RESUMO
The diagnosis and treatment of cancer is made difficult by the heterogeneous nature of the cell population. Determining its compositional breakup from measurements of various measurable traits (such as gene expression measurements) is an important problem in the field of cancer diagnosis and treatment. In addition, the computational aspect of the problem also needs attention. The processing of the collected data must be done as efficiently as possible in terms of computational speed and memory requirements. The use of Markov chain Monte Carlo methods is time consuming, and hence, other methods need to be used for the analysis. In this paper, we develop a model for heterogeneous cancer tissue, which uses quantitative polymerase chain reaction gene expression data to determine the compositional breakup of cell populations in the heterogeneous tissue. We develop a fast algorithm for the model using variational methods and demonstrate its use on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of the algorithm with other methods such as Markov chain Monte Carlo and expectation maximization.
Assuntos
Perfilação da Expressão Gênica/métodos , Modelos Biológicos , Modelos Estatísticos , Neoplasias/genética , Neoplasias/metabolismo , Adulto , Algoritmos , Teorema de Bayes , Linhagem Celular , Simulação por Computador , Fibroblastos/metabolismo , Humanos , Transdução de Sinais/genéticaRESUMO
An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple subpopulations. Each subpopulation reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. In this paper, we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real-world data collected from fibroblasts.
Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes , Modelos Biológicos , Modelos Estatísticos , Neoplasias , Algoritmos , Linhagem Celular Tumoral , Células Cultivadas , Simulação por Computador , Fibroblastos , Perfilação da Expressão Gênica , Humanos , Sistema de Sinalização das MAP Quinases , Neoplasias/genética , Neoplasias/metabolismoRESUMO
Organophosphorus (OP) compounds used as insecticides and chemical warfare agents are known to cause potent neurotoxic effects in humans and animals. Organophosphorus-induced delayed neuropathy (OPIDN) is currently thought to result from inhibition of neurotoxic esterase (NTE), but the actual molecular and cellular events leading to the development of OPIDN have not been characterized. This investigation examined the effects of OP compounds on the SY5Y human neuroblastoma cells at the cellular level to further characterize cellular targets of OP neurotoxicity. Mipafox and paraoxon were used as OP models that respectively do and do not induce OPIDN. Mipafox (0.05 mM) significantly decreased neurite length in SY5Y cells differentiated with nerve growth factor (NGF) while paraoxon at the same concentration had no effect when evaluated after each of three 4-day developmental windows during which cells were treated daily with OP or vehicle. In contrast, paraoxon but not mipafox altered intracellular calcium ion levels ([Ca(2+)](i)), as seen in three types of experiments. First, immediately following the addition of a single high concentration of OP to the culture, paraoxon caused a transient increase in [Ca(2+)](i), while mipafox up to 2 mM had no effect. Paraoxon hydrolysis products could also increase intracellular Ca(2+) levels, although the pattern of rise was different than it appeared immediately after paraoxon administration. Second, repeated low-level paraoxon treatment (0.05 mM/day for 4 days) decreased basal [Ca(2+)](i) in NGF-differentiated cells, though mipafox had no effect. Third, carbachol, a muscarinic acetylcholine receptor agonist, transiently increased [Ca(2+)](i) in differentiated cells, an affect attenuated by 4-day pretreatment with paraoxon (0.05 mM/day), but not by pretreatment with mipafox. These results indicate that the decrease in neurite extension that resulted from mipafox treatment was not caused by a disruption of Ca(2+) homeostasis. The effects of OPs that cause or do not cause OPIDN were clearly distinguishable, not only by their effects on neurite length, but also by their effects on Ca(2+) homeostasis in differentiated SY5Y cells.