Optimal dose of lactoferrin reduces the resilience of in vitro Staphylococcus aureus colonies.

The rise in antibiotic resistance has stimulated research into adjuvants that can improve the efficacy of broad-spectrum antibiotics. Lactoferrin is a candidate adjuvant; it is a multifunctional iron-binding protein with antimicrobial properties. It is known to show dose-dependent antimicrobial activity against Staphylococcus aureus through iron sequestration and repression of ß-lactamase expression. However, S. aureus can extract iron from lactoferrin through siderophores for their growth, which confounds the resolution of lactoferrin's method of action. We measured the minimum inhibitory concentration (MIC) for a range of lactoferrin/ ß-lactam antibiotic dose combinations and observed that at low doses (< 0.39 µM), lactoferrin contributes to increased S. aureus growth, but at higher doses (> 6.25 µM), iron-depleted native lactoferrin reduced bacterial growth and reduced the MIC of the ß-lactam-antibiotic cefazolin. This differential behaviour points to a bacterial population response to the lactoferrin/ ß-lactam dose combination. Here, with the aid of a mathematical model, we show that lactoferrin stratifies the bacterial population, and the resulting population heterogeneity is at the basis of the dose dependent response seen. Further, lactoferrin disables a sub-population from ß-lactam-induced production of ß-lactamase, which when sufficiently large reduces the population's ability to recover after being treated by an antibiotic. Our analysis shows that an optimal dose of lactoferrin acts as a suitable adjuvant to eliminate S. aureus colonies using ß-lactams, but sub-inhibitory doses of lactoferrin reduces the efficacy of ß-lactams.

Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics.

The value of digital twins for prototyping controllers or interventions in a sandbox environment are well-established in engineering and physics. However, this is challenging for biophysics trying to seamlessly compose models of multiple spatial and temporal scale behavior into the digital twin. Two challenges stand out as constraining progress: (i) ensuring physical consistency of conservation laws across composite models and (ii) drawing useful and timely clinical and scientific information from conceptually and computationally complex models. Challenge (i) can be robustly addressed with bondgraphs. However, challenge (ii) is exacerbated using this approach. The complexity question can be looked at from multiple angles. First from the perspective of discretizations that reflect underlying biophysics (functional tissue units) and secondly by exploring maximum entropy as the principle guiding multicellular biophysics. Statistical mechanics, long applied to understanding emergent phenomena from atomic physics, coupled with the observation that cellular architecture in tissue is orchestrated by biophysical constraints on metabolism and communication, shows conceptual promise. This architecture along with cell specific properties can be used to define tissue specific network motifs associated with energetic contributions. Complexity can be addressed based on energy considerations and finding mean measures of dependent variables. A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.

Our natural "makeup" reveals more than it hides: Modeling the skin and its microbiome.

Skin is our primary interface with the environment. A structurally and functionally complex organ that hosts a dynamic ecosystem of microbes, and synthesizes many compounds that affect our well-being and psychosocial interactions. It is a natural platform of signal exchange between internal organs, skin resident microbes, and the environment. These interactions have gained a great deal of attention due to the increased prevalence of atopic diseases, and the co-occurrence of multiple allergic diseases related to allergic sensitization in early life. Despite significant advances in experimentally characterizing the skin, its microbial ecology, and disease phenotypes, high-levels of variability in these characteristics even for the same clinical phenotype are observed. Addressing this variability and resolving the relevant biological processes requires a systems approach. This review presents some of our current understanding of the skin, skin-immune, skin-neuroendocrine, skin-microbiome interactions, and computer-based modeling approaches to simulate this ecosystem in the context of health and disease. The review highlights the need for a systems-based understanding of this sophisticated ecosystem. This article is categorized under: Infectious Diseases > Computational Models.

Inferring intra-cellular mechanics using geometric metamorphosis: A preliminary study.

