Membrane systems with proteins embedded in membranes

Membrane computing is a biologically inspired computational paradigm. Motivated by brane calculi we investigate membrane systems which differ from conventional membrane systems by the following features: (1) biomolecules (proteins) can move through the regions of the systems, and can attach onto (and de-attach from) membranes, and (2) membranes can evolve depending on the attached molecules. The evolution of membranes is performed by using rules that are motivated by the operation of pinocytosis (the pino rule) and the operation of cellular dripping (the drip rule) that take place in living cells. We show that such membrane systems are computationally universal. We also show that if only the second feature is used then one can generate at least the family of Parikh images of the languages generated by programmed grammars without appearance checking (which contains non-semilinear sets of vectors). If, moreover, the use of pino/drip rules is non-cooperative (i.e., not dependent on the proteins attached to membranes), then one generates a family of sets of vectors that is strictly included in the family of semilinear sets of vectors. We also consider a number of decision problems concerning reachability of configurations and boundness

Dynamics of Marine Microbial Metabolism and Physiology at Station Aloha.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Markov field models of molecular kinetics

Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

Structure, Dynamics, and Regulation of Collective Cell Migration

Collective migration is the process by which cells organize individual motions to productively migrate as a group and plays a fundamental role in organism development, tissue regeneration, and cancer invasion. In development, coordinated migration facilitates the formation of complex organ structures and is required for proper dissemination of neural crest cells throughout an organism. After injury, this process allows breaches in epithelial layers to be repaired while maintaining tissue integrity, and in cancer, collective behavior enhances invasion of tumor cells into the surrounding tissue. Chapter 1 provides an introduction for the role of collective migration across an organism’s lifespan, the mechanisms used by cells to generate motile force, and the emergence of collective behavior. Chapter 2 dissects the intertwined roles of three fundamental parameters often altered in collective migration processes: cell density, cell adhesion, and cell-cell contractility through the Rho-ROCK-Myosin II signaling axis. Through quantitative analysis of large-scale time-lapse imaging and mathematical modeling, I identify force-sensitive contractility and cell packing as mediators of two distinct classes of collective migration. From these results, I formulate a phase-diagram of collective cell migration and test predictions in an in-vivo epithelium using genetic manipulations to drive collective motion between predicted migratory phases. In Chapter 3, the effect of phenotypic heterogeneity on the organization of cells is examined, providing insight into the effects of early cancer progression on epithelial dynamics. I find that mutant cells within an otherwise wild-type tissue impact organization through local and field-effects, disrupting normal dynamics and leading to cell-type segregation. Chapter 4 provides a theoretical framework for quantitatively understanding and predicting the dynamics of protein interactions underlying biological processes including collective migration. Traditional chemical kinetics approaches break down in situations where components are slow diffusing or in countable numbers, requiring the formulation of new models that take into account this level of complexity. Here I develop an event-driven algorithm that bridges well-mixed and unmixed systems and use it to predict the effect of apparent changes in enzymatic efficiency due to alterations in mobility that may be caused by protein complex formation. Overall the work in this dissertation advances our understanding of the structure and dynamics of collective migration and the parameters governing this process by combining quantitative statistical analysis, mathematical modeling, and in-vivo live imaging

Defining complex rule-based models in space and over time

Computational biology seeks to understand complex spatio-temporal phenomena across multiple levels of structural and functional organisation. However, questions raised in this context are difficult to answer without modelling methodologies that are intuitive and approachable for non-expert users. Stochastic rule-based modelling languages such as Kappa have been the focus of recent attention in developing complex biological models that are nevertheless concise, comprehensible, and easily extensible. We look at further developing Kappa, in terms of how we might define complex models in both the spatial and the temporal axes. In defining complex models in space, we address the assumption that the reaction mixture of a Kappa model is homogeneous and well-mixed. We propose evolutions of the current iteration of Spatial Kappa to streamline the process of defining spatial structures for different modelling purposes. We also verify the existing implementation against established results in diffusion and narrow escape, thus laying the foundations for querying a wider range of spatial systems with greater confidence in the accuracy of the results. In defining complex models over time, we draw attention to how non-modelling specialists might define, verify, and analyse rules throughout a rigorous model development process. We propose structured visual methodologies for developing and maintaining knowledge base data structures, incorporating the information needed to construct a Kappa rule-based model. We further extend these methodologies to deal with biological systems defined by the activity of synthetic genetic parts, with the hope of providing tractable operations that allow multiple users to contribute to their development over time according to their area of expertise. Throughout the thesis we pursue the aim of bridging the divide between information sources such as literature and bioinformatics databases and the abstracting decisions inherent in a model. We consider methodologies for automating the construction of spatial models, providing traceable links from source to model element, and updating a model via an iterative and collaborative development process. By providing frameworks for modellers from multiple domains of expertise to work with the language, we reduce the entry barrier and open the field to further questions and new research

