Deept Kumar

Photosynthetic Acclimation Is Reflected in Specific Patterns of Gene Expression in Drought-Stressed Loblolly Pine

Because the product of a single gene can influence many aspects of plant growth and development, it is necessary to understand how gene products act in concert and upon each other to effect adaptive changes to stressful conditions. We conducted experiments to improve our understanding of the responses of loblolly pine (Pinus taeda) to drought stress. Water was withheld from rooted plantlets of to a measured water potential of -1 MPa for mild stress and -1.5 MPa for severe stress. Net photosynthesis was measured for each level of stress. RNA was isolated from needles and used in hybridizations against a microarray consisting of 2,173 cDNA clones from five pine expressed sequence tag libraries. Gene expression was estimated using a two-stage mixed linear model. Subsequently, data mining via inductive logic programming identified rules (relationships) among gene expression, treatments, and functional categories. Changes in RNA transcript profiles of loblolly pine due to drought stress were correlated with physiological data reflecting photosynthetic acclimation to mild stress or photosynthetic failure during severe stress. Analysis of transcript profiles indicated that there are distinct patterns of expression related to the two levels of stress. Genes encoding heat shock proteins, late embryogenic-abundant proteins, enzymes from the aromatic acid and flavonoid biosynthetic pathways, and from carbon metabolism showed distinctive responses associated with acclimation. Five genes shown to have different transcript levels in response to either mild or severe stress were chosen for further analysis using real-time polymerase chain reaction. The real-time polymerase chain reaction results were in good agreement with those obtained on microarrays.

Atomic level computational identification of ligand migration pathways between solvent and binding site in myoglobin

Myoglobin is a globular protein involved in oxygen storage and transport. No consensus yet exists on the atomic level mechanism by which oxygen and other small nonpolar ligands move between the myoglobins buried heme, which is the ligand binding site, and surrounding solvent. This study uses room temperature molecular dynamics simulations to provide a complete atomic level picture of ligand migration in myoglobin. Multiple trajectories—providing a cumulative total of 7 μs of simulation—are analyzed. Our simulation results are consistent with and tie together previous experimental findings. Specifically, we characterize: (i) Explicit full trajectories in which the CO ligand shuttles between the internal binding site and the solvent and (ii) pattern and structural origins of transient voids available for ligand migration. The computations are performed both in sperm whale myoglobin wild-type and in sperm whale V68F myoglobin mutant, which is experimentally known to slow ligand-binding kinetics. On the basis of these independent, but mutually consistent ligand migration and transient void computations, we find that there are two discrete dynamical pathways for ligand migration in myoglobin. Trajectory hops between these pathways are limited to two bottleneck regions. Ligand enters and exits the protein matrix in common identifiable portals on the protein surface. The pathways are located in the “softer” regions of the protein matrix and go between its helices and in its loop regions. Localized structural fluctuations are the primary physical origin of the simulated CO migration pathways inside the protein.

Transcriptional Response of Saccharomyces cerevisiae to Desiccation and Rehydration

ABSTRACT A transcriptional analysis of the response of Saccharomyces cerevisiae strain BY4743 to controlled air-drying (desiccation) and subsequent rehydration under minimal glucose conditions was performed. Expression of genes involved in fatty acid oxidation and the glyoxylate cycle was observed to increase during drying and remained in this state during the rehydration phase. When the BY4743 expression profile for the dried sample was compared to that of a commercially prepared dry active yeast, strikingly similar expression changes were observed. The fact that these two samples, dried by different means, possessed very similar transcriptional profiles supports the hypothesis that the response to desiccation is a coordinated event independent of the particular conditions involved in water removal. Similarities between “stationary-phase-essential genes” and those upregulated during desiccation were also noted, suggesting commonalities in different routes to reduced metabolic states. Trends in extracellular and intracellular glucose and trehalose levels suggested that the cells were in a “holding pattern” during the rehydration phase, a concept that was reinforced by cell cycle analyses. Application of a “redescription mining” algorithm suggested that sulfur metabolism is important for cell survival during desiccation and rehydration.

Turning CARTwheels: an alternating algorithm for mining redescriptions

We present an unusual algorithm involving classification trees---CARTwheels---where two trees are grown in opposite directions so that they are joined at their leaves. This approach finds application in a new data mining task we formulate, called redescription mining. A redescription is a shift-of-vocabulary, or a different way of communicating information about a given subset of data; the goal of redescription mining is to find subsets of data that afford multiple descriptions. We highlight the importance of this problem in domains such as bioinformatics, which exhibit an underlying richness and diversity of data descriptors (e.g., genes can be studied in a variety of ways). CARTwheels exploits the duality between class partitions and path partitions in an induced classification tree to model and mine redescriptions. It helps integrate multiple forms of characterizing datasets, situates the knowledge gained from one dataset in the context of others, and harnesses high-level abstractions for uncovering cryptic and subtle features of data. Algorithm design decisions, implementation details, and experimental results are presented.

Algorithms for storytelling

We formulate a new data mining problem called storytelling as a generalization of redescription mining. In traditional redescription mining, we are given a set of objects and a collection of subsets defined over these objects. The goal is to view the set system as a vocabulary and identify two expressions in this vocabulary that induce the same set of objects. Storytelling, on the other hand, aims to explicitly relate object sets that are disjoint (and, hence, maximally dissimilar) by finding a chain of (approximate) redescriptions between the sets. This problem finds applications in bioinformatics, for instance, where the biologist is trying to relate a set of genes expressed in one experiment to another set, implicated in a different pathway. We outline an efficient storytelling implementation that embeds the CARTwheels redescription mining algorithm in an A* search procedure, using the former to supply next move operators on search branches to the latter. This approach is practical and effective for mining large data sets and, at the same time, exploits the structure of partitions imposed by the given vocabulary. Three application case studies are presented: a study of word overlaps in large English dictionaries, exploring connections between gene sets in a bioinformatics data set, and relating publications in the PubMed index of abstracts.

Algorithms for Storytelling

We formulate a new data mining problem called storytelling as a generalization of redescription mining. In traditional redescription mining, we are given a set of objects and a collection of subsets defined over these objects. The goal is to view the set system as a vocabulary and identify two expressions in this vocabulary that induce the same set of objects. Storytelling, on the other hand, aims to explicitly relate object sets that are disjoint (and, hence, maximally dissimilar) by finding a chain of (approximate) redescriptions between the sets. This problem finds applications in bioinformatics, for instance, where the biologist is trying to relate a set of genes expressed in one experiment to another set, implicated in a different pathway. We outline an efficient storytelling implementation that embeds the CARTwheels redescription mining algorithm in an A* search procedure, using the former to supply next move operators on search branches to the latter. This approach is practical and effective for mining large data sets and, at the same time, exploits the structure of partitions imposed by the given vocabulary. Three application case studies are presented: a study of word overlaps in large English dictionaries, exploring connections between gene sets in a bioinformatics data set, and relating publications in the PubMed index of abstracts.