Biomedical subjects

Ranadip Pal

Publications and source records attributed to Ranadip Pal.

5 recordsLinked to original sources

PubMed2006

Comparative non-cholinergic neurotoxic effects of paraoxon and diisopropyl fluorophosphate (DFP) on human neuroblastoma and astrocytoma cell lines.

The objective of this study was to evaluate the comparative non-cholinergic neurotoxic effects of paraoxon, which is acutely neurotoxic, and diisopropyl fluorophosphate (DFP), which induces OPIDN, in the human neuroblastoma SY5Y and the human astrocytoma cell line CCF-STTG1. SY5Y cells have been studied extensively as a model for OP-induced neurotoxicity, but CCF cells have not previously been studied. We conducted a preliminary human gene array assay of OP-treated SY5Y cells in order to assess at the gene level whether these cells can distinguish between OP compounds that do and do not cause OPIDN. Paraoxon and DFP induced dramatically different profiles of gene expression. Two genes were upregulated and 13 downregulated by at least 2-fold in paraoxon-treated cells. In contrast, one gene was upregulated by DFP and none was downregulated at the 2-fold threshold. This finding is consistent with current and previous observations that SY5Y cells can distinguish between OPs that do or do not induce OPIDN. We also examined gene array results for possible novel target proteins or metabolic pathways for OP neurotoxicity. Protein levels of glucose regulated protein 78 (GRP78) revealed that paraoxon exposure at 3 microM for 24 h significantly reduced GRP78 levels by 30% in neuroblastoma cells, whereas DFP treatment had no effect. In comparison with SY5Y neuroblastoma cells, paraoxon and DFP (3 microM for 24 h) each significantly increased GRP78 levels by 23-24% in CCF astrocytoma cells. As we have previously evaluated intracellular changes in Ca(2+) levels in SY5Y cells, we investigated the effects of paraoxon and DFP on cellular Ca(2+) homeostasis in CCF by studying cytosolic and mitochondrial basal calcium levels. A significant decrease in the ratio of mitochondrial to cytosolic Ca(2+) fluorescence was detected in CCF cultures treated for either 1 or 3 days with 1, 3, 10, or 30 microM paraoxon. In contrast, treatment with DFP for 1 day had no significant effect on the ratio of mitochondrial to cytosolic Ca(2+) fluorescence; after 3 days treatment, only 30 microM decreased the ratio. These results are consistent with the finding that paraoxon induced a greater decrease than did DFP of intracellular esterase activity in CCF cells. The changes seen in the ratio of mitochondrial to cytosolic Ca(2+) represent a good indicator of the degree of injury induced by each chemical tested. This work further develops in vitro models that distinguish between compounds that cause OPIDN and those that induce acute neurotoxicity only. The study also exposes additional OP-induced toxicities that may be obscured in vivo.

Astrocytoma↗

PubMed2005

Generating Boolean networks with a prescribed attractor structure.

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm

Algorithms↗

PubMed2004

Boolean relationships among genes responsive to ionizing radiation in the NCI 60 ACDS.

MOTIVATION: An early use of gene-expression data coming from microarrays was to discover non-linear multivariate intergene relationships. Pursuing this direction, the motivation for this paper is 2-fold: (1) to discover and elucidate multivariate logical predictive relations among gene expressions in a dataset arising from radiation studies using the NCI 60 Anti-Cancer Drug Screen (ACDS) cell lines; and (2) to demonstrate how these logical relations based on coarse quantization reflect corresponding relations in the continuous data. RESULTS: Using the coefficient of determination, a large number of logical relationships have been discovered among genes in the NCI 60 ACDS cell lines. Moreover, these relationships can be seen directly in the original continuous data, and many are robust relative to the thresholds used to obtain the logical data from the continuous data. A key observation is that a number of intergene relationships appear to be considerably stronger when p53 is functional as compared to when it is not, which is consistent with earlier findings in the literature. AVAILABILITY: The appendix is available at http://gsp.tamu.edu/Publications/supplement.htm CONTACT: edward@ee.tamu.edu.

Cell Line, Tumor↗

PubMed2004

Intervention in context-sensitive probabilistic Boolean networks.

MOTIVATION: Intervention in a gene regulatory network is used to help it avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is essentially a finite collection of Boolean networks in which at any discrete time point the gene state vector transitions according to the rules of one of the constituent networks. For an instantaneously random PBN, the governing Boolean network is randomly chosen at each time point. For a context-sensitive PBN, the governing Boolean network remains fixed for an interval of time until a binary random variable determines a switch. The theory of automatic control has been previously applied to find optimal strategies for manipulating external (control) variables that affect the transition probabilities of an instantaneously random PBN to desirably affect its dynamic evolution over a finite time horizon. This paper extends the methods of external control to context-sensitive PBNs. RESULTS: This paper treats intervention via external control variables in context-sensitive PBNs by extending the results for instantaneously random PBNs in several directions. First, and most importantly, whereas an instantaneously random PBN yields a Markov chain whose state space is composed of gene vectors, each state of the Markov chain corresponding to a context-sensitive PBN is composed of a pair, the current gene vector occupied by the network and the current constituent Boolean network. Second, the analysis is applied to PBNs with perturbation, meaning that random gene perturbation is permitted at each instant with some probability. Third, the (mathematical) influence of genes within the network is used to choose the particular gene with which to intervene. Lastly, PBNs are designed from data using a recently proposed inference procedure that takes steady-state considerations into account. The results are applied to a context-sensitive PBN derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation, since the available data suggest that disruption of this influence could reduce the chance of a melanoma metastasizing.

Algorithms↗

PubMed2004

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

MOTIVATION: We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. RESULTS: We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Algorithms↗