A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Gene therapy: Principles, practice, problems and prospects

The remarkable advances made in recombinant DNA technology over the last two decades have paved way for the use of gene transfer to treat human diseases. Several protocols have been developed for the introduction and expression of genes in humans, but the clinical efficacy has not been conclusively demonstrated in any of them. The eventual success of gene therapy for genetic and acquired disorders depends on the development of better gene transfer vectors for sustained, long term expression of foreign genes as well as a better understanding of the pathophysiology of human diseases, it is heartening to note that some of the gene therapy protocols have found other applications such as the genetic immunization or DNA vaccines, which is being heralded as the third vaccine revolution, Gene therapy is yet to become a dream come true, but the light is seen at the end of the tunnel.

Role of elasto-plastic analysis under cyclic loading in fatigue crack growth studies

Linear Elastic Fracture Mechanics (LEFM) has been widely used in the past for fatigue crack growth studies, but this is acceptable only in situations which are within small scale yielding (SSY). In many practical structural components, conditions of SSY could be violated and one has to look for fracture criteria based on elasto-plastic analysis. Crack closure phenomenon, one of the most striking discoveries based on inelastic deformations during crack growth, has significant effect on fatigue crack growth rate. Numerical simulation of this phenomenon is computationally intensive and involved but has been successfully implemented. Stress intensity factors and strain energy release rates lose their meaning, J-integral (or its incremental) values are applicable only in specific situations, whereas alternate path independent integrals have been proposed in the literature for use with elasto-plastic fracture mechanics (EPFM) based criteria. This paper presents certain salient features of two independent finite element (numerical) studies of relevance to fatigue crack growth, where elasto-plastic analysis becomes significant. These problems can only be handled in the current day computational environment, and would have been only a dream just a few years ago.

Is The Burden of Medical Education Affecting Ethics of Medical Treatment?

A substantial number of medical students in India have to bear an enormous financial burden for earning a bachelor's degree in medicine referred to as MBBS (bachelor of medicine and bachelor of surgery). This degree program lasts for four and one-half years followed by one year of internship. A postgraduate degree, such as MD, has to be pursued separately on completion of a MBBS. Every medical college in India is part of a hospital where the medical students get clinical exposure during the course of their study. All or at least a number of medical colleges in a given state are affiliated to a university that mainly plays a role of an overseeing authority. The medical colleges usually have no official interaction with other disciplines of education such as science and engineering, perhaps because of their independent location and absence of emphasis on medical research. However, many of the medical colleges are adept in imparting high-quality and sound training in medical practices including diagnostics and treatment. The medical colleges in India are generally of two types, i.e., government owned and private. Since only a limited number of seats are available across India in the former category of colleges, only a small fraction of aspiring candidates can find admission in these colleges after performing competitively in the relevant entrance tests. A major advantage of studying in these colleges is the nominal tuition fees that have to be paid. On the other hand, a large majority of would-be medical graduates have to seek admission in the privately run medical institutes in which the tuition and other related fees can be mind boggling when compared to their public counterparts. Except for candidates of exceptionally affluent background, the only alternative for fulfilling the dream of becoming a doctor is by financing one's study through hefty bank loans that may take years to pay back. It is often heard from patients that they are asked by doctors to undergo a plethora of diagnostic tests for apparently minor illnesses, which may financially benefit those prescribing the tests. The present paper attempts to throw light on the extent of disparity in cost of a medical education between state-funded and privately managed medical colleges in India; the average salary of a new medical graduate, which is often ridiculously low when compared to what is offered in entry-level engineering and business jobs; and the possible repercussions of this apparently unjust economic situation regarding the exploitation of patients.