Mechanotransduction plays an important role in sub-cellular processes and is an active area of research. Determining the forces/strains that the intra-cellular structures experience is vital for developing quantitative models of cellular behavior. Established techniques such as traction force microscopy, digital image correlation etc. track surface forces and kinematics of intra-cellular structures. However, difficulties arise when cells cannot be seeded on micro-patterned substrates or the intra-cellular structures vary (unstable landmarks). Here, we applied geometric metamorphosis, a global image registration method, to determine the kinematic profile of a cell during cell division. The method does not require stable landmarks, the registration is non-local in nature and constraints such as volume conservation can be enforced. The cell wall was tracked over time and a sequence of transformations relating the cell wall at the start of cytokinesis to the configuration prior to the daughters completely separate was determined. These transformations are associated with a scalar metric and a statistical atlas describing the wall kinematics from multiple tracking's of the wall shape is constructed. Using these transformations, the cellular kinematics can be described using a Lagrangian frame of reference and the evolution of a material point property can be easily modeled. To demonstrate this, we use the kinematic data derived from the atlas along with a model of stress-fiber (de)formation dynamics to simulate the stress-fiber configuration as the cell domain deforms.

ICMA: an integrated cardiac modeling and analysis platform.

UNLABELLED: ICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses speckle tracking (endocardial border detection) on ultrasound (MRI) imaging data in the form of DICOM. Standard American Heart Association segment-based strain analysis can be performed using a browser-based interface. The speckle tracking, border detection and model fitting methods are implemented in C++ using open-source tools. They are wrapped as web services and orchestrated via a JBOSS-based application server. AVAILABILITY AND IMPLEMENTATION: The source code for ICMA is freely available under MPL 1.1 or GPL 2.0 or LGPL 2.1 license at https://github.com/ABI-Software-Laboratory/ICMA and a standalone virtual machine at http://goo.gl/M4lJKH for download. CONTACT: r.jagir@auckland.ac.nz SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Using CellML with OpenCMISS to Simulate Multi-Scale Physiology.

OpenCMISS is an open-source modeling environment aimed, in particular, at the solution of bioengineering problems. OpenCMISS consists of two main parts: a computational library (OpenCMISS-Iron) and a field manipulation and visualization library (OpenCMISS-Zinc). OpenCMISS is designed for the solution of coupled multi-scale, multi-physics problems in a general-purpose parallel environment. CellML is an XML format designed to encode biophysically based systems of ordinary differential equations and both linear and non-linear algebraic equations. A primary design goal of CellML is to allow mathematical models to be encoded in a modular and reusable format to aid reproducibility and interoperability of modeling studies. In OpenCMISS, we make use of CellML models to enable users to configure various aspects of their multi-scale physiological models. This avoids the need for users to be familiar with the OpenCMISS internal code in order to perform customized computational experiments. Examples of this are: cellular electrophysiology models embedded in tissue electrical propagation models; material constitutive relationships for mechanical growth and deformation simulations; time-varying boundary conditions for various problem domains; and fluid constitutive relationships and lumped-parameter models. In this paper, we provide implementation details describing how CellML models are integrated into multi-scale physiological models in OpenCMISS. The external interface OpenCMISS presents to users is also described, including specific examples exemplifying the extensibility and usability these tools provide the physiological modeling and simulation community. We conclude with some thoughts on future extension of OpenCMISS to make use of other community developed information standards, such as FieldML, SED-ML, and BioSignalML. Plans for the integration of accelerator code (graphical processing unit and field programmable gate array) generated from CellML models is also discussed.

A mean-field model of ventricular muscle tissue.

A theoretical model of the cross-linking topology of ventricular muscle tissue is developed. Using parameter estimation the terms of the theoretical model are estimated for normal and pathological conditions. The model represents the anisotropic structure of the tissue, reproduces published experimental data and characterizes the role of different tissue components in the observed macroscopic behavior. Changes in the material parameters are consistent with expected structural changes and the model is extended to reproduce force-Calcium relationships. Model results are invoked to argue that semisoft behavior and the material axis anisotropy arise from the constraints on the extracellular matrix cross-linking topology.

Efficient solutions of cardiac membrane models using novel unsupervised clustering algorithm.

We present a method to efficiently solve cardiac membrane models using a novel unsupervised clustering algorithm. The unsupervised clustering algorithm was designed to handle repeated clustering of multidimensional objects with rapidly changing properties. A Modified Trie datastructure that allowed efficient search, scalable and distributed assembly of the result was designed. The method was applied to solve monodomain models of cardiac tissue with highly non-linear reaction elements. We demonstrate the versatility and advantages of using the method by subjecting the tissue to various spatial excitation patterns.