Parameter identifiability and model selection for partial differential equation models of cell invasion.

When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter identifiability for four extensions of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.

E-cadherin biomaterials reprogram collective cell migration and cell cycling by forcing homeostatic conditions.

Cells attach to the world through either cell-extracellular matrix adhesion or cell-cell adhesion, and traditional biomaterials imitate the matrix for integrin-based adhesion. However, materials incorporating cadherin proteins that mimic cell-cell adhesion offer an alternative to program cell behavior and integrate into living tissues. We investigated how cadherin substrates affect collective cell migration and cell cycling in epithelia. Our approach involved biomaterials with matrix proteins on one-half and E-cadherin proteins on the other, forming a "Janus" interface across which we grew a single sheet of cells. Tissue regions over the matrix side exhibited normal collective dynamics, but an abrupt behavior shift occurred across the Janus boundary onto the E-cadherin side, where cells attached to the substrate via E-cadherin adhesions, resulting in stalled migration and slowing of the cell cycle. E-cadherin surfaces disrupted long-range mechanical coordination and nearly doubled the length of the G0/G1 phase of the cell cycle, linked to the lack of integrin focal adhesions on the E-cadherin surface.

E-cadherin biointerfaces reprogram collective cell migration and cell cycling by forcing homeostatic conditions.

Cells attach to the world around them in two ways-cell:extracellular-matrix adhesion and cell:cell adhesion-and conventional biomaterials are made to resemble the matrix to encourage integrin-based cell adhesion. However, interest is growing for cell-mimetic interfaces that mimic cell-cell interactions using cadherin proteins, as this offers a new way to program cell behavior and design synthetic implants and objects that can integrate directly into living tissues. Here, we explore how these cadherin-based materials affect collective cell behaviors, focusing specifically on collective migration and cell cycle regulation in cm-scale epithelia. We built culture substrates where half of the culture area was functionalized with matrix proteins and the contiguous half was functionalized with E-cadherin proteins, and we grew large epithelia across this 'Janus' interface. Parts of the tissues in contact with the matrix side of the Janus interface exhibited normal collective dynamics, but an abrupt shift in behaviors happened immediately across the Janus boundary onto the E-cadherin side, where cells formed hybrid E-cadherin junctions with the substrate, migration effectively froze in place, and cell-cycling significantly decreased. E-cadherin materials suppressed long-range mechanical correlations in the tissue and mechanical information reflected off the substrate interface. These effects could not be explained by conventional density, shape index, or contact inhibition explanations. E-cadherin surfaces nearly doubled the length of the G0/G1 phase of the cell cycle, which we ultimately connected to the exclusion of matrix focal adhesions induced by the E-cadherin culture surface.

Learning the rules of collective cell migration using deep attention networks.

Collective, coordinated cellular motions underpin key processes in all multicellular organisms, yet it has been difficult to simultaneously express the 'rules' behind these motions in clear, interpretable forms that effectively capture high-dimensional cell-cell interaction dynamics in a manner that is intuitive to the researcher. Here we apply deep attention networks to analyze several canonical living tissues systems and present the underlying collective migration rules for each tissue type using only cell migration trajectory data. We use these networks to learn the behaviors of key tissue types with distinct collective behaviors-epithelial, endothelial, and metastatic breast cancer cells-and show how the results complement traditional biophysical approaches. In particular, we present attention maps indicating the relative influence of neighboring cells to the learned turning decisions of a 'focal cell'-the primary cell of interest in a collective setting. Colloquially, we refer to this learned relative influence as 'attention', as it serves as a proxy for the physical parameters modifying the focal cell's future motion as a function of each neighbor cell. These attention networks reveal distinct patterns of influence and attention unique to each model tissue. Endothelial cells exhibit tightly focused attention on their immediate forward-most neighbors, while cells in more expansile epithelial tissues are more broadly influenced by neighbors in a relatively large forward sector. Attention maps of ensembles of more mesenchymal, metastatic cells reveal completely symmetric attention patterns, indicating the lack of any particular coordination or direction of interest. Moreover, we show how attention networks are capable of detecting and learning how these rules change based on biophysical context, such as location within the tissue and cellular crowding. That these results require only cellular trajectories and no modeling assumptions highlights the potential of attention networks for providing further biological insights into complex cellular systems.

Homelessness and rates of physical dysfunctions characteristic of premature geriatric syndromes: systematic review and meta-analysis.

BACKGROUND: Homeless adults may experience accelerated aging, presenting earlier with geriatric syndromes such as falls and functional limitations. Though homelessness is surging in United States, data are scarce regarding rates of physical dysfunctions characteristic of geriatric syndromes experienced in this underserved population. PURPOSE: Examine associations between homelessness, premature geriatric syndromes, and functional limitations. METHODS: Two reviewers independently searched PubMed, CINAHL, and PEDro databases for prognostic studies reporting rates of geriatric syndromes in homeless adults aged 40 years and older. Two reviewers independently performed study selection. Data were extracted for homeless adults and community-dwelling controls regarding age, demographic information, limitations of activities of daily living (ADL) and instrumental ADL (IADL), frailty, and falls the past year. Risk ratio (RR) and 95% confidence interval (CI) were calculated across studies to compare groups. RESULTS: Five studies met predetermined criteria. Meta-analysis revealed greater rates in homeless adults (average age 56) compared to housed adults (average age 78) for ADL limitation (RR = 1.50, 95% CI = 1.37-1.64) and IADL limitation (RR = 1.36, 95% CI = 1.28-1.45). Falls were three times more prevalent in homeless individuals (RR = 3.42, 95% CI = 3.16-3.70). Heterogeneous frailty data did not reach significance (RR = 2.59, 95% CI = 0.90-7.46). CONCLUSION: Homeless adults have increased risk of premature geriatric syndromes. Limitations in ADL and IADL rates were 30-50% higher than adults with stable housing averaging 20 years older, and fall rates were three times higher than controls averaging 4.5 years older. These results underscore the need for healthcare providers such as physical therapists to address physical dysfunction in homeless adults.

Fostering Equitable Outcomes in Introductory Biology Courses through Use of a Dual Domain Pedagogy.

Recent studies demonstrate that significant learning gains can be achieved when instructors take intentional steps to address the affective components of learning. While such efforts enhance the outcomes of all students, they are particularly beneficial for students from underrepresented groups and can reduce performance gaps. In the present study, we examined whether intentional efforts to address the affective domain of learning (through growth mindset messaging) can synergize with best practices for addressing the cognitive domain (via active-learning strategies) to enhance academic outcomes in biology courses. We compared the impact of this two-pronged approach (known as dual domain pedagogy, or DDP) with that of two other pedagogies (lecture only or active learning only). Our results demonstrate that DDP is a powerful tool for narrowing performance gaps. DDP, but not active learning, eliminated the performance gap observed between Black and white students in response to lecture. While a significant gap between white and Latin@ students was observed in response to active learning (but not lecture), this gap was reduced by DDP. These findings demonstrate that DDP is an effective approach for promoting a more equitable classroom and can foster learning outcomes that supersede those conferred by active learning alone.