Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

Acid–base homeostasis is a fundamental property of living cells,and its persistent disruption in human cells can lead to a wide range of diseases.In this study,we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas(TCGA)database.Built on our previous study,we quantitatively estimated the average production rate of OHby cytosolic Fenton reactions,which continuously disrupt the intracellular pH(pHi)homeostasis.Our predictions indicate that all or at least a subset of 43 reprogrammed metabolisms(RMs)are induced to produce net protons(H+)at comparable rates of Fenton reactions to keep the pHi stable.We then discovered that a number of wellknown phenotypes of cancers,including increased growth rate,metastasis rate,and local immune cell composition,can be naturally explained in terms of the Fenton reaction level and the induced RMs.This study strongly suggests the possibility to have a unified framework for studies of cancerinducing stressors,adaptive metabolic reprogramming,and cancerous behaviors.In addition,strong evidence is provided to demonstrate that a popular view that Na+/H+exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

Hawking radiation and page curves of the black holes in thermal environment

As realistic objects in the Universe,the black holes are surrounded by complex environment.By taking the effect of thermal environment into account,we investigate the evaporation process and the time evolutions(page curves)of the entanglement entropies of Hawking radiation of various types of black holes.It is found that the black holes with the thermal environments evaporate slower than those without the environments due to the environmental contribution of the energy flux in addition to Hawking radiation.For Schwarzschild black hole and Reissner-Nordstr?m black hole in flat spaces,when the initial temperature of the black hole is higher than the environment temperature,the black holes evaporate completely and the Hawking radiation is eventually purified.For Schwarzschild-Ad S black hole,it will evaporate completely and the Hawking radiation is purified when the environment temperature is lower than the critical temperature.Otherwise,it will reach an equilibrium state with the environment and the radiation is maximally entangled with the black hole.Our results indicate that the final state of the black hole is determined by the environmental temperature and the temporal evolution and the speed of the information purification process characterized by the page curve of the Hawking radiation is also influenced by the thermal environment significantly.

Data mining and mathematical models in cancer prognosis and prediction

Cancer is a fetal and complex disease.Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments.Pathological differences are reflected in tissues,cells and gene levels etc.The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis.It is a huge challenge to understand all of these mechanistically and quantitatively.Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications.With the rapidly growing and available computing powers,researchers begin to integrate huge data sets,multi-dimensional data types and information.The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators.For example,the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle.Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks,which are essential for cancer study when the information and gene regulations are clear and available.In this review,we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks(ANNs),Decision Trees(DTs),Support Vector Machine(SVM)and naive Bayes.Then we describe a few typical models for building up gene regulatory networks such as Correlation,Regression and Bayes methods based on available data.These methods can help on cancer diagnosis such as susceptibility,recurrence,survival etc.At last,we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks.These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.