Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. RESULTS: In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. CONCLUSION: Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

MicroRNA-based regulatory circuit involved in sperm infertility.

miRNAs (MicroRNAs), known as noncoding and important endogenous factors regulating the expression protein-coding genes, are vital regulators in each biological process. Thus, this study aims to explore the key role of four microRNAs in regulating the spermatogenesis. To conduct this experiment, 55 infertile and fertile men provided the study with the sperm and testicular tissue samples. To study the spermatozoa in terms of the morphology, Diff-Quick was applied. Then, quantitative real-time polymerase chain reaction (RT-PCR) was conducted on samples. Our data indicated that in contrast to the miR-15b, significant increasing of miR-383 and miR-122 occurred in both severe oligoasthenoteratozoospermia (SOAT) and moderate oligoasthenoteratozoospermia (MOAT) compared to normal sperm group (N). In addition, it was observed that miR-15b and miR-122 increased in patients with nonobstructive azoospermia (NOA) compared with obstructive azoospermia (OA) group. Expression levels of target genes including P53, CASPASE-9 and CYCLIN D1 underwent principle changes according to miRNAs expression level. Our finding indicated that miRNAs had essential role in the regulation of spermatogenesis, and their expression altering was associated with sperm abnormalities. Thus, microRNAs can be introduced as useful biomarkers to determine male infertility reasons to choose the effective treatment.

Influence of cryopreservation on structure and function of mammalian spermatozoa: an overview.

Cryopreservation is a useful approach to preserve male fertility for assisted reproduction technique and other evaluations. In spite of extensive development in the cryopreservation field, there are biological and biochemical points including DNA fragmentation and antioxidant which should be illuminated to preserve fertility in safe form. Several molecular markers such as coding and noncoding RNAs are transferred from spermatozoa into oocyte via fertilization. These biomarkers affect fertility potential during the cryopreservation. Despite its impact, sperm cryopreservation has some destructive effect including loss of sperm motility and viability, acrosomal damage, mitochondrial membrane depolarization, plasma membrane permeability alteration even nuclear, and DNA damage. There is a growing concern about the impact of sperm cryopreservation on biological factors which can interrupt successful fertility procedures such as in vitro fertilization. Additionally, cryo-damage can decrease embryonic development extent. Promoting cryopreservation method via investigating the wide range of non-invasive factors may be increasingly important to have access to safe approach of freezing. Therefore, the aim of this study is the assessment of biological factors which can penetrate the fertility potential during the freezing procedure, explicate innovative methods in fertility preservation.

Simulating ATO Mechanism and EGFR Signaling with Fuzzy Logic and Petri Net.

BACKGROUND: Interactions of many key proteins or genes in signalling pathway have been studied qualitatively in the literature, but only little quantitative information is available. OBJECTIVE: Although much has been done to clarify the biochemistry of transcriptional dynamics in signalling pathway, it remains difficult to find out and predict quantitative responses. The aim of this study is to construct a computational model of epidermal growth factor receptor (EGFR) signalling pathway as one of hallmarks of cancer so as to predict quantitative responses. MATERIAL AND METHODS: In this analytical study, we presented a computational model to investigate EGFR signalling pathway. Interaction of Arsenic trioxide (ATO) with EGFR signalling pathway factors has been elicited by systematic search in data bases, as ATO is one of the mysterious chemotherapy agents that control EGFR expression in cancer. ATO has dichotomous manner in vivo, dependent on its concentration. According to fuzzy rules based upon qualitative knowledge and Petri Net, we can construct a quantitative model to describe ATO mechanism in EGFR signalling pathway. RESULTS: By Fuzzy Logic models that have the potential to trade with the loss of quantitative information on how different species interact, along with Petri net quantitatively describe the dynamics of EGFR signalling pathway. By this model the dynamic of different factors in EGFR signalling pathway is achieved. CONCLUSION: The use of Fuzzy Logic and PNs in biological network modelling causes a deeper understanding and comprehensive analysis of the biological networks.

Extended Robust Boolean Network of Budding Yeast Cell Cycle.

BACKGROUND: How to explore the dynamics of transition probabilities between phases of budding yeast cell cycle (BYCC) network based on the dynamics of protein activities that control this network? How to identify the robust structure of protein interactions of BYCC Boolean network (BN)? Budding yeast allows scientists to put experiments into effect in order to discover the intracellular cell cycle regulating structures which are well simulated by mathematical modeling. METHODS: We extended an available deterministic BN of proteins responsible for the cell cycle to a Markov chain model containing apoptosis besides G1, S, G2, M, and stationary G1. Using genetic algorithm (GA), we estimated the kinetic parameters of the extended BN model so that the subsequent transition probabilities derived using Markov chain model of cell states as normal cell cycle becomes the maximum while the structure of chemical interactions of extended BN of cell cycle becomes more stable. RESULTS: Using kinetic parameters optimized by GA, the probability of the subsequent transitions between cell cycle phases is maximized. The relative basin size of stationary G1 increased from 86% to 96.48% while the number of attractors decreased from 7 in the original model to 5 in the extended one. Hence, an increase in the robustness of the system has been achieved. CONCLUSION: The structure of interacting proteins in cell cycle network affects its robustness and probabilities of transitions between different cell cycle phases. Markov chain and BN are good approaches to study the stability and dynamics of the cell cycle network.