Biology subjects

Baker, R.

Publications and source records attributed to Baker, R..

6 recordsLinked to original sources

bioRxiv2018

Using experimental data and information criteria to guide model selection for reaction--diffusion problems in mathematical biology

Reaction-diffusion models describing the movement, reproduction and death of individuals within a population are key mathematical modelling tools with widespread applications in mathematical biology. A diverse range of such continuum models have been applied in various biological contexts by choosing different flux and source terms in the reaction-diffusion framework. For example, to describe collective spreading of cell populations, the flux term may be chosen to reflect various movement mechanisms, such as random motion (diffusion), adhesion, haptotaxis, chemokinesis and chemotaxis. The choice of flux terms in specific applications, such as wound healing, is usually made heuristically, and rarely is it tested quantitatively against detailed cell density data. More generally, in mathematical biology, the questions of model validation and model selection have not received the same attention as the questions of model development and model analysis. Many studies do not consider model validation or model selection, and those that do often base the selection of the model on residual error criteria after model calibration is performed using nonlinear regression techniques. In this work, we present a model selection case study, in the context of cell invasion, with a very detailed experimental data set. Using Bayesian analysis and information criteria, we demonstrate that model selection and model validation should account for both residual errors and model complexity. These considerations are often overlooked in the mathematical biology literature. The results we present here provide a clear methodology that can be used to guide model selection across a range of applications. Furthermore, the case study we present provides a clear example where neglecting the role of model complexity can give rise to misleading outcomes.

biophysics↗

bioRxiv2018

Mechanistic and experimental models of cell migration reveal the importance of intercellular interactions in cell invasion

Moving fronts of cells are essential for development, repair and disease progression. Therefore, understanding and quantifying the details of the mechanisms that drive the movement of cell fronts is of wide interest. Quantitatively identifying the role of intercellular interactions, and in particular the role of cell pushing, remains an open question. Indeed, perhaps the most common continuum mathematical idealization of moving cell fronts is to treat the population of cells, either implicitly or explicitly, as a population of point particles undergoing a random walk that neglects intercellular interactions. In this work, we report a combined experimental-modelling approach showing that intercellular interactions contribute significantly to the spatial spreading of a population of cells. We use a novel experimental data set with PC-3 prostate cancer cells that have been pretreated with Mitomycin-C to suppress proliferation. This allows us to experimentally separate the effects of cell migration from cell proliferation, thereby enabling us to focus on the migration process in detail as the population of cells recolonizes an initially-vacant region in a series of two-dimensional experiments. We quantitatively model the experiments using a stochastic modelling framework, based on Langevin dynamics, which explicitly incorporates random motility and various intercellular forces including: (i) long range attraction (adhesion); and (ii) finite size effects that drive short range repulsion (pushing). Quantitatively comparing the ability of this model to describe the experimentally observed population-level behaviour provides us with quantitative insight into the roles of random motility and intercellular interactions. To quantify the mechanisms at play, we calibrate the stochastic model to match experimental cell density profiles to obtain estimates of cell diffusivity, D, and the amplitude of intercellular forces, f0. Our analysis shows that taking a standard modelling approach which ignores intercellular forces provides a poor match to the experimental data whereas incorporating intercellular forces, including short-range pushing and longer range attraction, leads to a faithful representation of the experimental observations. These results demonstrate a significant role for intercellular interactions in cell invasion.\n\nAuthor summaryMoving cell fronts are routinely observed in various physiological processes, such as wound healing, malignant invasion and embryonic morphogenesis. We explore the effects of a previously overlooked mechanism that contributes to population-level front movement: pushing. Our framework is flexible and incorporates range of reasonable biological phenomena, such as random motility, cell-to-cell adhesion, and pushing. We find that neglecting finite size effects and intercellular forces, such as cell pushing, reduces our ability to mimic and predict our experimental observations.

biophysics↗

bioRxiv2018

Strain-level overlap between infant and hospital fungal microbiomes revealed through de novo assembly of eukaryotic genomes from metagenomes

Eukaryotes are a leading cause of nosocomial infections in neonates, but their diversity and population heterogeneity are rarely investigated. This has led to an incomplete understanding of eukaryotic strains that colonize infants and of the neonatal intensive care unit (NICU) as a possible source of these strains. Analysis of 1,174 time-series metagenomes from 161 premature infants revealed fungal colonization of 13 infants, primarily in the first two weeks of life. Nearly all 24 NICU samples contained eukaryotes, and the most diverse communities were in NICU sinks. Five of fourteen newly-assembled eukaryotic genomes derived from genomically undescribed species. Purpureocillium lilacinum genomes from infant and NICU samples shared 99.999% average nucleotide identity, highlighting the potential of hospital-associated fungi to colonize hospitalized infants. We quantified zygosity and within-population variation associated with the diploid eukaryotes, and thus defined the genetic reservoirs of eukaryotes in room environments and infants.

microbiology↗

bioRxiv2018

The developing premature infant gut microbiome is a major factor shaping the microbiome of neonatal intensive care unit rooms

