Stochastic cell fate and longevity of offspring

Objective: Cellular decision-making is a key process in which cells with similar genetic and environmental background make dissimilar decisions. This stochastic process, which happens in prokaryotic and eukaryotic cells including stem cells, causes cellular diversity and phenotypic variation. In addition, fitness predicts and describes changes in the genetic composition of populations throughout the evolutionary history. Fitness may thus be defined as the ability to adapt and produce surviving offspring. Here, we present a mathematical model to predict the fitness of a cell and to address the fundamental issue of phenotypic variation. We study a basic decision-making scenario where a bacteriophage lambda reproduces in E. coli, using both the lytic and the lysogenic pathways. In the lytic pathway, the bacteriophage replicates itself within the host bacterium. This fast replication overcrowds and in turn destroys the host bacterium. In the lysogenic pathway, however, the bacteriophage inserts its DNA into the host genome, and is replicated simultaneously with the host genome

Materials and Methods: In this prospective study, a mathematical predictive model was developed to estimate fitness as an index of survived offspring. We then leverage experimental data to validate the predictive power of our proposed model. A mathematical model based on game theory was also generated to elucidate a rationale behind cell decision

Results: Our findings indicate that a rational decision that is aimed to maximize life expectancy of offspring is almost identical to bacteriophage behavior reported based on experimental data. The results also showed that stochastic decision on cell fate maximizes the expected number of survived offspring

Conclusion: We present a mathematical framework for analyzing a basic phenotypic variation problem and explain how bacteriophages maximize offspring longevity based on this model. We also introduce a mathematical benchmark for other investigations of phenotypic variation that exists in eukaryotes including stem cell differentiation

[ effects of temperature and birth weight on the transition rate of hypothermia in hospitalized neonates using Markov models]

Hypothermia is an important determinant of survival in newborns, especially among low-birth-weight ones. Prolonged hypothermia leads to edema, generalized hemorrhage, jaundice and ultimately death. This study was undertaken to examine the factors affecting transition from hypothermic state in neonates. The study consisted of 439 neonates hospitalized in NICU of Valiasr in Tehran, Iran in 2005. The neonates' rectal temperature was measured immediately after birth and every 30 minutes afterwards, until neonates passed hypothermia stages. In order to estimate the rate of transition from neonatal hypothermic state, we used multistate Markov models with two covariates, birth weight and environmental temperature. We also used R package to fit the model. Estimated transition rates from severe hypothermia and mild hypothermia were 0.1192 and 0.0549 per minute, respectively. Weight had a significant effect on transition from hypothermia to normal condition [95% CI: 0.1364-0.4165, P<0.001]. Environmental temperature significantly affected the transition from hypothermia to normal stage [95% CI: 0.0439-0.4963, P<0.001]. The results of this study showed that neonates with normal weight and neonates in an environmental temperature greater than 28°C had a higher transition rate from hypothermia stages. Since birth weight at the time of delivery is not under the control of medical staff, keeping the environmental temperature in an optimum level could help neonates to pass through the hypothermia stages faster

Haplotype block partitioning and tag SNP selection under the perfect phylogeny model

Single Nucleotide Polymorphisms [SNPs] are the most usual form of polymorphism in human genome. Analyses of genetic variations have revealed that individual genomes share common SNP-haplotypes. The particular pattern of these common variations forms a block-like structure on human genome. In this work, we develop a new method based on the Perfect Phylogeny Model to identify haplotype blocks using samples of individual genomes. We introduce a rigorous definition of the quality of the partitioning of haplotypes into blocks and devise a greedy algorithm for finding the proper partitioning in case of perfect and semi-perfect phylogeny. It is shown that the minimum number of tagSNPs in a haplotype block of Perfect Phylogeny can be obtained by a polynomial time algorithm. We compare the performance of our algorithm on haplotype data of human chromosome 21 with other previously developed methods through simulations. The results demonstrate that our algorithm outperforms the conventional implementation of the Four Gamete Test approach which is the only available method for haplotype block partitioning based on Perfect Phylogeny