scholarly journals An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-14
Author(s):  
Michael Gormley ◽  
Viswanadha U. Akella ◽  
Judy N. Quong ◽  
Andrew A. Quong
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Gene Networks ◽  
Computational Models ◽  
Simulation Analysis ◽  
Network Models ◽  
Biochemical Networks ◽  
Phenotypic Data ◽  
Model Cell ◽  
Systematic Identification

Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

scholarly journals Negative energy balance and hepatic gene expression patterns in high-yielding dairy cows during the early postpartum period: a global approach

2010 ◽  
Vol 42A (3) ◽  
pp. 188-199 ◽  
Author(s):  
S. D. McCarthy ◽  
S. M. Waters ◽  
D. A. Kenny ◽  
M. G. Diskin ◽  
R. Fitzpatrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Energy Balance ◽  
Dairy Cows ◽  
Postpartum Period ◽  
Gene Networks ◽  
Negative Energy ◽  
Hepatic Gene Expression ◽  
Early Postpartum Period ◽  
Early Postpartum

In high-yielding dairy cows the liver undergoes extensive physiological and biochemical changes during the early postpartum period in an effort to re-establish metabolic homeostasis and to counteract the adverse effects of negative energy balance (NEB). These adaptations are likely to be mediated by significant alterations in hepatic gene expression. To gain new insights into these events an energy balance model was created using differential feeding and milking regimes to produce two groups of cows with either a mild (MNEB) or severe NEB (SNEB) status. Cows were slaughtered and liver tissues collected on days 6–7 of the first follicular wave postpartum. Using an Affymetrix 23k oligonucleotide bovine array to determine global gene expression in hepatic tissue of these cows, we found a total of 416 genes (189 up- and 227 downregulated) to be altered by SNEB. Network analysis using Ingenuity Pathway Analysis revealed that SNEB was associated with widespread changes in gene expression classified into 36 gene networks including those associated with lipid metabolism, connective tissue development and function, cell signaling, cell cycle, and metabolic diseases, the three most significant of which are discussed in detail. SNEB cows displayed reduced expression of transcription activators and signal transducers that regulate the expression of genes and gene networks associated with cell signaling and tissue repair. These alterations are linked with increased expression of abnormal cell cycle and cellular proliferation associated pathways. This study provides new information and insights on the effect of SNEB on gene expression in high-yielding Holstein Friesian dairy cows in the early postpartum period.

scholarly journals Digital clocks: simple Boolean models can quantitatively describe circadian systems

2012 ◽  
Vol 9 (74) ◽  
pp. 2365-2382 ◽  
Author(s):  
Ozgur E. Akman ◽  
Steven Watterson ◽  
Andrew Parton ◽  
Nigel Binns ◽  
Andrew J. Millar ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Computational Models ◽  
Molecular Data ◽  
Biochemical Networks ◽  
Boolean Logic ◽  
Adaptive Behaviour ◽  
Boolean Models ◽  
Endogenous Rhythms ◽  
Logic Models

The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day–night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.

scholarly journals Systematic Verification of Upstream Regulators of a Computable Cellular Proliferation Network Model on Non-Diseased Lung Cells Using a Dedicated Dataset

2013 ◽  
Vol 7 ◽  
pp. BBI.S12167 ◽  
Author(s):  
Vincenzo Belcastro ◽  
Carine Poussin ◽  
Stephan Gebel ◽  
Carole Mathis ◽  
Walter K. Schlage ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Causal Reasoning ◽  
Cellular Proliferation ◽  
Statistical Significance ◽  
Network Models ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Response Specificity

We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.

scholarly journals Population growth affects intrinsic and extrinsic noise in gene expression

2018 ◽  
Author(s):  
Philipp Thomas
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Intrinsic Noise ◽  
Integrated Approach ◽  
Time Lapse ◽  
Biochemical Networks ◽  
Intrinsic Variability ◽  
Extrinsic Noise ◽  
Growing Population ◽  
Intrinsic And Extrinsic Noise

Clonal cells of exponentially growing populations vary substantially from cell to cell. The main drivers of this heterogeneity are the population dynamics and stochasticity in the intracellular reactions, which are commonly studied separately. Here we develop an agent-based framework that allows tracking of the biochemical dynamics in every single cell of a growing population that accounts for both of these factors. Apart from the common intrinsic variability of the biochemical reactions, the framework also predicts extrinsic noise arising from fluctuations in the histories of cells without the need to introduce fluctuating rate constants. Instead, these extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age, which are ubiquitously observed in growing populations. We give explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics as measured in two-colour experiments. We find that these statistics may differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) snapshots of a growing population with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots of unknown cell ages as measured from static images. Our integrated approach applies to arbitrary biochemical networks and generation time distributions. By employing models of stochastic gene expression and feedback regulation, we elucidate that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

Ethanol alters cell cycle gene expression in human embryonic stem cells

2016 ◽  
Vol 01 (03) ◽  
pp. 201-208 ◽  
Author(s):  
Malini Krishnamoorthy ◽  
Brian Gerwe ◽  
Jamie Heimburg-Molinaro ◽  
Rachel Nash ◽  
Jagan Arumugham ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Embryonic Stem ◽  
Cell Cycle Gene ◽  
Cell Cycle Gene Expression

Reverse Screening Bioinformatics Approach to Identify Potential Anti Breast Cancer Targets Using Thymoquinone from Neutraceuticals Black Cumin Oil

2019 ◽  
Vol 19 (5) ◽  
pp. 599-609 ◽  
Author(s):  
Sumathi Sundaravadivelu ◽  
Sonia K. Raj ◽  
Banupriya S. Kumar ◽  
Poornima Arumugamand ◽  
Padma P. Ragunathan
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Cycle ◽  
Cell Death ◽  
In Silico ◽  
Chemotherapeutic Agents ◽  
Normal Cells ◽  
Black Cumin ◽  
In Silico Docking ◽  
Wide Range

Background: Functional foods, neutraceuticals and natural antioxidants have established their potential roles in the protection of human health and diseases. Thymoquinone (TQ), the main bioactive component of Nigella sativa seeds (black cumin seeds), a plant derived neutraceutical was used by ancient Egyptians because of their ability to cure a variety of health conditions and used as a dietary food supplement. Owing to its multi targeting nature, TQ interferes with a wide range of tumorigenic processes and counteracts carcinogenesis, malignant growth, invasion, migration, and angiogenesis. Additionally, TQ can specifically sensitize tumor cells towards conventional cancer treatments (e.g., radiotherapy, chemotherapy, and immunotherapy) and simultaneously minimize therapy-associated toxic effects in normal cells besides being cost effective and safe. TQ was found to play a protective role when given along with chemotherapeutic agents to normal cells. Methods: In the present study, reverse in silico docking approach was used to search for potential molecular targets for cancer therapy. Various metastatic and apoptotic targets were docked with the target ligand. TQ was also tested for its anticancer activities for its ability to cause cell death, arrest cell cycle and ability to inhibit PARP gene expression. Results: In silico docking studies showed that TQ effectively docked metastatic targets MMPs and other apoptotic and cell proliferation targets EGFR. They were able to bring about cell death mediated by apoptosis, cell cycle arrest in the late apoptotic stage and induce DNA damage too. TQ effectively down regulated PARP gene expression which can lead to enhanced cancer cell death. Conclusion: Thymoquinone a neutraceutical can be employed as a new therapeutic agent to target triple negative breast cancer which is otherwise difficult to treat as there are no receptors on them. Can be employed along with standard chemotherapeutic drugs to treat breast cancer as a combinatorial therapy.

Sign in / Sign up

Export Citation Format

Share Document