Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

Simulating the life course of psoriasis patients: the interplay between therapy intervention and marital status.

BACKGROUND: Psoriasis, a chronic relapsing inflammatory disease affecting primarily the skin, shows multiple comorbidities including depression, cardiovascular diseases and other relevant conditions. Psoriasis patients experience social isolation, job loss, financial difficulties and partnership problems. Inversely, psychosocial impairments may negatively influence the disease course. OBJECTIVE: To explore the feasibility of a model describing the interaction of psychosocial and clinical factors over the life course of patients. METHODS: We considered only seven states for members of a hypothetical population: single and healthy, single and having a psoriasis flare, single and 'cured', coupled and healthy, coupled and having a psoriasis flare, coupled and 'cured', and dead. Transition probabilities between states were taken from the Norwegian Population Register for the healthy population and from epidemiological research articles. Clinical experience allowed adjustments on the assumed parameters. RESULTS: Our macromodel, which simulates the effect of therapy intervention on patients' partnership status, yields a description of the transitions between the seven states. Treatment efficacy shows only a negligible effect on the chances of living with a partner. CONCLUSIONS: Mathematical modelling of interactions between social and health variables is in principle feasible. However, complex models, comprising more variables (for instance: employment status, depression level, obesity etc.), are needed for more realistic simulations for the interactions studied. As increasing the number of variables leads to an exponential increase of the model's state space, switching to micromodelling (representing each individual separately) may be necessary.

Genetic variants affecting the neural processing of human facial expressions: evidence using a genome-wide functional imaging approach.

Human faces present crucial visual information for social interaction. Specialized brain regions are involved in the perception of faces, with the fusiform face area (FFA) a key neuronal substrate. Face processing is genetically controlled, but by which specific genes is unknown. A genome-wide approach identified common single nucleotide polymorphisms (SNPs) associated with areas of increased brain activity in response to affective facial expressions, measured with functional magnetic resonance imaging. SNPs in 20 genetic regions were linked with neural responses to negative facial expressions in a Norwegian sample (n=246), which included patients with mental illness. Three genetic regions were linked with FFA activation in a further discovery experiment using positive facial expressions and involving many of the same individuals (n=284). Two of these three regions showed significant association with right FFA activation to negative facial expressions in an independent North American replication sample of healthy Caucasians (n=85, 3q26.31, P=0.004; 20p12.3, P=0.045). The activation patterns were particularly striking for the SNP in 3q26.31, which lies in a gene TMEM212; only the FFA was activated. The specialized function of this brain region suggests that TMEM212 could contribute to the innate architecture of face processing.

Accounting for haplotype phase uncertainty in linkage disequilibrium estimation.

The characterization of linkage disequilibrium (LD) is applied in a variety of studies including the identification of molecular determinants of the local recombination rate, the migration and population history of populations, and the role of positive selection in adaptation. LD suffers from the phase uncertainty of the haplotypes used in its calculation, which reflects limitations of the algorithms used for haplotype estimation. We introduce a LD calculation method, which deals with phase uncertainty by weighting all possible haplotype pairs according to their estimated probabilities as evaluated by PHASE. In contrast to the expectation-maximization (EM) algorithm as implemented in the HAPLOVIEW and GENETICS packages, our method considers haplotypes based on the entire genetic information available for the candidate region. We tested the method using simulated and real genotyping data. The results show that, for all practical purposes, the new method is advantageous in comparison with algorithms that calculate LD using only the most probable haplotype or bilocus haplotypes based on the EM algorithm. The new method deals especially well with low LD regions, which contribute strongly to phase uncertainty. Altogether, the method is an attractive alternative to standard LD calculation procedures, including those based on the EM algorithm. We implemented the method in the software suite R, together with an interface to the popular haplotype calculation package PHASE.

Predicting survival from microarray data--a comparative study.

MOTIVATION: Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Cox's proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS: Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY: Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.

[Gastrointestinal endoscopy in children]. / Gastrointestinale endoskopier av barn.

BACKGROUND: Abdominal pain and other gastrointestinal symptoms in children may be, but are not always due to disease. Endoscopy in general anaesthesia may be necessary in children with long-term symptoms from the gastrointestinal tract. MATERIAL AND METHODS: We present a retrospective study of all children under the age of 15 who underwent endoscopy at the Central Hospital in Akershus (SIA) in the period 1993-98. 254 gastro-, 123 colono- and 43 sigmoideoscopies were performed. A conclusive diagnosis was made following endoscopy. There were few complications to the endoscopies. RESULTS AND INTERPRETATION: Our results indicate a higher incidence of inflammatory bowel disease than in similar studies, but with the same relative rates. Celiac disease occurs at a rate similar to the Swedish epidemic in the 1980s.

A stochastic model for neuronal bursting.

A new stochastic model for bursting of neuronal firing is proposed. It is based on stochastic diffusion and related to the first passage time problem. However, the model is not of renewal type. Its form and parameters are physiologically interpretable. Parametric and non-parametric inferential issues are discussed.

A stochastic model for the membrane potential of a stimulated neuron.

We present a simple model describing the transition between the prefiring, firing and postfiring phases of a single neuron in a large neural net. Using typical values for the physiological parameters that enter the model, we find average interspike times that are close to those reported in experimental measurements.