Breeding biology and the evolution of dynamic sexual dichromatism in frogs.

PubMed

Bell, R C; Webster, G N; Whiting, M J

2017-12-01

Dynamic sexual dichromatism is a temporary colour change between the sexes and has evolved independently in a wide range of anurans, many of which are explosive breeders wherein males physically compete for access to females. Behavioural studies in a few species indicate that dynamic dichromatism functions as a visual signal in large breeding aggregations; however, the prevalence of this trait and the social and environmental factors underlying its expression are poorly understood. We compiled a database of 178 anurans with dynamic dichromatism that include representatives from 15 families and subfamilies. Dynamic dichromatism is common in two of the three subfamilies of hylid treefrogs. Phylogenetic comparative analyses of 355 hylid species (of which 95 display dynamic dichromatism) reveal high transition rates between dynamic dichromatism, ontogenetic (permanent) dichromatism and monochromatism reflecting the high evolutionary lability of this trait. Correlated evolution in hylids between dynamic dichromatism and forming large breeding aggregations indicates that the evolution of large breeding aggregations precedes the evolution of dynamic dichromatism. Multivariate phylogenetic logistic regression recovers the interaction between biogeographic distribution and forming breeding aggregations as a significant predictor of dynamic dichromatism in hylids. Accounting for macroecological differences between temperate and tropical regions, such as seasonality and the availability of breeding sites, may improve our understanding of ecological contexts in which dynamic dichromatism is likely to arise in tropical lineages and why it is retained in some temperate species and lost in others. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Enhanced sampling techniques in molecular dynamics simulations of biological systems.

PubMed

Bernardi, Rafael C; Melo, Marcelo C R; Schulten, Klaus

2015-05-01

Molecular dynamics has emerged as an important research methodology covering systems to the level of millions of atoms. However, insufficient sampling often limits its application. The limitation is due to rough energy landscapes, with many local minima separated by high-energy barriers, which govern the biomolecular motion. In the past few decades methods have been developed that address the sampling problem, such as replica-exchange molecular dynamics, metadynamics and simulated annealing. Here we present an overview over theses sampling methods in an attempt to shed light on which should be selected depending on the type of system property studied. Enhanced sampling methods have been employed for a broad range of biological systems and the choice of a suitable method is connected to biological and physical characteristics of the system, in particular system size. While metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics, simulated annealing is well suited to characterize very flexible systems. The use of annealing methods for a long time was restricted to simulation of small proteins; however, a variant of the method, generalized simulated annealing, can be employed at a relatively low computational cost to large macromolecular complexes. Molecular dynamics trajectories frequently do not reach all relevant conformational substates, for example those connected with biological function, a problem that can be addressed by employing enhanced sampling algorithms. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.

Information Complexity and Biology

NASA Astrophysics Data System (ADS)

Bagnoli, Franco; Bignone, Franco A.; Cecconi, Fabio; Politi, Antonio

Kolmogorov contributed directly to Biology in essentially three problems: the analysis of population dynamics (Lotka-Volterra equations), the reaction-diffusion formulation of gene spreading (FKPP equation), and some discussions about Mendel's laws. However, the widely recognized importance of his contribution arises from his work on algorithmic complexity. In fact, the limited direct intervention in Biology reflects the generally slow growth of interest of mathematicians towards biological issues. From the early work of Vito Volterra on species competition, to the slow growth of dynamical systems theory, contributions to the study of matter and the physiology of the nervous system, the first 50-60 years have witnessed important contributions, but as scattered pieces apparently uncorrelated, and in branches often far away from Biology. Up to the 40' it is hard to see the initial loose build up of a convergence, for those theories that will become mainstream research by the end of the century, and connected by the study of biological systems per-se.

Molecular dynamics simulations and applications in computational toxicology and nanotoxicology.

PubMed

Selvaraj, Chandrabose; Sakkiah, Sugunadevi; Tong, Weida; Hong, Huixiao

2018-02-01

Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations. Published by Elsevier Ltd.

Conservation of Dynamics Associated with Biological Function in an Enzyme Superfamily.

PubMed

Narayanan, Chitra; Bernard, David N; Bafna, Khushboo; Gagné, Donald; Chennubhotla, Chakra S; Doucet, Nicolas; Agarwal, Pratul K

2018-03-06

Enzyme superfamily members that share common chemical and/or biological functions also share common features. While the role of structure is well characterized, the link between enzyme function and dynamics is not well understood. We present a systematic characterization of intrinsic dynamics of over 20 members of the pancreatic-type RNase superfamily, which share a common structural fold. This study is motivated by the fact that the range of chemical activity as well as molecular motions of RNase homologs spans over 10 5 folds. Dynamics was characterized using a combination of nuclear magnetic resonance experiments and computer simulations. Phylogenetic clustering led to the grouping of sequences into functionally distinct subfamilies. Detailed characterization of the diverse RNases showed conserved dynamical traits for enzymes within subfamilies. These results suggest that selective pressure for the conservation of dynamical behavior, among other factors, may be linked to the distinct chemical and biological functions in an enzyme superfamily. Copyright © 2018 Elsevier Ltd. All rights reserved.

Seasonally forced disease dynamics explored as switching between attractors

NASA Astrophysics Data System (ADS)

Keeling, Matt J.; Rohani, Pejman; Grenfell, Bryan T.

2001-01-01

Biological phenomena offer a rich diversity of problems that can be understood using mathematical techniques. Three key features common to many biological systems are temporal forcing, stochasticity and nonlinearity. Here, using simple disease models compared to data, we examine how these three factors interact to produce a range of complicated dynamics. The study of disease dynamics has been amongst the most theoretically developed areas of mathematical biology; simple models have been highly successful in explaining the dynamics of a wide variety of diseases. Models of childhood diseases incorporate seasonal variation in contact rates due to the increased mixing during school terms compared to school holidays. This ‘binary’ nature of the seasonal forcing results in dynamics that can be explained as switching between two nonlinear spiral sinks. Finally, we consider the stability of the attractors to understand the interaction between the deterministic dynamics and demographic and environmental stochasticity. Throughout attention is focused on the behaviour of measles, whooping cough and rubella.

From powder to solution: hydration dependence of human hemoglobin dynamics correlated to body temperature.

PubMed

Stadler, A M; Digel, I; Embs, J P; Unruh, T; Tehei, M; Zaccai, G; Büldt, G; Artmann, G M

2009-06-17

A transition in hemoglobin (Hb), involving partial unfolding and aggregation, has been shown previously by various biophysical methods. The correlation between the transition temperature and body temperature for Hb from different species, suggested that it might be significant for biological function. To focus on such biologically relevant human Hb dynamics, we studied the protein internal picosecond motions as a response to hydration, by elastic and quasielastic neutron scattering. Rates of fast diffusive motions were found to be significantly enhanced with increasing hydration from fully hydrated powder to concentrated Hb solution. In concentrated protein solution, the data showed that amino acid side chains can explore larger volumes above body temperature than expected from normal temperature dependence. The body temperature transition in protein dynamics was absent in fully hydrated powder, indicating that picosecond protein dynamics responsible for the transition is activated only at a sufficient level of hydration. A collateral result from the study is that fully hydrated protein powder samples do not accurately describe all aspects of protein picosecond dynamics that might be necessary for biological function.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

PubMed Central

He, Fei; Murabito, Ettore; Westerhoff, Hans V.

2016-01-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. PMID:27075000

Overshoot in biological systems modelled by Markov chains: a non-equilibrium dynamic phenomenon.

PubMed

Jia, Chen; Qian, Minping; Jiang, Daquan

2014-08-01

A number of biological systems can be modelled by Markov chains. Recently, there has been an increasing concern about when biological systems modelled by Markov chains will perform a dynamic phenomenon called overshoot. In this study, the authors found that the steady-state behaviour of the system will have a great effect on the occurrence of overshoot. They showed that overshoot in general cannot occur in systems that will finally approach an equilibrium steady state. They further classified overshoot into two types, named as simple overshoot and oscillating overshoot. They showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a non-equilibrium dynamic phenomenon with energy consumption. In addition, the main result in this study is validated with real experimental data.

Review of the fundamental theories behind small angle X-ray scattering, molecular dynamics simulations, and relevant integrated application.

PubMed

Boldon, Lauren; Laliberte, Fallon; Liu, Li

2015-01-01

In this paper, the fundamental concepts and equations necessary for performing small angle X-ray scattering (SAXS) experiments, molecular dynamics (MD) simulations, and MD-SAXS analyses were reviewed. Furthermore, several key biological and non-biological applications for SAXS, MD, and MD-SAXS are presented in this review; however, this article does not cover all possible applications. SAXS is an experimental technique used for the analysis of a wide variety of biological and non-biological structures. SAXS utilizes spherical averaging to produce one- or two-dimensional intensity profiles, from which structural data may be extracted. MD simulation is a computer simulation technique that is used to model complex biological and non-biological systems at the atomic level. MD simulations apply classical Newtonian mechanics' equations of motion to perform force calculations and to predict the theoretical physical properties of the system. This review presents several applications that highlight the ability of both SAXS and MD to study protein folding and function in addition to non-biological applications, such as the study of mechanical, electrical, and structural properties of non-biological nanoparticles. Lastly, the potential benefits of combining SAXS and MD simulations for the study of both biological and non-biological systems are demonstrated through the presentation of several examples that combine the two techniques.

