Synergistic and antagonistic effects of deterministic and stochastic cell-cell variations.

By diversifying, cells in a clonal population can together overcome the limits of individuals. Diversity in single-cell growth rates allows the population to survive environmental stresses, such as antibiotics, and grow faster than the undiversified population. These functional cell-cell variations can arise stochastically, from noise in biochemical reactions, or deterministically, by asymmetrically distributing damaged components. While each of the mechanisms is well understood, the effect of the combined mechanisms is unclear. To evaluate the contribution of the deterministic component we developed a mathematical model by mapping the growing population to the Ising model. To analyze the combined effects of stochastic and deterministic contributions we introduced the analytical results of the Ising-mapping into an Euler-Lotka framework. Model results, confirmed by simulations and experimental data, show that deterministic cell-cell variations increase near-linearly with stress. As a consequence, we predict that the gain in population doubling time from cell-cell variations is primarily stochastic at low stress but may cross over to deterministic at higher stresses. Furthermore, we find that while the deterministic component minimizes population damage, stochastic variations antagonize this effect. Together our results may help identifying stress-tolerant pathogenic cells and thus inspire novel antibiotic strategies.

Algorithmic prediction of HIV status using nation-wide electronic registry data.

BACKGROUND: Late HIV diagnosis is detrimental both to the individual and to society. Strategies to improve early diagnosis of HIV must be a key health care priority. We examined whether nation-wide electronic registry data could be used to predict HIV status using machine learning algorithms. METHODS: We extracted individual level data from Danish registries and used algorithms to predict HIV status. We used various algorithms to train prediction models and validated these models. We calibrated the models to mimic different clinical scenarios and created confusion matrices based on the calibrated models. FINDINGS: A total 4,384,178 individuals, including 4,350 with incident HIV, were included in the analyses. The full model that included all variables that included demographic variables and information on past medical history had the highest area under the receiver operating characteristics curves of 88·4% (95%CI: 87·5% - 89·4%) in the validation dataset. Performance measures did not differ substantially with regards to which machine learning algorithm was used. When we calibrated the models to a specificity of 99·9% (pre-exposure prophylaxis (PrEP) scenario), we found a positive predictive value (PPV) of 8·3% in the full model. When we calibrated the models to a sensitivity of 90% (screening scenario), 384 individuals would have to be tested to find one undiagnosed person with HIV. INTERPRETATION: Machine learning algorithms can learn from electronic registry data and help to predict HIV status with a fairly high level of accuracy. Integration of prediction models into clinical software systems may complement existing strategies such as indicator condition-guided HIV testing and prove useful for identifying individuals suitable for PrEP. FUNDING: The study was supported by funds from the Preben and Anne Simonsens Foundation, the Novo Nordisk Foundation, Rigshospitalet, Copenhagen University, the Danish AIDS Foundation, the Augustinus Foundation and the Danish Health Foundation.

Asymmetric Damage Segregation Constitutes an Emergent Population-Level Stress Response.

Asymmetric damage segregation (ADS) is a mechanism for increasing population fitness through non-random, asymmetric partitioning of damaged macromolecules at cell division. ADS has been reported across multiple organisms, though the measured effects on fitness of individuals are often small. Here, we introduce a cell-lineage-based framework that quantifies the population-wide effects of ADS and then verify our results experimentally in E. coli under heat and antibiotic stress. Using an experimentally validated mathematical model, we find that the beneficial effect of ADS increases with stress. In effect, low-damage subpopulations divide faster and amplify within the population acting like a positive feedback loop whose strength scales with stress. Analysis of protein aggregates shows that the degree of asymmetric inheritance is damage dependent in single cells. Together our results indicate that, despite small effects in single cell, ADS exerts a strong beneficial effect on the population level and arises from the redistribution of damage within a population, through both single-cell and population-level feedback.

The role of paracrine and autocrine signaling in the early phase of adipogenic differentiation of adipose-derived stem cells.

INTRODUCTION: High cell density is known to enhance adipogenic differentiation of mesenchymal stem cells, suggesting secretion of signaling factors or cell-contact-mediated signaling. By employing microfluidic biochip technology, we have been able to separate these two processes and study the secretion pathways. METHODS AND RESULTS: Adipogenic differentiation of human adipose-derived stem cells (ASCs) cultured in a microfluidic system was investigated under perfusion conditions with an adipogenic medium or an adipogenic medium supplemented with supernatant from differentiating ASCs (conditioned medium). Conditioned medium increased adipogenic differentiation compared to adipogenic medium with respect to accumulation of lipid-filled vacuoles and gene expression of key adipogenic markers (C/EBPα, C/EBPß, C/EBPδ, PPARγ, LPL and adiponectin). The positive effects of conditioned medium were observed early in the differentiation process. CONCLUSIONS: Using different cell densities and microfluidic perfusion cell cultures to suppress the effects of cell-released factors, we have demonstrated the significant role played by auto- or paracrine signaling in adipocyte differentiation. The cell-released factor(s) were shown to act in the recruitment phase of the differentiation process.

Migration of cells in a social context.

In multicellular organisms and complex ecosystems, cells migrate in a social context. Whereas this is essential for the basic processes of life, the influence of neighboring cells on the individual remains poorly understood. Previous work on isolated cells has observed a stereotypical migratory behavior characterized by short-time directional persistence with long-time random movement. We discovered a much richer dynamic in the social context, with significant variations in directionality, displacement, and speed, which are all modulated by local cell density. We developed a mathematical model based on the experimentally identified "cellular traffic rules" and basic physics that revealed that these emergent behaviors are caused by the interplay of single-cell properties and intercellular interactions, the latter being dominated by a pseudopod formation bias mediated by secreted chemicals and pseudopod collapse following collisions. The model demonstrates how aspects of complex biology can be explained by simple rules of physics and constitutes a rapid test bed for future studies of collective migration of individual cells.

Development of a theoretical framework for analyzing cerebrospinal fluid dynamics.

BACKGROUND: To date hydrocephalus researchers acknowledge the need for rigorous but utilitarian fluid mechanics understanding and methodologies in studying normal and hydrocephalic intracranial dynamics. Pressure volume models and electric circuit analogs introduced pressure into volume conservation; but control volume analysis enforces independent conditions on pressure and volume. Previously, utilization of clinical measurements has been limited to understanding of the relative amplitude and timing of flow, volume and pressure waveforms; qualitative approaches without a clear framework for meaningful quantitative comparison. METHODS: Control volume analysis is presented to introduce the reader to the theoretical background of this foundational fluid mechanics technique for application to general control volumes. This approach is able to directly incorporate the diverse measurements obtained by clinicians to better elucidate intracranial dynamics and progression to disorder. RESULTS: Several examples of meaningful intracranial control volumes and the particular measurement sets needed for the analysis are discussed. CONCLUSION: Control volume analysis provides a framework to guide the type and location of measurements and also a way to interpret the resulting data within a fundamental fluid physics analysis.