1.1BackgroundThe neonatal intensive care unit (NICU) contains a unique cohort of patients with underdeveloped immune systems and nascent microbiome communities. Patients often spend several months in the same room and it has been previously shown that the gut microbiomes of these infants often resemble the microbes found in the NICU. Little is known, however, about the identity, persistence and absolute abundance of NICU room-associated bacteria over long stretches of time. Here we couple droplet digital PCR (ddPCR), 16S rRNA gene surveys, and recently published metagenomics data from infant gut samples to infer the extent to which the NICU microbiome is shaped by its room occupants.\n\nResultsOver 2,832 swabs, wipes, and air samples were collected from sixteen private-style NICU rooms housing very low birthweight (<1,500 g), premature (<31 weeks gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for one month. The first samples from the first infant and last samples from the last infant were collected 383 days apart. Twenty-two NICU locations spanning room surfaces, hands, electronics, sink basins, and air were collected. Results point to an incredibly simple room community where 5-10 taxa, mostly skin associated, account for over 50% of 16S reads. Biomass estimates reveal 4-5 orders of magnitude difference between the least to the most dense microbial communities, air and sink basins, respectively. Biomass trends from bioaerosol samples and petri dish dust collectors suggest occupancy to be a main driver of suspended biological particles within the NICU. Using a machine learning algorithm to classify the origin of room samples, we show that each room has a unique microbial fingerprint. Several important taxa driving this model were dominant gut colonizers of infants housed within each room.\n\nConclusionsDespite regular cleaning of hospital surfaces, bacterial biomass was detectable at varying densities. A room specific microbiome signature was detected, suggesting microbes seeding NICU surfaces are sourced from reservoirs within the room and that these reservoirs contain actively dividing cells. Collectively, the data suggests that hospitalized infants, in combination with their caregivers, shape the microbiome of NICU rooms.

microbiology↗

bioRxiv2017

Hospitalized premature infants are colonized by related bacterial strains with distinct proteomic profiles

During the first weeks of life, microbial colonization of the gut impacts human immune system maturation and other developmental processes. In premature infants, aberrant colonization has been implicated in the onset of necrotizing enterocolitis (NEC), a life-threatening intestinal disease. To study the premature infant gut colonization process, genome-resolved metagenomics was conducted on 343 fecal samples collected during the first three months of life from 35 premature infants housed in a neonatal intensive care unit, 14 of which developed NEC, and metaproteomic measurements were made on 87 samples. Microbial community composition and proteomic profiles remained relatively stable on the time scale of a week, but the proteome was more variable. Although genetically similar organisms colonized many infants, most infants were colonized by distinct strains with metabolic profiles that could be distinguished using metaproteomics. Microbiome composition correlated with infant, antibiotics administration, and NEC diagnosis. Communities were found to cluster into seven primary types, and community type switched within infants, sometimes multiple times. Interestingly, some communities sampled from the same infant at subsequent time points clustered with those of other infants. In some cases, switches preceded onset of NEC; however, no species or community type could account for NEC across the majority of infants. In addition to a correlation of protein abundances with organism replication rates, we found that organism proteomes correlated with overall community composition. Thus, this genome-resolved proteomics study demonstrates that the contributions of individual organisms to microbiome development depend on microbial community context.\n\nImportance\n\nHumans are colonized by microbes at birth, a process that is important to health and development. However, much remains to be known about the fine-scale microbial dynamics that occur during the colonization period. We conducted a genome-resolved study of microbial community composition, replication rates, and proteomes during the first three months of life of both healthy and sick premature infants. Infants were found to be colonized by similar microbes, but each underwent a distinct colonization trajectory.\n\nInterestingly, related microbes colonizing different infants were found to have distinct proteomes, indicating that microbiome function is not only driven by which organisms are present, but also largely depends on microbial responses to the unique set of physiological conditions in the infant gut.

microbiology↗

bioRxiv2017

Asymmetric MukB ATPases are regulated independently by the N- and C-terminal domains of MukF kleisin

The Escherichia coli SMC complex, MukBEF, acts in chromosome segregation. MukBEF shares the distinctive architecture of other SMC complexes, with one prominent difference; unlike other kleisins, MukF forms dimers through its N-terminal domain. We show that a 4-helix bundle adjacent to the MukF dimerization domain interacts functionally with the MukB coiled-coiled neck adjacent to the ATPase head, forming an asymmetric tripartite complex, as in other SMC complexes. Since MukF dimerization is preserved during this interaction, MukF directs the formation of dimer of dimers MukBEF complexes, observed previously in vivo. The MukF N- and C-terminal domains stimulate ATPase independently and additively, consistent with them each targeting only one of the two MukB ATPase active sites in the asymmetric complex. We demonstrate that MukF interaction with the MukB neck turns over during cycles of ATP binding and hydrolysis in vivo and that impairment of this interaction leads to MukBEF release from chromosomes.

biochemistry↗