Tikhonov regularization, or ridge regression, is a popular technique to deal with collinearity in multivariate regression. We unveil a formal analogy between ridge regression and statistical mechanics, where the objective function is comparable to a free energy, and the ridge parameter plays the role of temperature. This analogy suggests two novel criteria for selecting a suitable ridge parameter: specific-heat (Cv) and maximum penalty (MP). We apply these fits to evaluate the relative contributions of rigid-body and internal fluctuations, which are typically highly collinear, to crystallographic B-factors. This issue is particularly important for computational models of protein dynamics, such as the elastic network model (ENM), since the amplitude of the predicted internal motion is commonly calibrated using B-factor data. After validation on simulated datasets, our results indicate that rigid-body motions account on average for more than 80% of the amplitude of B-factors. Furthermore, we evaluate the ability of different fits to reproduce the amplitudes of internal fluctuations in X-ray ensembles from the B-factors in the corresponding single X-ray structures. The new ridge criteria are shown to be markedly superior to the commonly used two-parameter fit that neglects rigid-body rotations and to the full fits regularized under generalized cross-validation. In conclusion, the proposed fits ensure a more robust calibration of the ENM force constant and should prove valuable in other applications.

There is an increasing need to investigate microbiomes in their entirety in a variety of contexts ranging from environmental to human health scenarios. This requirement is becoming increasingly important with the emergence of antibiotic resistance. In general, more conventional approaches are too expensive and/or time-consuming and often predicated on prior knowledge of the microorganisms one wishes to study. Herein, we propose the use of biospectroscopy tools as relatively high-throughput, non-destructive approaches to profile microbiomes under study. Fourier-transform infrared (FTIR) or Raman spectroscopy both generate fingerprint spectra of biological material and such spectra can readily be subsequently classed according to biochemical changes in the microbiota, such as emergence of antibiotic resistance. FTIR spectroscopy techniques generally can only be applied to desiccated material whereas Raman approaches can be applied to more hydrated samples. The ability to readily fingerprint microbiomes could lend itself to new approaches in determining microbial behaviours and emergence of antibiotic resistance.

In vitro models of the liver microvasculature, especially with respect to cancer cell extravasation, should include not only endothelial and cancer cells but also surrounding cells to mimic the physiological situation. To this end, in the present study, we established a physiologically-relevant hierarchical co-culture model by stacking layers of primary rat hepatocytes (Hep), hepatic stellate cells embedded in collagen gel (LX-2) and endothelial cells (HUVECs) on a specially designed oxygen-permeable polydimethylsiloxane PDMS bottom plate. The model was used to investigate the role and contribution of each of the three cell types in pancreatic cancer and promyeloblast cell adhesion. In particular, we showed an increase in albumin production by the primary hepatocytes and in the consumption of the produced vascular endothelial growth factors (VEGFs). Furthermore, in co-culture, the HUVECs exhibited a mature vascular endothelial and non-inflamed phenotype, as evidenced by Stabilin-1, lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1), intercellular adhesion molecule (ICAM-1), and vascular adhesion protein-1 (VAP-1) expression. The HUVECs were also successfully activated with an inflammatory cytokine and their ICAM-1 response was found to be higher in monoculture compared to co-culture. Additionally, the adhesion of MiaPaCa-2 pancreatic cancer cells and HL60 promyeloblasts was tested in both cases (i.e.: activation or not by an inflammatory cytokine). It has been found that their adhesion was always reduced in the co-culture model. These results highlight the importance of integrating hepatic stellate cells in the design of biomimetic models of the hepatic endothelial barrier.

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

Micropatterned cocultures are a useful experimental tool for the study of cell-cell interactions. Patterning methods often rely on sequential seeding of different cell types or removal of a barrier separating two populations, but it is difficult to pattern sharp interfaces between pure populations with low cross-contamination when using these approaches. Patterning by the use of reconfigurable substrates can overcome these limitations, but such methods can be costly and challenging to employ in a typical biology laboratory. Here, we describe a low-cost and simple-to-use reconfigurable substrate comprised of a transparent elastic material that is partially cut to form a slit that opens when the device is stretched. The slit seals back up when released, allowing two initially separate, adherent cell populations to be brought together to form a contact interface. Fluorescent imaging of patterned cocultures demonstrates the early establishment of a sharp cellular interface. As a proof of principle, we demonstrate the use of this device to study competition at the interface of two stem cell populations.

Magnetic liposome-mediated combined chemotherapy and hyperthermia is gaining importance as an effective therapeutic modality for cancer. However, control and maintenance of optimum hyperthermia are major challenges in clinical settings due to the overheating of tissues. To overcome this problem, we developed a novel magnetic liposomes formulation co-entrapping a dextran coated biphasic suspension of La0.75Sr0.25MnO3 (LSMO) and iron oxide (Fe3O4) nanoparticles for self-controlled hyperthermia and chemotherapy. However, the general apprehension about biocompatibility and safety of the newly developed formulation needs to be addressed. In this work, in vitro and in vivo biocompatibility and therapeutic evaluation studies of the novel magnetic liposomes are reported. Biocompatibility study of the magnetic liposomes formulation was carried out to evaluate the signs of preliminary systemic toxicity, if any, following intravenous administration of the magnetic liposomes in Swiss mice. Therapeutic efficacy of the magnetic liposomes formulation was evaluated in the fibrosarcoma tumour bearing mouse model. Fibrosarcoma tumour-bearing mice were subjected to hyperthermia following intratumoral injection of single or double doses of the magnetic liposomes with or without chemotherapeutic drug paclitaxel. Hyperthermia (three spurts, each at 3 days interval) with drug loaded magnetic liposomes following single dose administration reduced the growth of tumours by 2.5 fold (mean tumour volume 2356 ± 550 mm3) whereas the double dose treatment reduced the tumour growth by 3.6 fold (mean tumour volume 1045 ± 440 mm3) compared to their corresponding control (mean tumour volume 3782 ± 515 mm3). At the end of the tumour efficacy studies, the presence of MNPs was studied in the remnant tumour tissues and vital organs of the mice. No significant leaching or drainage of the magnetic liposomes during the study was observed from the tumour site to the other vital organs of the body, suggesting again the potential of the novel magnetic liposomes formulation for possibility of developing as an effective modality for treatment of drug resistant or physiologically vulnerable cancer.