Cellular fluid mechanics.

PubMed

Kamm, Roger D

2002-01-01

The coupling of fluid dynamics and biology at the level of the cell is an intensive area of investigation because of its critical role in normal physiology and disease. Microcirculatory flow has been a focus for years, owing to the complexity of cell-cell or cell-glycocalyx interactions. Noncirculating cells, particularly those that comprise the walls of the circulatory system, experience and respond biologically to fluid dynamic stresses. In this article, we review the more recent studies of circulating cells, with an emphasis on the role of the glycocalyx on red-cell motion in small capillaries and on the deformation of leukocytes passing through the microcirculation. We also discuss flows in the vicinity of noncirculating cells, the influence of fluid dynamic shear stress on cell biology, and diffusion in the lipid bi-layer, all in the context of the important fluid-dynamic phenomena.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Ultrafast electron diffraction and electron microscopy: present status and future prospects

NASA Astrophysics Data System (ADS)

Ishchenko, A. A.; Aseyev, S. A.; Bagratashvili, V. N.; Panchenko, V. Ya; Ryabov, E. A.

2014-07-01

Acting as complementary research tools, high time-resolved spectroscopy and diffractometry techniques proceeding from various physical principles open up new possibilities for studying matter with necessary integration of the 'structure-dynamics-function' triad in physics, chemistry, biology and materials science. Since the 1980s, a new field of research has started at the leading research laboratories, aimed at developing means of filming the coherent dynamics of nuclei in molecules and fast processes in biological objects ('atomic and molecular movies'). The utilization of ultrashort laser pulse sources has significantly modified traditional electron beam approaches to and provided high space-time resolution for the study of materials. Diffraction methods using frame-by-frame filming and the development of the main principles of the study of coherent dynamics of atoms have paved the way to observing wave packet dynamics, the intermediate states of reaction centers, and the dynamics of electrons in molecules, thus allowing a transition from the kinetics to the dynamics of the phase trajectories of molecules in the investigation of chemical reactions.

Molecular dynamics simulations of large macromolecular complexes.

PubMed

Perilla, Juan R; Goh, Boon Chong; Cassidy, C Keith; Liu, Bo; Bernardi, Rafael C; Rudack, Till; Yu, Hang; Wu, Zhe; Schulten, Klaus

2015-04-01

Connecting dynamics to structural data from diverse experimental sources, molecular dynamics simulations permit the exploration of biological phenomena in unparalleled detail. Advances in simulations are moving the atomic resolution descriptions of biological systems into the million-to-billion atom regime, in which numerous cell functions reside. In this opinion, we review the progress, driven by large-scale molecular dynamics simulations, in the study of viruses, ribosomes, bioenergetic systems, and other diverse applications. These examples highlight the utility of molecular dynamics simulations in the critical task of relating atomic detail to the function of supramolecular complexes, a task that cannot be achieved by smaller-scale simulations or existing experimental approaches alone. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pattern dynamics of the reaction-diffusion immune system.

PubMed

Zheng, Qianqian; Shen, Jianwei; Wang, Zhijie

2018-01-01

In this paper, we will investigate the effect of diffusion, which is ubiquitous in nature, on the immune system using a reaction-diffusion model in order to understand the dynamical behavior of complex patterns and control the dynamics of different patterns. Through control theory and linear stability analysis of local equilibrium, we obtain the optimal condition under which the system loses stability and a Turing pattern occurs. By combining mathematical analysis and numerical simulation, we show the possible patterns and how these patterns evolve. In addition, we establish a bridge between the complex patterns and the biological mechanism using the results from a previous study in Nature Cell Biology. The results in this paper can help us better understand the biological significance of the immune system.

From globally coupled maps to complex-systems biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaneko, Kunihiko, E-mail: kaneko@complex.c.u-tokyo.ac.jp

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, Daniel D.; Department of Biomedical Engineering, University of California Davis, Davis, CA; Villarreal, Fernando D.

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with amore » special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.« less

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

PubMed Central

Lewis, Daniel D.; Villarreal, Fernando D.; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems. PMID:25538941

Synthetic biology outside the cell: linking computational tools to cell-free systems.

PubMed

Lewis, Daniel D; Villarreal, Fernando D; Wu, Fan; Tan, Cheemeng

2014-01-01

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.

PubMed

Carey, Michelle; Ramírez, Juan Camilo; Wu, Shuang; Wu, Hulin

2018-07-01

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

PubMed

He, Fei; Murabito, Ettore; Westerhoff, Hans V

2016-04-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

Review of the fundamental theories behind small angle X-ray scattering, molecular dynamics simulations, and relevant integrated application

PubMed Central

Boldon, Lauren; Laliberte, Fallon; Liu, Li

2015-01-01

In this paper, the fundamental concepts and equations necessary for performing small angle X-ray scattering (SAXS) experiments, molecular dynamics (MD) simulations, and MD-SAXS analyses were reviewed. Furthermore, several key biological and non-biological applications for SAXS, MD, and MD-SAXS are presented in this review; however, this article does not cover all possible applications. SAXS is an experimental technique used for the analysis of a wide variety of biological and non-biological structures. SAXS utilizes spherical averaging to produce one- or two-dimensional intensity profiles, from which structural data may be extracted. MD simulation is a computer simulation technique that is used to model complex biological and non-biological systems at the atomic level. MD simulations apply classical Newtonian mechanics’ equations of motion to perform force calculations and to predict the theoretical physical properties of the system. This review presents several applications that highlight the ability of both SAXS and MD to study protein folding and function in addition to non-biological applications, such as the study of mechanical, electrical, and structural properties of non-biological nanoparticles. Lastly, the potential benefits of combining SAXS and MD simulations for the study of both biological and non-biological systems are demonstrated through the presentation of several examples that combine the two techniques. PMID:25721341

Biomolecular Deuteration for Neutron Structural Biology and Dynamics.

PubMed

Haertlein, Michael; Moulin, Martine; Devos, Juliette M; Laux, Valerie; Dunne, Orla; Forsyth, V Trevor

2016-01-01

Neutron scattering studies provide important information in structural biology that is not accessible using other approaches. The uniqueness of the technique, and its complementarity with X-ray scattering, is greatest when full use is made of deuterium labeling. The ability to produce tailor-made deuterium-labeled biological macromolecules allows neutron studies involving solution scattering, crystallography, reflection, and dynamics to be optimized in a manner that has major impact on the scope, quality, and throughput of work in these areas. Deuteration facilities have now been developed at many neutron centres throughout the world; these are having a crucial effect on neutron studies in the life sciences and on biologically related studies in soft matter. This chapter describes methods that have been developed for the efficient production of deuterium-labeled samples for a wide range of neutron scattering applications. Examples are given that illustrate the use of these samples for each of the main techniques. Perspectives for biological deuterium labeling are discussed in relation to developments at current facilities and those that are planned in the future. © 2016 Elsevier Inc. All rights reserved.

On the relationship between the dynamic behavior and nanoscale staggered structure of the bone

NASA Astrophysics Data System (ADS)

Qwamizadeh, Mahan; Zhang, Zuoqi; Zhou, Kun; Zhang, Yong Wei

2015-05-01

Bone, a typical load-bearing biological material, composed of ordinary base materials such as organic protein and inorganic mineral arranged in a hierarchical architecture, exhibits extraordinary mechanical properties. Up to now, most of previous studies focused on its mechanical properties under static loading. However, failure of the bone occurs often under dynamic loading. An interesting question is: Are the structural sizes and layouts of the bone related or even adapted to the functionalities demanded by its dynamic performance? In the present work, systematic finite element analysis was performed on the dynamic response of nanoscale bone structures under dynamic loading. It was found that for a fixed mineral volume fraction and unit cell area, there exists a nanoscale staggered structure at some specific feature size and layout which exhibits the fastest attenuation of stress waves. Remarkably, these specific feature sizes and layouts are in excellent agreement with those experimentally observed in the bone at the same scale, indicating that the structural size and layout of the bone at the nanoscale are evolutionarily adapted to its dynamic behavior. The present work points out the importance of dynamic effect on the biological evolution of load-bearing biological materials.

Workshop on High-Field NMR and Biological Applications

NASA Astrophysics Data System (ADS)

Scientists at the Pacific Northwest Laboratory have been working toward the establishment of a new Molecular Science Research Center (MSRC). The primary scientific thrust of this new research center is in the areas of theoretical chemistry, chemical dynamics, surface and interfacial science, and studies on the structure and interactions of biological macromolecules. The MSRC will provide important new capabilities for studies on the structure of biological macromolecules. The MSRC program includes several types of advanced spectroscopic techniques for molecular structure analysis, and a theory and modeling laboratory for molecular mechanics/dynamics calculations and graphics. It is the goal to closely integrate experimental and theoretical studies on macromolecular structure, and to join these research efforts with those of the molecular biological programs to provide new insights into the structure/function relationships of biological macromolecules. One of the areas of structural biology on which initial efforts in the MSRC will be focused is the application of high field, 2-D NMR to the study of biological macromolecules. First, there is interest in obtaining 3-D structural information on large proteins and oligonucleotides. Second, one of the primary objectives is to closely link theoretical approaches to molecular structure analysis with the results obtained in experimental research using NMR and other spectroscopies.

Supporting High School Student Accomplishment of Biology Content Using Interactive Computer-Based Curricular Case Studies

NASA Astrophysics Data System (ADS)

Oliver, Joseph Steve; Hodges, Georgia W.; Moore, James N.; Cohen, Allan; Jang, Yoonsun; Brown, Scott A.; Kwon, Kyung A.; Jeong, Sophia; Raven, Sara P.; Jurkiewicz, Melissa; Robertson, Tom P.

2017-11-01

Research into the efficacy of modules featuring dynamic visualizations, case studies, and interactive learning environments is reported here. This quasi-experimental 2-year study examined the implementation of three interactive computer-based instructional modules within a curricular unit covering cellular biology concepts in an introductory high school biology course. The modules featured dynamic visualizations and focused on three processes that underlie much of cellular biology: diffusion, osmosis, and filtration. Pre-tests and post-tests were used to assess knowledge growth across the unit. A mixture Rasch model analysis of the post-test data revealed two groups of students. In both years of the study, a large proportion of the students were classified as low-achieving based on their pre-test scores. The use of the modules in the Cell Unit in year 2 was associated with a much larger proportion of the students having transitioned to the high-achieving group than in year 1. In year 2, the same teachers taught the same concepts as year 1 but incorporated the interactive computer-based modules into the cell biology unit of the curriculum. In year 2, 67% of students initially classified as low-achieving were classified as high-achieving at the end of the unit. Examination of responses to assessments embedded within the modules as well as post-test items linked transition to the high-achieving group with correct responses to items that both referenced the visualization and the contextualization of that visualization within the module. This study points to the importance of dynamic visualization within contextualized case studies as a means to support student knowledge acquisition in biology.

How to train your microbe: methods for dynamically characterizing gene networks

PubMed Central

Castillo-Hair, Sebastian M.; Igoshin, Oleg A.; Tabor, Jeffrey J.

2015-01-01

Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways. PMID:25677419

A Large, First-Year, Introductory, Multi-Sectional Biological Concepts of Health Course Designed to Develop Skills and Enhance Deeper Learning

ERIC Educational Resources Information Center

Murrant, Coral L.; Dyck, David J.; Kirkland, James B.; Newton, Genevieve S.; Ritchie, Kerry L.; Tishinsky, Justine M.; Bettger, William J.; Richardson, Nicolette S.

2015-01-01

Large first-year biology classes, with their heavy emphasis on factual content, contribute to low student engagement and misrepresent the dynamic, interdisciplinary nature of biological science. We sought to redesign a course to deliver fundamental biology curriculum through the study of health, promote skills development, and encourage a deeper…

Speckle dynamics under ergodicity breaking

NASA Astrophysics Data System (ADS)

Sdobnov, Anton; Bykov, Alexander; Molodij, Guillaume; Kalchenko, Vyacheslav; Jarvinen, Topias; Popov, Alexey; Kordas, Krisztian; Meglinski, Igor

2018-04-01

Laser speckle contrast imaging (LSCI) is a well-known and versatile approach for the non-invasive visualization of flows and microcirculation localized in turbid scattering media, including biological tissues. In most conventional implementations of LSCI the ergodic regime is typically assumed valid. However, most composite turbid scattering media, especially biological tissues, are non-ergodic, containing a mixture of dynamic and static centers of light scattering. In the current study, we examined the speckle contrast in different dynamic conditions with the aim of assessing limitations in the quantitative interpretation of speckle contrast images. Based on a simple phenomenological approach, we introduced a coefficient of speckle dynamics to quantitatively assess the ratio of the dynamic part of a scattering medium to the static one. The introduced coefficient allows one to distinguish real changes in motion from the mere appearance of static components in the field of view. As examples of systems with static/dynamic transitions, thawing and heating of Intralipid samples were studied by the LSCI approach.

Concise Review: Stem Cell Population Biology: Insights from Hematopoiesis.

PubMed

MacLean, Adam L; Lo Celso, Cristina; Stumpf, Michael P H

2017-01-01

Stem cells are fundamental to human life and offer great therapeutic potential, yet their biology remains incompletely-or in cases even poorly-understood. The field of stem cell biology has grown substantially in recent years due to a combination of experimental and theoretical contributions: the experimental branch of this work provides data in an ever-increasing number of dimensions, while the theoretical branch seeks to determine suitable models of the fundamental stem cell processes that these data describe. The application of population dynamics to biology is amongst the oldest applications of mathematics to biology, and the population dynamics perspective continues to offer much today. Here we describe the impact that such a perspective has made in the field of stem cell biology. Using hematopoietic stem cells as our model system, we discuss the approaches that have been used to study their key properties, such as capacity for self-renewal, differentiation, and cell fate lineage choice. We will also discuss the relevance of population dynamics in models of stem cells and cancer, where competition naturally emerges as an influential factor on the temporal evolution of cell populations. Stem Cells 2017;35:80-88. © 2016 AlphaMed Press.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes

PubMed Central

Kuang, Zheng; Ji, Zhicheng

2018-01-01

Abstract Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes. PMID:29325176

Application of dynamic topic models to toxicogenomics data.

PubMed

Lee, Mikyung; Liu, Zhichao; Huang, Ruili; Tong, Weida

2016-10-06

All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data. A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms. We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

Salinity fluctuation influencing biological adaptation: growth dynamics and Na+ /K+ -ATPase activity in a euryhaline bacterium.

PubMed

Yang, Hao; Meng, Yang; Song, Youxin; Tan, Yalin; Warren, Alan; Li, Jiqiu; Lin, Xiaofeng

2017-07-01

Although salinity fluctuation is a prominent characteristic of many coastal ecosystems, its effects on biological adaptation have not yet been fully recognized. To test the salinity fluctuations on biological adaptation, population growth dynamics and Na + /K + -ATPase activity were investigated in the euryhaline bacterium Idiomarina sp. DYB, which was acclimated at different salinity exposure levels, exposure times, and shifts in direction of salinity. Results showed: (1) bacterial population growth dynamics and Na + /K + -ATPase activity changed significantly in response to salinity fluctuation; (2) patterns of variation in bacterial growth dynamics were related to exposure times, levels of salinity, and shifts in direction of salinity change; (3) significant tradeoffs were detected between growth rate (r) and carrying capacity (K) on the one hand, and Na + /K + -ATPase activity on the other; and (4) beneficial acclimation was confirmed in Idiomarina sp. DYB. In brief, this study demonstrated that salinity fluctuation can change the population growth dynamics, Na + /K + -ATPase activity, and tradeoffs between r, K, and Na + /K + -ATPase activity, thus facilitating bacterial adaption in a changing environment. These findings provide constructive information for determining biological response patterns to environmental change. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena

PubMed Central

Tohsato, Yukako; Ho, Kenneth H. L.; Kyoda, Koji; Onami, Shuichi

2016-01-01

Motivation: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis. Results: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus. The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis. Availability and Implementation: SSBD is accessible at http://ssbd.qbic.riken.jp. Contact: sonami@riken.jp PMID:27412095

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena.

PubMed

Tohsato, Yukako; Ho, Kenneth H L; Kyoda, Koji; Onami, Shuichi

2016-11-15

Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis. We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis. SSBD is accessible at http://ssbd.qbic.riken.jp CONTACT: sonami@riken.jp. © The Author 2016. Published by Oxford University Press.

Brain synchronization during perception of facial emotional expressions with natural and unnatural dynamics

PubMed Central

Volhard, Jakob; Müller, Viktor; Kaulard, Kathrin; Brick, Timothy R.; Wallraven, Christian; Lindenberger, Ulman

2017-01-01

Research on the perception of facial emotional expressions (FEEs) often uses static images that do not capture the dynamic character of social coordination in natural settings. Recent behavioral and neuroimaging studies suggest that dynamic FEEs (videos or morphs) enhance emotion perception. To identify mechanisms associated with the perception of FEEs with natural dynamics, the present EEG (Electroencephalography)study compared (i) ecologically valid stimuli of angry and happy FEEs with natural dynamics to (ii) FEEs with unnatural dynamics, and to (iii) static FEEs. FEEs with unnatural dynamics showed faces moving in a biologically possible but unpredictable and atypical manner, generally resulting in ambivalent emotional content. Participants were asked to explicitly recognize FEEs. Using whole power (WP) and phase synchrony (Phase Locking Index, PLI), we found that brain responses discriminated between natural and unnatural FEEs (both static and dynamic). Differences were primarily observed in the timing and brain topographies of delta and theta PLI and WP, and in alpha and beta WP. Our results support the view that biologically plausible, albeit atypical, FEEs are processed by the brain by different mechanisms than natural FEEs. We conclude that natural movement dynamics are essential for the perception of FEEs and the associated brain processes. PMID:28723957

Brain synchronization during perception of facial emotional expressions with natural and unnatural dynamics.

PubMed

Perdikis, Dionysios; Volhard, Jakob; Müller, Viktor; Kaulard, Kathrin; Brick, Timothy R; Wallraven, Christian; Lindenberger, Ulman

2017-01-01

Research on the perception of facial emotional expressions (FEEs) often uses static images that do not capture the dynamic character of social coordination in natural settings. Recent behavioral and neuroimaging studies suggest that dynamic FEEs (videos or morphs) enhance emotion perception. To identify mechanisms associated with the perception of FEEs with natural dynamics, the present EEG (Electroencephalography)study compared (i) ecologically valid stimuli of angry and happy FEEs with natural dynamics to (ii) FEEs with unnatural dynamics, and to (iii) static FEEs. FEEs with unnatural dynamics showed faces moving in a biologically possible but unpredictable and atypical manner, generally resulting in ambivalent emotional content. Participants were asked to explicitly recognize FEEs. Using whole power (WP) and phase synchrony (Phase Locking Index, PLI), we found that brain responses discriminated between natural and unnatural FEEs (both static and dynamic). Differences were primarily observed in the timing and brain topographies of delta and theta PLI and WP, and in alpha and beta WP. Our results support the view that biologically plausible, albeit atypical, FEEs are processed by the brain by different mechanisms than natural FEEs. We conclude that natural movement dynamics are essential for the perception of FEEs and the associated brain processes.

Hydrogen-bond dynamics at the bio-water interface in hydrated proteins: a molecular-dynamics study.

PubMed

Nandi, Prithwish K; English, Niall J; Futera, Zdenek; Benedetto, Antonio

2016-12-21

Water is fundamental to the biochemistry of enzymes. It is well known that without a minimum amount of water, enzymes are not biologically active. Bare minimal solvation for biological function corresponds to about a single layer of water covering enzymes' surfaces. Many contradictory studies on protein-hydration-water-coupled dynamics have been published in recent decades. Following prevailing wisdom, a dynamical crossover in hydration water (at around 220 K for hydrated lysozymes) can trigger larger-amplitude motions of the protein, activating, in turn, biological functions. Here, we present a molecular-dynamics-simulation study on a solvated model protein (hen egg-white lysozyme), in which we determine, inter alia, the relaxation dynamics of the hydrogen-bond network between the protein and its hydration water molecules on a residue-per-residue basis. Hydrogen-bond breakage/formation kinetics is rather heterogeneous in temperature dependence (due to the heterogeneity of the free-energy surface), and is driven by the magnitude of thermal motions of various different protein residues which provide enough thermal energy to overcome energy barriers to rupture their respective hydrogen bonds with water. In particular, arginine residues exhibit the highest number of such hydrogen bonds at low temperatures, losing almost completely such bonding above 230 K. This suggests that hydration water's dynamical crossover, observed experimentally for hydrated lysozymes at ∼220 K, lies not at the origin of the protein residues' larger-amplitude motions, but rather arises as a consequence thereof. This highlights the need for new experimental investigations, and new interpretations to link protein dynamics to functions, in the context of key interrelationships with the solvation layer.

Spectroscopy of Isolated Prebiotic Nucleobases

NASA Technical Reports Server (NTRS)

Svadlenak, Nathan; Callahan, Michael P.; Ligare, Marshall; Gulian, Lisa; Gengeliczki, Zsolt; Nachtigallova, Dana; Hobza, Pavel; deVries, Mattanjah

2011-01-01

We use multiphoton ionization and double resonance spectroscopy to study the excited state dynamics of biologically relevant molecules as well as prebiotic nucleobases, isolated in the gas phase. Molecules that are biologically relevant to life today tend to exhibit short excited state lifetimes compared to similar but non-biologically relevant analogs. The mechanism is internal conversion, which may help protect the biologically active molecules from UV damage. This process is governed by conical intersections that depend very strongly on molecular structure. Therefore we have studied purines and pyrimidines with systematic variations of structure, including substitutions, tautomeric forms, and cluster structures that represent different base pair binding motifs. These structural variations also include possible alternate base pairs that may shed light on prebiotic chemistry. With this in mind we have begun to probe the ultrafast dynamics of molecules that exhibit very short excited states and search for evidence of internal conversions.

Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level

NASA Astrophysics Data System (ADS)

Thakar, Juilee; Albert, Réka

The following sections are included: * Introduction * Boolean Network Concepts and History * Extensions of the Classical Boolean Framework * Boolean Inference Methods and Examples in Biology * Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology * Conclusions * References

DYNAMICS OF EXTRACELLULAR SIGNAL-REGULATED KINASE (ERK) ACTIVATION IN DEVELOPING CEREBELLAR GRANULE CELLS (CGC): A SYSTEMS BIOLOGY-ORIENTED STUDY

EPA Science Inventory

The objective of this study was to 1) characterize the dynamics of ERK activation in response to BDNF and NMDA; 2) use computational models to promote understanding of the signaling network underlying ERK activation.

Coarse-Grained Models Reveal Functional Dynamics – II. Molecular Dynamics Simulation at the Coarse-Grained Level – Theories and Biological Applications

PubMed Central

Chng, Choon-Peng; Yang, Lee-Wei

2008-01-01

Molecular dynamics (MD) simulation has remained the most indispensable tool in studying equilibrium/non-equilibrium conformational dynamics since its advent 30 years ago. With advances in spectroscopy accompanying solved biocomplexes in growing sizes, sampling their dynamics that occur at biologically interesting spatial/temporal scales becomes computationally intractable; this motivated the use of coarse-grained (CG) approaches. CG-MD models are used to study folding and conformational transitions in reduced resolution and can employ enlarged time steps due to the absence of some of the fastest motions in the system. The Boltzmann-Inversion technique, heavily used in parameterizing these models, provides a smoothed-out effective potential on which molecular conformation evolves at a faster pace thus stretching simulations into tens of microseconds. As a result, a complete catalytic cycle of HIV-1 protease or the assembly of lipid-protein mixtures could be investigated by CG-MD to gain biological insights. In this review, we survey the theories developed in recent years, which are categorized into Folding-based and Molecular-Mechanics-based. In addition, physical bases in the selection of CG beads/time-step, the choice of effective potentials, representation of solvent, and restoration of molecular representations back to their atomic details are systematically discussed. PMID:19812774

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

PubMed

Villaverde, Alejandro F; Banga, Julio R

2017-11-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.

Dynamic Biological Functioning Important for Simulating and Stabilizing Ocean Biogeochemistry

NASA Astrophysics Data System (ADS)

Buchanan, P. J.; Matear, R. J.; Chase, Z.; Phipps, S. J.; Bindoff, N. L.

2018-04-01

The biogeochemistry of the ocean exerts a strong influence on the climate by modulating atmospheric greenhouse gases. In turn, ocean biogeochemistry depends on numerous physical and biological processes that change over space and time. Accurately simulating these processes is fundamental for accurately simulating the ocean's role within the climate. However, our simulation of these processes is often simplistic, despite a growing understanding of underlying biological dynamics. Here we explore how new parameterizations of biological processes affect simulated biogeochemical properties in a global ocean model. We combine 6 different physical realizations with 6 different biogeochemical parameterizations (36 unique ocean states). The biogeochemical parameterizations, all previously published, aim to more accurately represent the response of ocean biology to changing physical conditions. We make three major findings. First, oxygen, carbon, alkalinity, and phosphate fields are more sensitive to changes in the ocean's physical state. Only nitrate is more sensitive to changes in biological processes, and we suggest that assessment protocols for ocean biogeochemical models formally include the marine nitrogen cycle to assess their performance. Second, we show that dynamic variations in the production, remineralization, and stoichiometry of organic matter in response to changing environmental conditions benefit the simulation of ocean biogeochemistry. Third, dynamic biological functioning reduces the sensitivity of biogeochemical properties to physical change. Carbon and nitrogen inventories were 50% and 20% less sensitive to physical changes, respectively, in simulations that incorporated dynamic biological functioning. These results highlight the importance of a dynamic biology for ocean properties and climate.

NMR-based Metabolomics Applications in Biological and Environmental Science

EPA Science Inventory

As a complimentary tool to other omics platforms, metabolomics is increasingly being used bybiologists to study the dynamic response of biological systems (cells, tissues, or wholeorganisms) under diverse physiological or pathological conditions. Metabolomics deals with the quali...

Dynamic stability of running: The effects of speed and leg amputations on the maximal Lyapunov exponent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Look, Nicole; Arellano, Christopher J.; Grabowski, Alena M.

2013-12-15

In this paper, we study dynamic stability during running, focusing on the effects of speed, and the use of a leg prosthesis. We compute and compare the maximal Lyapunov exponents of kinematic time-series data from subjects with and without unilateral transtibial amputations running at a wide range of speeds. We find that the dynamics of the affected leg with the running-specific prosthesis are less stable than the dynamics of the unaffected leg and also less stable than the biological legs of the non-amputee runners. Surprisingly, we find that the center-of-mass dynamics of runners with two intact biological legs are slightlymore » less stable than those of runners with amputations. Our results suggest that while leg asymmetries may be associated with instability, runners may compensate for this effect by increased control of their center-of-mass dynamics.« less

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

PubMed

Kuang, Zheng; Ji, Zhicheng; Boeke, Jef D; Ji, Hongkai

2018-01-09

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Dynamics of water in strawberry and red onion as studied by dielectric spectroscopy

NASA Astrophysics Data System (ADS)

Jansson, H.; Huldt, C.; Bergman, R.; Swenson, J.

2005-01-01

We have investigated the microscopic dynamics of strawberry and red onion by means of broadband dielectric spectroscopy. In contrast to most of the previous experiments on carbohydrate-rich biological materials, which have mainly considered the more global dynamics of the “biological matrix,” we are here focusing on the microscopic dynamics of mainly the associated water. The results for both strawberry and red onion show that the imaginary part of the permittivity contains one conductivity term and a clear dielectric loss peak, which was found to be similar to the strongest relaxation process of water in carbohydrate solutions. The temperature dependence of the relaxation process was analyzed for different water content. The relaxation process slows down, and its temperature dependence becomes more non-Arrhenius, with decreasing water content. The reason for this is most likely that, on average, the water molecules interact more strongly with carbohydrates and other biological materials at low water content, and the dynamical properties of this biological matrix changes substantially with increasing temperature (from an almost rigid matrix where the water is basically unable to perform long-range diffusion due to confinement effects, to a dynamic matrix with no static confinement effects), which also changes (i.e., reduces) the activation energy of the relaxation process with increasing temperature (i.e., causes a non-Arrhenius temperature dependence). This further changes the conductivity from mainly polarization effects at low temperatures, due to hindered ionic motions, to long-range diffusivity at T>250K . Thus, around this temperature ions in the carbohydrate solution no longer get stuck in confined cavities, since the motion of the biological matrix “opens up” the cavities and the ions are then able to perform long-range migration.

Mode localization in the cooperative dynamics of protein recognition

NASA Astrophysics Data System (ADS)

Copperman, J.; Guenza, M. G.

2016-07-01

The biological function of proteins is encoded in their structure and expressed through the mediation of their dynamics. This paper presents a study on the correlation between local fluctuations, binding, and biological function for two sample proteins, starting from the Langevin Equation for Protein Dynamics (LE4PD). The LE4PD is a microscopic and residue-specific coarse-grained approach to protein dynamics, which starts from the static structural ensemble of a protein and predicts the dynamics analytically. It has been shown to be accurate in its prediction of NMR relaxation experiments and Debye-Waller factors. The LE4PD is solved in a set of diffusive modes which span a vast range of time scales of the protein dynamics, and provides a detailed picture of the mode-dependent localization of the fluctuation as a function of the primary structure of the protein. To investigate the dynamics of protein complexes, the theory is implemented here to treat the coarse-grained dynamics of interacting macromolecules. As an example, calculations of the dynamics of monomeric and dimerized HIV protease and the free Insulin Growth Factor II Receptor (IGF2R) domain 11 and its IGF2R:IGF2 complex are presented. Either simulation-derived or experimentally measured NMR conformers are used as input structural ensembles to the theory. The picture that emerges suggests a dynamical heterogeneous protein where biologically active regions provide energetically comparable conformational states that are trapped by a reacting partner in agreement with the conformation-selection mechanism of binding.

Biological Motion Preference in Humans at Birth: Role of Dynamic and Configural Properties

ERIC Educational Resources Information Center

Bardi, Lara; Regolin, Lucia; Simion, Francesca

2011-01-01

The present study addresses the hypothesis that detection of biological motion is an intrinsic capacity of the visual system guided by a non-species-specific predisposition for the pattern of vertebrate movement and investigates the role of global vs. local information in biological motion detection. Two-day-old babies exposed to a biological…

Fast wavefront optimization for focusing through biological tissue (Conference Presentation)

NASA Astrophysics Data System (ADS)

Blochet, Baptiste; Bourdieu, Laurent; Gigan, Sylvain

2017-02-01

The propagation of light in biological tissues is rapidly dominated by multiple scattering: ballistic light is exponentially attenuated, which limits the penetration depth of conventional microscopy techniques. For coherent light, the recombination of the different scattered paths creates a complex interference: speckle. Recently, different wavefront shaping techniques have been developed to coherently manipulate the speckle. It opens the possibility to focus light through complex media and ultimately to image in them, provided however that the medium can be considered as stationary. We have studied the possibility to focus in and through time-varying biological tissues. Their intrinsic temporal dynamics creates a fast decorrelation of the speckle pattern. Therefore, focusing through biological tissues requires fast wavefront shaping devices, sensors and algorithms. We have investigated the use of a MEMS-based spatial light modulator (SLM) and a fast photodetector, combined with FPGA electronics to implement a closed-loop optimization. Our optimization process is just limited by the temporal dynamics of the SLM (200µs) and the computation time (45µs), thus corresponding to a rate of 4 kHz. To our knowledge, it's the fastest closed loop optimization using phase modulators. We have studied the focusing through colloidal solutions of TiO2 particles in glycerol, allowing tunable temporal stability, and scattering properties similar to biological tissues. We have shown that our set-up fulfills the required characteristics (speed, enhancement) to focus through biological tissues. We are currently investigating the focusing through acute rat brain slices and the memory effect in dynamic scattering media.

Plankton Production Biology

DTIC Science & Technology

2013-09-30

kind. It will permit the study of stage-specific population dynamics (growth rate, production, mortality) of copepod larvae in mixed populations...will be picked up. 4. The translation and distribution of Sazhina’s keys will permit the study of stage-specific population dynamics (growth rate...of the 1987 Russian original, edited by K. Banse) Petipa, T.S. (ed.) (1986) Ecological Systems in Dynamically Active Zones of the Indian Ocean

Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature

NASA Astrophysics Data System (ADS)

Ma, Zhanshan (Sam)

In evolutionary computing (EC), population size is one of the critical parameters that a researcher has to deal with. Hence, it was no surprise that the pioneers of EC, such as De Jong (1975) and Holland (1975), had already studied the population sizing from the very beginning of EC. What is perhaps surprising is that more than three decades later, we still largely depend on the experience or ad-hoc trial-and-error approach to set the population size. For example, in a recent monograph, Eiben and Smith (2003) indicated: "In almost all EC applications, the population size is constant and does not change during the evolutionary search." Despite enormous research on this issue in recent years, we still lack a well accepted theory for population sizing. In this paper, I propose to develop a population dynamics theory forEC with the inspiration from the population dynamics theory of biological populations in nature. Essentially, the EC population is considered as a dynamic system over time (generations) and space (search space or fitness landscape), similar to the spatial and temporal dynamics of biological populations in nature. With this conceptual mapping, I propose to 'transplant' the biological population dynamics theory to EC via three steps: (i) experimentally test the feasibility—whether or not emulating natural population dynamics improves the EC performance; (ii) comparatively study the underlying mechanisms—why there are improvements, primarily via statistical modeling analysis; (iii) conduct theoretical analysis with theoretical models such as percolation theory and extended evolutionary game theory that are generally applicable to both EC and natural populations. This article is a summary of a series of studies we have performed to achieve the general goal [27][30]-[32]. In the following, I start with an extremely brief introduction on the theory and models of natural population dynamics (Sections 1 & 2). In Sections 4 to 6, I briefly discuss three categories of population dynamics models: deterministic modeling with Logistic chaos map as an example, stochastic modeling with spatial distribution patterns as an example, as well as survival analysis and extended evolutionary game theory (EEGT) modeling. Sample experiment results with Genetic algorithms (GA) are presented to demonstrate the applications of these models. The proposed EC population dynamics approach also makes survival selection largely unnecessary or much simplified since the individuals are naturally selected (controlled) by the mathematical models for EC population dynamics.

Applications of NMR-based metabolomics in biological and environmental research

EPA Science Inventory

As a complimentary tool to other omics platforms, metabolomics is increasingly being used by biologists to study the dynamic response of biological systems (cells, tissues, or whole organisms) under diverse physiological or pathological conditions. Metabolomics deals with the qu...

Ultrafast dynamics and Raman imaging of metal complexes of tetrasulphonated phthalocyanines in human cancerous and noncancerous breast tissues

NASA Astrophysics Data System (ADS)

Abramczyk, H.; Jarota, A.; Brozek-Pluska, B.; Tondusson, M.; Freysz, E.; Musial, J.; Kordek, R.

2013-03-01

A promising material in medicine, electronics, optoelectronics, electrochemistry, catalysis and photophysics, Al(III) phthalocyanine chloride tetrasulfonic acid (AlPcS4) is investigated at biological interfaces of human breast tissue by means of time-resolved spectroscopy. The nature of fast processes and pathways of the competing relaxation mechanisms from the initially excited electronic states of a photosensitizer at biological interfaces have been studied. Comparison between the results in the biological environment of the breast tissues and in aqueous solutions demonstrates that the photochemical mechanisms become dramatically different. The presented results provide a basis for a substantial revision of the commonly accepted assumption that photochemistry of the bulk properties of photosensitizers in solutions can be translated to the interfacial region. First, in solution the dynamics of the photosensitizer is much slower than that at the biological interface. Second, the dynamics of the photosensitizer in the cancerous tissue is dramatically slower than that in noncancerous tissue. Our results provide evidence that molecular structures responsible for harvesting of the light energy in biological tissue find their ways for a recovery through some special features of the potential energy surfaces such as conical intersections, which facilitate the rate of radiationless transitions and maintain the photostability in the biological systems.

Informations in Models of Evolutionary Dynamics

NASA Astrophysics Data System (ADS)

Rivoire, Olivier

2016-03-01

Biological organisms adapt to changes by processing informations from different sources, most notably from their ancestors and from their environment. We review an approach to quantify these informations by analyzing mathematical models of evolutionary dynamics and show how explicit results are obtained for a solvable subclass of these models. In several limits, the results coincide with those obtained in studies of information processing for communication, gambling or thermodynamics. In the most general case, however, information processing by biological populations shows unique features that motivate the analysis of specific models.

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

PubMed Central

Awan, Imtiaz; Aziz, Wajid; Habib, Nazneen; Alowibdi, Jalal S.; Saeed, Sharjil; Nadeem, Malik Sajjad Ahmed; Shah, Syed Ahsin Ali

2018-01-01

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features. PMID:29771977

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis.

PubMed

Awan, Imtiaz; Aziz, Wajid; Shah, Imran Hussain; Habib, Nazneen; Alowibdi, Jalal S; Saeed, Sharjil; Nadeem, Malik Sajjad Ahmed; Shah, Syed Ahsin Ali

2018-01-01

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation

PubMed Central

2017-01-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability. PMID:29186132

A swinging seesaw as a novel model mechanism for time-dependent hormesis under dose-dependent stimulatory and inhibitory effects: A case study on the toxicity of antibacterial chemicals to Aliivibrio fischeri.

PubMed

Sun, Haoyu; Calabrese, Edward J; Zheng, Min; Wang, Dali; Pan, Yongzheng; Lin, Zhifen; Liu, Ying

2018-08-01

Hormesis occurs frequently in broadly ranging biological areas (e.g. plant biology, microbiology, biogerontology), toxicology, pharmacology and medicine. While numerous mechanisms (e.g. receptor and pathway mediated pathway responses) account for stimulatory and inhibitory features of hormetic dose responses, the vast majority emphasizes the inclusion of many doses but only one timepoint or use of a single optimized dose that is assessed over a broad range of timepoints. In this paper, a toxicity study was designed using a large number of properly spaced doses with responses determined over a large number of timepoints, which could help us reveal the underlying mechanism of hormesis. We present the results of a dose-time-response study on hormesis using five antibacterial chemicals on the bioluminescence of Aliivibrio fischeri, measuring expression of protein mRNA based on quorum sensing, simulating bioluminescent reaction and analyzing toxic actions of test chemicals. The findings show dose-time-dependent responses conforming to the hormetic dose-response model, while revealing unique response dynamics between agent induced stimulatory and inhibitory effects within bacterial growth phase dynamics. These dynamic dose-time features reveal a type of biological seesaw model that integrates stimulatory and inhibitory responses within unique growth phase, dose and time features, which has faultlessly explained the time-dependent hormetic phenomenon induced by five antibacterial chemicals (characterized by low-dose stimulation and high-dose inhibition). This study offers advances in understanding cellular dynamics, the biological integration of diverse and opposing responses and their role in evolutionary adaptive strategies to chemicals, which can provide new insight into the mechanistic investigation of hormesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Decompositions of large-scale biological systems based on dynamical properties.

PubMed

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

2012-01-01

Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties. The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems. Original heuristics for the methods investigated are described in the article. altafini@sissa.it

Dynamism & Detail

ERIC Educational Resources Information Center

Flannery, Maura C.

2004-01-01

New material discovered in the study of cell research is presented for the benefit of biology teachers. Huge amounts of data are being generated in fields like cellular dynamics, and it is felt that people's understanding of the cell is becoming much more complex and detailed.

Evaluation of a grid based molecular dynamics approach for polypeptide simulations.

PubMed

Merelli, Ivan; Morra, Giulia; Milanesi, Luciano

2007-09-01

Molecular dynamics is very important for biomedical research because it makes possible simulation of the behavior of a biological macromolecule in silico. However, molecular dynamics is computationally rather expensive: the simulation of some nanoseconds of dynamics for a large macromolecule such as a protein takes very long time, due to the high number of operations that are needed for solving the Newton's equations in the case of a system of thousands of atoms. In order to obtain biologically significant data, it is desirable to use high-performance computation resources to perform these simulations. Recently, a distributed computing approach based on replacing a single long simulation with many independent short trajectories has been introduced, which in many cases provides valuable results. This study concerns the development of an infrastructure to run molecular dynamics simulations on a grid platform in a distributed way. The implemented software allows the parallel submission of different simulations that are singularly short but together bring important biological information. Moreover, each simulation is divided into a chain of jobs to avoid data loss in case of system failure and to contain the dimension of each data transfer from the grid. The results confirm that the distributed approach on grid computing is particularly suitable for molecular dynamics simulations thanks to the elevated scalability.

Exploration of the Energy Landscape of Acetylcholinesterase by Molecular Dynamics Simulation.

NASA Astrophysics Data System (ADS)

McCammon, J. Andrew

2002-03-01

Proteins have rough energy landscapes. Often more states than just the ground state are occupied and have biological functions. It is essential to study these conformational substates and the dynamical transitions among them. Acetylcholinesterase (AChE) is an important enzyme that has biological functions including the termination of synaptic transmission signals. X-ray structures show that it has an active site that is accessible only via a long and narrow channel from its surface. Therefore the fact that acetylcholine and larger ligands can reach the active site is believed to reflect the protein's structural fluctuation. We carried out long molecular dynamics simulations to investigate the dynamics of AChE and its relation to biological function, and compared our results with experiments. The results reveal several "doors" that open intermittantly between the active site and the surface. Instead of having simple exponential decay correlation functions, the time series of these channels reveal complex, fractal gating between conformations. We also compared the AChE dynamics data with those from an AchE-fasciculin complex. (Fasciculin is a small protein that is a natural inhibitor of AChE.) The results show remarkable effects of the protein-protein interaction, including allosteric and dynamical inhibition by fasciculin besides direct steric blocking. More information and images can be found at http://mccammon.ucsd.edu

Support for ICES International Symposium: Recruitment Dynamics of Exploited Marine Populations: Physical-biological Interactions

DTIC Science & Technology

1997-09-30

Environmental Science ,Chesapeake Biological Laboratory,PO Box 38,Solomons,MD,20688 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...DYNAMICS OF EXPLOITED MARINE POPULATIONS: PHYSICAL-BIOLOGICAL INTERACTIONS Michael J. Fogarty University of Maryland Center for Environmental Science Chesapeake

A consistent S-Adenosylmethionine force field improved by dynamic Hirshfeld-I atomic charges for biomolecular simulation

NASA Astrophysics Data System (ADS)

Saez, David Adrian; Vöhringer-Martinez, Esteban

2015-10-01

S-Adenosylmethionine (AdoMet) is involved in many biological processes as cofactor in enzymes transferring its sulfonium methyl group to various substrates. Additionally, it is used as drug and nutritional supplement to reduce the pain in osteoarthritis and against depression. Due to the biological relevance of AdoMet it has been part of various computational simulation studies and will also be in the future. However, to our knowledge no rigorous force field parameter development for its simulation in biological systems has been reported. Here, we use electronic structure calculations combined with molecular dynamics simulations in explicit solvent to develop force field parameters compatible with the AMBER99 force field. Additionally, we propose new dynamic Hirshfeld-I atomic charges which are derived from the polarized electron density of AdoMet in aqueous solution to describe its electrostatic interactions in biological systems. The validation of the force field parameters and the atomic charges is performed against experimental interproton NOE distances of AdoMet in aqueous solution and crystal structures of AdoMet in the cavity of three representative proteins.

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

PubMed Central

Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R.

2017-01-01

Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling. PMID:28813442

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.

PubMed

Penas, David R; Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R

2017-01-01

We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.

Observability of Boolean multiplex control networks

NASA Astrophysics Data System (ADS)

Wu, Yuhu; Xu, Jingxue; Sun, Xi-Ming; Wang, Wei

2017-04-01

Boolean multiplex (multilevel) networks (BMNs) are currently receiving considerable attention as theoretical arguments for modeling of biological systems and system level analysis. Studying control-related problems in BMNs may not only provide new views into the intrinsic control in complex biological systems, but also enable us to develop a method for manipulating biological systems using exogenous inputs. In this article, the observability of the Boolean multiplex control networks (BMCNs) are studied. First, the dynamical model and structure of BMCNs with control inputs and outputs are constructed. By using of Semi-Tensor Product (STP) approach, the logical dynamics of BMCNs is converted into an equivalent algebraic representation. Then, the observability of the BMCNs with two different kinds of control inputs is investigated by giving necessary and sufficient conditions. Finally, examples are given to illustrate the efficiency of the obtained theoretical results.

Evolutionary game based control for biological systems with applications in drug delivery.

PubMed

Li, Xiaobo; Lenaghan, Scott C; Zhang, Mingjun

2013-06-07

Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nonlinear dynamics in the study of birdsong

NASA Astrophysics Data System (ADS)

Mindlin, Gabriel B.

2017-09-01

Birdsong, a rich and complex behavior, is a stellar model to understand a variety of biological problems, from motor control to learning. It also enables us to study how behavior emerges when a nervous system, a biomechanical device and the environment interact. In this review, I will show that many questions in the field can benefit from the approach of nonlinear dynamics, and how birdsong can inspire new directions for research in dynamics.

Dynamics of macroautophagy: Modeling and oscillatory behavior

NASA Astrophysics Data System (ADS)

Han, Kyungreem; Kwon, Hyun Woong; Kang, Hyuk; Kim, Jinwoong; Lee, Myung-Shik; Choi, M. Y.

2012-02-01

We propose a model for macroautophagy and study the resulting dynamics of autophagy in a system isolated from its extra-cellular environment. It is found that the intracellular concentrations of autophagosomes and autolysosomes display oscillations with their own natural frequencies. Such oscillatory behaviors, which are interrelated to the dynamics of intracellular ATP, amino acids, and proteins, are consistent with the very recent biological observations. Implications of this theoretical study of autophagy are discussed, with regard to the possibility of guiding molecular studies of autophagy.

Biomolecular Modeling in a Process Dynamics and Control Course

ERIC Educational Resources Information Center

Gray, Jeffrey J.

2006-01-01

I present modifications to the traditional course entitled, "Process dynamics and control," which I renamed "Modeling, dynamics, and control of chemical and biological processes." Additions include the central dogma of biology, pharmacokinetic systems, population balances, control of gene transcription, and large­-scale…

Dynamic Open Inquiry Performances of High-School Biology Students

ERIC Educational Resources Information Center

Zion, Michal; Sadeh, Irit

2010-01-01

In examining open inquiry projects among high-school biology students, we found dynamic inquiry performances expressed in two criteria: "changes occurring during inquiry" and "procedural understanding". Characterizing performances in a dynamic open inquiry project can shed light on both the procedural and epistemological…

Entropy for the Complexity of Physiological Signal Dynamics.

PubMed

Zhang, Xiaohua Douglas

2017-01-01

Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.

Sensitivity analysis of dynamic biological systems with time-delays.

PubMed

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2010-10-15

Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary. We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention. By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

PubMed Central

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-01-01

Background Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. Results We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. Conclusion By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions. PMID:18570683

Systematic reconstruction of TRANSPATH data into cell system markup language.

PubMed

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-06-23

Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.

G-quadruplex dynamics.

PubMed

Harkness, Robert W; Mittermaier, Anthony K

2017-11-01

G-quadruplexes (GQs) are four-stranded nucleic acid secondary structures formed by guanosine (G)-rich DNA and RNA sequences. It is becoming increasingly clear that cellular processes including gene expression and mRNA translation are regulated by GQs. GQ structures have been extensively characterized, however little attention to date has been paid to their conformational dynamics, despite the fact that many biological GQ sequences populate multiple structures of similar free energies, leading to an ensemble of exchanging conformations. The impact of these dynamics on biological function is currently not well understood. Recently, structural dynamics have been demonstrated to entropically stabilize GQ ensembles, potentially modulating gene expression. Transient, low-populated states in GQ ensembles may additionally regulate nucleic acid interactions and function. This review will underscore the interplay of GQ dynamics and biological function, focusing on several dynamic processes for biological GQs and the characterization of GQ dynamics by nuclear magnetic resonance (NMR) spectroscopy in conjunction with other biophysical techniques. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

6,7-dimethoxy-coumarin as a probe of hydration dynamics in biologically relevant systems

NASA Astrophysics Data System (ADS)

Ghose, Avisek; Amaro, Mariana; Kovaricek, Petr; Hof, Martin; Sykora, Jan

2018-04-01

Coumarin derivatives are well known fluorescence reporters for investigating biological systems due to their strong micro-environment sensitivity. Despite having wide range of environment sensitive fluorescence probes, the potential of 6,7-dimethoxy-coumarin has not been studied extensively so far. With a perspective of its use in protein studies, namely using the unnatural amino acid technology or as a substrate for hydrolase enzymes, we study acetyloxymethyl-6,7-dimethoxycoumarin (Ac-DMC). We investigate the photophysics and hydration dynamics of this dye in aerosol-OT (AOT) reverse micelles at various water contents using the time dependent fluorescence shift (TDFS) method. The TDFS response in AOT reverse micelles from water/surfactant ratio of 0 to 20 confirms its sensitivity towards the hydration and mobility of its microenvironment. Moreover, we show that the fluorophore can be efficiently quenched by halide ions. Hence, we conclude that the 6,7-dimethoxy-methylcoumarin fluorophore is useful for studying hydration parameters in biologically relevant systems.

Inferring Biological Structures from Super-Resolution Single Molecule Images Using Generative Models

PubMed Central

Maji, Suvrajit; Bruchez, Marcel P.

2012-01-01

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information. PMID:22629348

Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.

PubMed

Linel, Patrice; Wu, Shuang; Deng, Nan; Wu, Hulin

2014-10-01

Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

Bayesian integration of position and orientation cues in perception of biological and non-biological forms.

PubMed

Thurman, Steven M; Lu, Hongjing

2014-01-01

Visual form analysis is fundamental to shape perception and likely plays a central role in perception of more complex dynamic shapes, such as moving objects or biological motion. Two primary form-based cues serve to represent the overall shape of an object: the spatial position and the orientation of locations along the boundary of the object. However, it is unclear how the visual system integrates these two sources of information in dynamic form analysis, and in particular how the brain resolves ambiguities due to sensory uncertainty and/or cue conflict. In the current study, we created animations of sparsely-sampled dynamic objects (human walkers or rotating squares) comprised of oriented Gabor patches in which orientation could either coincide or conflict with information provided by position cues. When the cues were incongruent, we found a characteristic trade-off between position and orientation information whereby position cues increasingly dominated perception as the relative uncertainty of orientation increased and vice versa. Furthermore, we found no evidence for differences in the visual processing of biological and non-biological objects, casting doubt on the claim that biological motion may be specialized in the human brain, at least in specific terms of form analysis. To explain these behavioral results quantitatively, we adopt a probabilistic template-matching model that uses Bayesian inference within local modules to estimate object shape separately from either spatial position or orientation signals. The outputs of the two modules are integrated with weights that reflect individual estimates of subjective cue reliability, and integrated over time to produce a decision about the perceived dynamics of the input data. Results of this model provided a close fit to the behavioral data, suggesting a mechanism in the human visual system that approximates rational Bayesian inference to integrate position and orientation signals in dynamic form analysis.

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information. PMID:25162401

Computational modeling of ion transport through nanopores.

PubMed

Modi, Niraj; Winterhalter, Mathias; Kleinekathöfer, Ulrich

2012-10-21

Nanoscale pores are ubiquitous in biological systems while artificial nanopores are being fabricated for an increasing number of applications. Biological pores are responsible for the transport of various ions and substrates between the different compartments of biological systems separated by membranes while artificial pores are aimed at emulating such transport properties. As an experimental method, electrophysiology has proven to be an important nano-analytical tool for the study of substrate transport through nanopores utilizing ion current measurements as a probe for the detection. Independent of the pore type, i.e., biological or synthetic, and objective of the study, i.e., to model cellular processes of ion transport or electrophysiological experiments, it has become increasingly important to understand the dynamics of ions in nanoscale confinements. To this end, numerical simulations have established themselves as an indispensable tool to decipher ion transport processes through biological as well as artificial nanopores. This article provides an overview of different theoretical and computational methods to study ion transport in general and to calculate ion conductance in particular. Potential new improvements in the existing methods and their applications are highlighted wherever applicable. Moreover, representative examples are given describing the ion transport through biological and synthetic nanopores as well as the high selectivity of ion channels. Special emphasis is placed on the usage of molecular dynamics simulations which already have demonstrated their potential to unravel ion transport properties at an atomic level.

Biological Dynamics Markup Language (BDML): an open format for representing quantitative biological dynamics data

PubMed Central

Kyoda, Koji; Tohsato, Yukako; Ho, Kenneth H. L.; Onami, Shuichi

2015-01-01

Motivation: Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats. Results: We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis. Availability and implementation: A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/. Contact: sonami@riken.jp Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:25414366

Biological Dynamics Markup Language (BDML): an open format for representing quantitative biological dynamics data.

PubMed

Kyoda, Koji; Tohsato, Yukako; Ho, Kenneth H L; Onami, Shuichi

2015-04-01

Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats. We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis. A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Dynamics of intracellular processes in live-cell systems unveiled by fluorescence correlation microscopy.

PubMed

González Bardeci, Nicolás; Angiolini, Juan Francisco; De Rossi, María Cecilia; Bruno, Luciana; Levi, Valeria

2017-01-01

Fluorescence fluctuation-based methods are non-invasive microscopy tools especially suited for the study of dynamical aspects of biological processes. These methods examine spontaneous intensity fluctuations produced by fluorescent molecules moving through the small, femtoliter-sized observation volume defined in confocal and multiphoton microscopes. The quantitative analysis of the intensity trace provides information on the processes producing the fluctuations that include diffusion, binding interactions, chemical reactions and photophysical phenomena. In this review, we present the basic principles of the most widespread fluctuation-based methods, discuss their implementation in standard confocal microscopes and briefly revise some examples of their applications to address relevant questions in living cells. The ultimate goal of these methods in the Cell Biology field is to observe biomolecules as they move, interact with targets and perform their biological action in the natural context. © 2016 IUBMB Life, 69(1):8-15, 2017. © 2016 International Union of Biochemistry and Molecular Biology.

