A major challenge in computational biology is constraining free parameters in mathematical models. Adjusting a parameter to make a given model output more realistic sometimes has unexpected and undesirable effects on other model behaviors. Here, we extend a regression-based method for parameter sensitivity analysis and show that a straightforward procedure can uniquely define most ionic conductances in a well-known model of the human ventricular myocyte. The model's parameter sensitivity was analyzed by randomizing ionic conductances, running repeated simulations to measure physiological outputs, then collecting the randomized parameters and simulation results as “input” and “output” matrices, respectively. Multivariable regression derived a matrix whose elements indicate how changes in conductances influence model outputs. We show here that if the number of linearly-independent outputs equals the number of inputs, the regression matrix can be inverted. This is significant, because it implies that the inverted matrix can specify the ionic conductances that are required to generate a particular combination of model outputs. Applying this idea to the myocyte model tested, we found that most ionic conductances could be specified with precision (R2 > 0.77 for 12 out of 16 parameters). We also applied this method to a test case of changes in electrophysiology caused by heart failure and found that changes in most parameters could be well predicted. We complemented our findings using a Bayesian approach to demonstrate that model parameters cannot be specified using limited outputs, but they can be successfully constrained if multiple outputs are considered. Our results place on a solid mathematical footing the intuition-based procedure simultaneously matching a model's output to several data sets. More generally, this method shows promise as a tool to define model parameters, in electrophysiology and in other biological fields.

The capacity of stem cells to self renew and the ability of stem cell daughters to differentiate into highly specialized cells depend on external cues provided by their cellular microenvironments [1-3]. However, how microenvironments are shaped is poorly understood. In testes of Drosophila melanogaster, germ cells are enclosed by somatic support cells. This physical interrelationship depends on signaling from germ cells to the Epidermal growth factor receptor (Egfr) on somatic support cells [4]. We show that germ cells signal via the Egf class ligand Spitz (Spi) and provide evidence that the Egfr associates with and acts through the guanine nucleotide exchange factor Vav to regulate activities of Rac1. Reducing activity of the Egfr, Vav, or Rac1 from somatic support cells enhanced the germ cell enclosure defects of a conditional spi allele. Conversely, reducing activity of Rho1 from somatic support cells suppressed the germ cell enclosure defects of the conditional spi allele. We propose that a differential in Rac and Rho activities across somatic support cells guides their growth around the germ cells. Our novel findings reveal how signals from one cell type regulate cell-shape changes in another to establish a critical partnership required for proper differentiation of a stem cell lineage.

Across individuals within a population, several levels of variability are observed, from the differential expression of ion channels at the molecular level, to the various action potential morphologies observed at the cellular level, to divergent responses to drugs at the organismal level. However, the limited ability of experiments to probe complex interactions between components has hitherto hindered our understanding of the factors that cause a range of behaviours within a population. Variability is a challenging issue that is encountered in all physiological disciplines, but recent work suggests that novel methods for analysing mathematical models can assist in illuminating its causes. In this review, we discuss mathematical modelling studies in cardiac electrophysiology and neuroscience that have enhanced our understanding of variability in a number of key areas. Specifically, we discuss parameter sensitivity analysis techniques that may be applied to generate quantitative predictions based on considering behaviours within a population of models, thereby providing novel insight into variability. Our discussion focuses on four issues that have benefited from the utilization of these methods: (1) the comparison of different electrophysiological models of cardiac myocytes, (2) the determination of the individual contributions of different molecular changes in complex disease phenotypes, (3) the identification of the factors responsible for the variable response to drugs, and (4) the constraining of free parameters in electrophysiological models of heart cells. Together, the studies that we discuss suggest that rigorous analyses of mathematical models can generate quantitative predictions regarding how molecular-level variations contribute to functional differences between experimental samples. These strategies may be applicable not just in cardiac electrophysiology, but in a wide range of disciplines. University. During her Doctorate, she built mathematical models to understand the effects of changes in cardiac ion channel expression on clinically relevant and measurable properties of the heart. Currently, she is a Fellow at the Collège des Ingénieurs in Paris, where she is pursuing a specialized MBA for engineers and scientists while working for a leading French company. David Christini (middle) received a BS degree in electrical engineering from the Pennsylvania State University and MS and PhD degrees in biomedical engineering from Boston University. He is a Professor in the Departments of Medicine and Physiology and Biophysics, Weill Cornell Medical College, New York. He uses computational and experimental methods to study cellular-to organ-level cardiac electrophysiological dynamics, with an emphasis on understanding the mechanisms underlying arrhythmia initiation and in developing new arrhythmia therapies. Eric Sobie (right) is an Associate Professor in the Department of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine in New York City. He holds a BSE degree from Duke Unive...

The purpose of this review is to create a set of provisional criteria for Institutional Review Boards (IRBs) to refer to when assessing the ethical orientation of transgender health research proposals. We began by searching for literature on this topic using databases and the reference lists of key articles, resulting in a preliminary set of criteria. We then collaborated to develop the following nine guidelines: (1) Whenever possible, research should be grounded, from inception to dissemination, in a meaningful collaboration with community stakeholders; (2) language and framing of transgender health research should be non-stigmatizing; (3) research should be disseminated back to the community; (4) the diversity of the transgender and gender diverse (TGGD) community should be accurately reflected and sensitively reflected; (5) informed consent must be meaningful, without coercion or undue influence; (6) the protection of participant confidentiality should be paramount; (7) alternative consent procedures should be considered for TGGD minors; (8) research should align with current professional standards that refute conversion, reorientation, or reparative therapy; and (9) IRBs should guard against the temptation to avoid, limit, or delay research on this subject.