Bayesian Inference Approaches for Particle Trajectory Analysis in Cell Biology

Despite the importance of single particle motion in biological systems, systematic inference approaches to analyze particle trajectories and evaluate competing motion models are lacking. An automated approach for robust evaluation of motion models that does not require manual intervention is highly desirable to enable analysis of datasets from high-throughput imaging technologies that contain hundreds or thousands of trajectories of biological particles, such as membrane receptors, vesicles, chromosomes or kinetochores, mRNA particles, or whole cells in developing embryos. Bayesian inference is a general theoretical framework for performing such model comparisons that has proven successful in handling noise and experimental limitations in other biological applications. The inherent Bayesian penalty on model complexity, which avoids overfitting, is particularly important for particle trajectory analysis given the highly stochastic nature of particle diffusion. This thesis presents two complementary approaches for analyzing particle motion using Bayesian inference. The first method, MSD-Bayes, discriminates a wide range of motion models--including diffusion, directed motion, anomalous and confined diffusion--based on mean- square displacement analysis of a set of particle trajectories, while the second method, HMM-Bayes, identifies dynamic switching between diffusive and directed motion along individual trajectories using hidden Markov models. These approaches are validated on biological particle trajectory datasets from a wide range of experimental systems, demonstrating their broad applicability to research in cell biology

INTEGRATION OF MODELING AND EXPERIMENTS TO DEFINE PRINCIPLES OF EGFR ACTIVATION AND UBIQUITINATION

Epidermal growth factor receptor (EGFR)-dependent signaling is involved in numerous physiological processes, and its deregulation leads to cellular dysfunctions and pathologies, first and forecast, cancer. Endocytosis has a crucial impact on the downstream EGFR signaling response and it is regulated by ligand concentration. Indeed, depending on the EGF dose, the EGFR can be internalized through clathrin-mediated endocytosis (CME) or non-clathrin endocytosis (NCE). The switch between these two internalization mechanisms occurs over a narrow range of EGF concentrations (1-10 ng/ml). Importantly, EGFR ubiquitination shows a threshold response over the same range of EGF doses and is responsible for the commitment of EGFR to NCE, and thus, for EGFR signal extinction through receptor degradation. In this project, we were interested in elucidating the cellular mechanisms that regulate and coordinate the choice between these two endocytic routes, in addition, we aim to clarify how the integration of the two pathways influences EGFR downstream signaling. In order to deal with the complexity of the system, we adopted an integrated research approach combining mathematical modeling with wet-lab experiments. To this purpose, in collaboration with the Systems Biology group at our Institute, we developed a mathematical model of early EGFR activation that quantitatively accounts for the ubiquitination threshold observed at 2 minutes of EGF stimulation. The \u2018early model\u2019 was able to generate important predictions; in particular, it predicts a weakness in the system that is unveiled in the presence of high EGF concentrations and EGFR overexpression, two conditions frequently observed in cancer. We tested these predictions using different cell-based model systems subjected to varying perturbations. A challenge in the biological validation of the model, was obtaining quantitative reproducible data. To this aim, we optimized a quantitative ELISA-based assay to measure EGFR ubiquitination/phosphorylation upon different perturbations. This assay revealed to be powerful and allowed us to validate the predictions generated by the model. Thanks to our integrative approach, we identified Cbl as the limiting and weak element of the system. We expect that our model of EGFR activation will provide novel insights into the role of EGFR endocytosis, controlling the balance between EGFR signaling and downmodulation, frequently altered in cancer

Functionally Relevant Macromolecular Interactions of Disordered Proteins

Disordered proteins are relatively recent newcomers in protein science. They were first described in detail by Wright and Dyson, in their J. Mol. Biol. paper in 1999. First, it was generally thought for more than a decade that disordered proteins or disordered parts of proteins have different amino acid compositions than folded proteins, and various prediction methods were developed based on this principle. These methods were suitable for distinguishing between the disordered (unstructured) and structured proteins known at that time. In addition, they could predict the site where a folded protein binds to the disordered part of a protein, shaping the latter into a well-defined 3D structure. Recently, however, evidence has emerged for a new type of disordered protein family whose members can undergo coupled folding and binding without the involvement of any folded proteins. Instead, they interact with each other, stabilizing their structure via “mutual synergistic folding” and, surprisingly, they exhibit the same residue composition as the folded protein. Increasingly more examples have been found where disordered proteins interact with non-protein macromolecules, adding to the already large variety of protein–protein interactions. There is also a very new phenomenon when proteins are involved in phase separation, which can represent a weak but functionally important macromolecular interaction. These phenomena are presented and discussed in the chapters of this book

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 403)

This bibliography lists 217 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1995. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance