Analysis of intercellular signal transduction in the tumor microenvironment

A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint

We perform logic-based network analysis on a model of the mammalian cell cycle. This model is composed of a Restriction Switch driving cell cycle commitment and a Phase Switch driving mitotic entry and exit. By generalizing the concept of stable motif, i.e., a self-sustaining positive feedback loop that maintains an associated state, we introduce the concept of a conditionally stable motif, the stability of which is contingent on external conditions. We show that the stable motifs of the Phase Switch are contingent on the state of three nodes through which it receives input from the rest of the network. Biologically, these conditions correspond to cell cycle checkpoints. Holding these nodes locked (akin to a checkpoint-free cell) transforms the Phase Switch into an autonomous oscillator that robustly toggles through the cell cycle phases G1, G2 and mitosis. The conditionally stable motifs of the Phase Switch Oscillator are organized into an ordered sequence, such that they serially stabilize each other but also cause their own destabilization. Along the way they channel the dynamics of the module onto a narrow path in state space, lending robustness to the oscillation. Self-destabilizing conditionally stable motifs suggest a general negative feedback mechanism leading to sustained oscillations.

Boolean model of growth signaling, cell cycle and apoptosis predicts the molecular mechanism of aberrant cell cycle progression driven by hyperactive PI3K

The PI3K/AKT signaling pathway plays a role in most cellular functions linked to cancer progression, including cell growth, proliferation, cell survival, tissue invasion and angiogenesis. It is generally recognized that hyperactive PI3K/AKT1 are oncogenic due to their boost to cell survival, cell cycle entry and growth-promoting metabolism. That said, the dynamics of PI3K and AKT1 during cell cycle progression are highly nonlinear. In addition to negative feedback that curtails their activity, protein expression of PI3K subunits has been shown to oscillate in dividing cells. The low-PI3K/low-AKT1 phase of these oscillations is required for cytokinesis, indicating that oncogenic PI3K may directly contribute to genome duplication. To explore this, we construct a Boolean model of growth factor signaling that can reproduce PI3K oscillations and link them to cell cycle progression and apoptosis. The resulting modular model reproduces hyperactive PI3K-driven cytokinesis failure and genome duplication and predicts the molecular drivers responsible for these failures by linking hyperactive PI3K to mis-regulation of Polo-like kinase 1 (Plk1) expression late in G2. To do this, our model captures the role of Plk1 in cell cycle progression and accurately reproduces multiple effects of its loss: G2 arrest, mitotic catastrophe, chromosome mis-segregation / aneuploidy due to premature anaphase, and cytokinesis failure leading to genome duplication, depending on the timing of Plk1 inhibition along the cell cycle. Finally, we offer testable predictions on the molecular drivers of PI3K oscillations, the timing of these oscillations with respect to division, and the role of altered Plk1 and FoxO activity in genome-level defects caused by hyperactive PI3K. Our model is an important starting point for the predictive modeling of cell fate decisions that include AKT1-driven senescence, as well as the non-intuitive effects of drugs that interfere with mitosis.

Expression pattern of RAGE and IGF-1 in the human fetal ovary and ovarian serous carcinoma

The expression pattern of RAGE and IGF-1 proteins in different ovarian cell lineages was histologically analyzed in six fetal, nine adult human ovaries, and nine serous ovarian carcinomas (OSC) using immunohistochemical methods. Mild expression of IGF-1 in ovarian surface epithelium (Ose) and oocytes in the 15-week human ovaries increased to moderate or strong in the stromal cells, oocytes and follicular cells in week 22. Occasional mild RAGE expression was observed in Ose during week 15, while strong expression characterized primordial follicles in week 22. In the reproductive human ovary, IGF-1 was mildly to moderately expressed in all ovarian cell lineages except in theca cells of the tertiary follicle where IGF-1 was negative. RAGE was strongly positive in the granulosa cells and some theca cells of the tertiary follicle, while negative to mildly positive in all cells of the secondary follicle. In the postmenopausal human ovary IGF-1 and RAGE were mildly expressed in Ose and stroma. In OSC, cells were strongly positive to IGF-1 and RAGE, except for some negative stromal cells. Different levels of IGF-1 and RAGE co-expression characterized fetal ovarian cells during development. In reproductive ovaries, IGF-1 and RAGE were co-localized in the granulosa and theca interna cells of tertiary follicles, while in postmenopausal ovaries and OSC, IGF-1 and RAGE were co-localized in Ose and OSC cells respectively. Our results indicate that intracellular levels of IGF-1 and RAGE protein might regulate the final destiny of the ovarian cell populations prior and during folliculogenesis, possibly controlling the metastatic potential of OSC as well.

A novel procedure for statistical inference and verification of gene regulatory subnetwork

Background

The reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.

Results

In this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data. The dynamic Bayesian network, a statistical inference algorithm, is at first implemented to infer an optimal network from time series microarray data of S. cerevisiae, then, a weighted symbolic model checker is applied to automatically verify or falsify the inferred network through checking some desired temporal logic formulas abstracted from experiments or public database.

Conclusions

Our studies show that the marriage of statistical inference algorithm with model checking technique provides a more efficient way to automatically infer and verify the gene regulatory network from time series expression data than previous studies.

Computational analysis of the roles of ER-Golgi network in the cell cycle

BACKGROUND

ER-Golgi network plays an important role in the processing, sorting and transport of proteins, and it's also a site for many signaling pathways that regulate the cell cycle. Accumulating evidence suggests that, the stressed ER and malfunction of Golgi apparatus are associated with the pathogenesis of cancer and Alzheimer's disease (AD). Our previous work discovered and verified that altering the expression levels of target SNARE and GEF could modulate the size of Golgi apparatus. Moreover, Golgi's structure and size undergo dramatic changes during the development of several diseases. It is of importance to investigate the roles of ER-Golgi network in the cell cycle progression and some diseases.

RESULTS

In this work, we first develop a computational model to study the ER stress-induced and Golgi-related apoptosis-survival signaling pathways. Then, we propose and apply both asynchronous and synchronous model checking methods, which extend our previous verification technique, to automatically and formally analyze the ER-Golgi-regulated signaling pathways in the cell cycle progression through verifying some computation tree temporal logic formulas.

CONCLUSIONS

The proposed asynchronous and synchronous verification technique has advantages for large network analysis and verification over traditional simulation methods. Using the model checking method, we verified several Alzheimer's disease and cancer-related properties, and also identified important proteins (NFκB, ATF4, ASK1 and TRAF2) in the ER-Golgi network, which might be responsible for the pathogenesis of cancer and AD. Our studies indicate that targeting the ER stress-induced and Golgi-related pathways might serve as potent therapeutic targets for the treatment of cancer and Alzheimer's disease.

InCoB2013 introduces Systems Biology as a major conference theme

The Asia-Pacific Bioinformatics Network (APBioNet) held the first International Conference on Bioinformatics (InCoB) in Bangkok in 2002 to promote North-South networking. Commencing as a forum for Asia-Pacific researchers to interact with and learn from with scientists of developed countries, InCoB has become a major regional bioinformatics conference, with participants from the region as well as North America and Europe. Since 2006, InCoB has selected the best submissions for publication in BMC Bioinformatics. In response to the growth and maturation of data-driven approaches, InCoB added BMC Genomics in 2009 and with the introduction of this conference supplement, BMC Systems Biology to its journal choices for submitting authors. Co-hosting InCoB2013 with the second International Conference for Translational Bioinformatics (ICTBI) is in line with InCoB's support for the current trend in taking bioinformatics to the bedside, along with a systems approach to solving biological problems.