors will contain about 15 contact residues, in agreement with experimental observation.

Animals↗

PubMed1984

Modified Bayes technique in sequential clinical trials.

We consider the problem of optimizing the treatment of a population by two drugs of unknown efficacy. The success or failure of each treatment is assumed to be known before the next patient arrives to be treated, and the objective is to use the developing information both to select optimally for a given patient and to asymptotically restrict treatment to the better of the two drugs. A straightforward Bayes estimator is first assumed. It is shown by computer simulation, and to some extent algebraically , that this leads to the possibility of "trapping" into treatment by the poorer drug, due to early anomalously poor performance by the better drug. The difficulty is ameliorated by imposing a bias towards success on the input (a priori) distribution of the unknown success probabilities. In fact, the resulting protocol, which is ethical from the point of view of the individual patient, is also superior for the full treated population to a few sampling-plus-stopping-rule techniques against which it is compared.

Clinical Trials as Topic↗