Microendophenotypes of psychiatric disorders: phenotypes of psychiatric disorders at the level of molecular dynamics, synapses, neurons, and neural circuits.

PubMed

Kida, S; Kato, T

2015-01-01

Psychiatric disorders are caused not only by genetic factors but also by complicated factors such as environmental ones. Moreover, environmental factors are rarely quantitated as biological and biochemical indicators, making it extremely difficult to understand the pathological conditions of psychiatric disorders as well as their underlying pathogenic mechanisms. Additionally, we have actually no other option but to perform biological studies on postmortem human brains that display features of psychiatric disorders, thereby resulting in a lack of experimental materials to characterize the basic biology of these disorders. From these backgrounds, animal, tissue, or cell models that can be used in basic research are indispensable to understand biologically the pathogenic mechanisms of psychiatric disorders. In this review, we discuss the importance of microendophenotypes of psychiatric disorders, i.e., phenotypes at the level of molecular dynamics, neurons, synapses, and neural circuits, as targets of basic research on these disorders.

Static and dynamic light scattering by red blood cells: A numerical study.

PubMed

Mauer, Johannes; Peltomäki, Matti; Poblete, Simón; Gompper, Gerhard; Fedosov, Dmitry A

2017-01-01

Light scattering is a well-established experimental technique, which gains more and more popularity in the biological field because it offers the means for non-invasive imaging and detection. However, the interpretation of light-scattering signals remains challenging due to the complexity of most biological systems. Here, we investigate static and dynamic scattering properties of red blood cells (RBCs) using two mesoscopic hydrodynamics simulation methods-multi-particle collision dynamics and dissipative particle dynamics. Light scattering is studied for various membrane shear elasticities, bending rigidities, and RBC shapes (e.g., biconcave and stomatocyte). Simulation results from the two simulation methods show good agreement, and demonstrate that the static light scattering of a diffusing RBC is not very sensitive to the changes in membrane properties and moderate alterations in cell shapes. We also compute dynamic light scattering of a diffusing RBC, from which dynamic properties of RBCs such as diffusion coefficients can be accessed. In contrast to static light scattering, the dynamic measurements can be employed to differentiate between the biconcave and stomatocytic RBC shapes and generally allow the differentiation based on the membrane properties. Our simulation results can be used for better understanding of light scattering by RBCs and the development of new non-invasive methods for blood-flow monitoring.

Static and dynamic light scattering by red blood cells: A numerical study

PubMed Central

Mauer, Johannes; Peltomäki, Matti; Poblete, Simón; Gompper, Gerhard

2017-01-01

Light scattering is a well-established experimental technique, which gains more and more popularity in the biological field because it offers the means for non-invasive imaging and detection. However, the interpretation of light-scattering signals remains challenging due to the complexity of most biological systems. Here, we investigate static and dynamic scattering properties of red blood cells (RBCs) using two mesoscopic hydrodynamics simulation methods—multi-particle collision dynamics and dissipative particle dynamics. Light scattering is studied for various membrane shear elasticities, bending rigidities, and RBC shapes (e.g., biconcave and stomatocyte). Simulation results from the two simulation methods show good agreement, and demonstrate that the static light scattering of a diffusing RBC is not very sensitive to the changes in membrane properties and moderate alterations in cell shapes. We also compute dynamic light scattering of a diffusing RBC, from which dynamic properties of RBCs such as diffusion coefficients can be accessed. In contrast to static light scattering, the dynamic measurements can be employed to differentiate between the biconcave and stomatocytic RBC shapes and generally allow the differentiation based on the membrane properties. Our simulation results can be used for better understanding of light scattering by RBCs and the development of new non-invasive methods for blood-flow monitoring. PMID:28472125

LASER BIOLOGY: Optomechanical tests of hydrated biological tissues subjected to laser shaping

NASA Astrophysics Data System (ADS)

Omel'chenko, A. I.; Sobol', E. N.

2008-03-01

The mechanical properties of a matrix are studied upon changing the size and shape of biological tissues during dehydration caused by weak laser-induced heating. The cartilage deformation, dehydration dynamics, and hydraulic conductivity are measured upon laser heating. The hydrated state and the shape of samples of separated fascias and cartilaginous tissues were controlled by using computer-aided processing of tissue images in polarised light.

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

Detecting subnetwork-level dynamic correlations.

PubMed

Yan, Yan; Qiu, Shangzhao; Jin, Zhuxuan; Gong, Sihong; Bai, Yun; Lu, Jianwei; Yu, Tianwei

2017-01-15

The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Population biological principles of drug-resistance evolution in infectious diseases.

PubMed

zur Wiesch, Pia Abel; Kouyos, Roger; Engelstädter, Jan; Regoes, Roland R; Bonhoeffer, Sebastian

2011-03-01

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fluid Dynamics of the Heart and its Valves

NASA Astrophysics Data System (ADS)

Peskin, Charles S.

1997-11-01

The fluid dynamics of the heart involve the interaction of blood, a viscous incompressible fluid, with the flexible, elastic, fiber-reinforced heart valve leaflets that are immersed in that fluid. Neither the fluid motion nor the valve leaflet motion are known in advance: both must be computed simultaneously by solving their coupled equations of motion. This can be done by the immersed boundary method(Peskin CS and McQueen DM: A general method for the computer simulation of biological systems interacting with fluids. In: Biological Fluid Dynamics (Ellington CP and Pedley TJ, eds.), The Company of Biologists Limited, Cambridge UK, 1995, pp. 265-276.), which can be extended to incorporate the contractile fiber architecture of the muscular heart walls as well as the valve leaflets and the blood. In this way we arrive at a three-dimensional computer model of the heart(Peskin CS and McQueen DM: Fluid dynamics of the heart and its valves. In: Case Studies in Mathematical Modeling: Ecology, Physiology, and Cell Biology (Othmer HG, Adler FR, Lewis MA, and Dallon JC, eds.), Prentice-Hall, Englewood Cliffs NJ, 1996, pp. 309-337.), which can be used as a test chamber for the design of prosthetic cardiac valves, and also to study the function of the heart in health and in disease. Numerical solutions of the equations of cardiac fluid dynamics obtained by the immersed boundary method will be presented in the form of a video animation of the beating heart.

Model structure identification for wastewater treatment simulation based on computational fluid dynamics.

PubMed

Alex, J; Kolisch, G; Krause, K

2002-01-01

The objective of this presented project is to use the results of an CFD simulation to automatically, systematically and reliably generate an appropriate model structure for simulation of the biological processes using CSTR activated sludge compartments. Models and dynamic simulation have become important tools for research but also increasingly for the design and optimisation of wastewater treatment plants. Besides the biological models several cases are reported about the application of computational fluid dynamics ICFD) to wastewater treatment plants. One aim of the presented method to derive model structures from CFD results is to exclude the influence of empirical structure selection to the result of dynamic simulations studies of WWTPs. The second application of the approach developed is the analysis of badly performing treatment plants where the suspicion arises that bad flow behaviour such as short cut flows is part of the problem. The method suggested requires as the first step the calculation of fluid dynamics of the biological treatment step at different loading situations by use of 3-dimensional CFD simulation. The result of this information is used to generate a suitable model structure for conventional dynamic simulation of the treatment plant by use of a number of CSTR modules with a pattern of exchange flows between the tanks automatically. The method is explained in detail and the application to the WWTP Wuppertal Buchenhofen is presented.

Chemical and Biological Dynamics Using Droplet-Based Microfluidics.

PubMed

Dressler, Oliver J; Casadevall I Solvas, Xavier; deMello, Andrew J

2017-06-12

Recent years have witnessed an increased use of droplet-based microfluidic techniques in a wide variety of chemical and biological assays. Nevertheless, obtaining dynamic data from these platforms has remained challenging, as this often requires reading the same droplets (possibly thousands of them) multiple times over a wide range of intervals (from milliseconds to hours). In this review, we introduce the elemental techniques for the formation and manipulation of microfluidic droplets, together with the most recent developments in these areas. We then discuss a wide range of analytical methods that have been successfully adapted for analyte detection in droplets. Finally, we highlight a diversity of studies where droplet-based microfluidic strategies have enabled the characterization of dynamic systems that would otherwise have remained unexplorable.

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit.

PubMed

Eriksson, O; Brinne, B; Zhou, Y; Björkegren, J; Tegnér, J

2009-03-01

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a 'tearing-and-zooming' approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits. [Includes supplementary material].

Computational Study of the Genomic and Epigenomic Phenomena

NASA Astrophysics Data System (ADS)

Yang, Wenjing

Biological systems are perhaps the ultimate complex systems, uniquely capable of processing and communicating information, reproducing in their lifetimes, and adapting in evolutionary time scales. My dissertation research focuses on using computational approaches to understand the biocomplexity manifested in the multitude of length scales and time scales. At the molecular and cellular level, central to the complex behavior of a biological system is the regulatory network. My research study focused on epigenetics, which is essential for multicellular organisms to establish cellular identity during development or in response to intracellular and environmental stimuli. My computational study of epigenomics is greatly facilitated by recent advances in high-throughput sequencing technology, which enables high-resolution snapshots of epigenomes and transcriptomes. Using human CD4+ T cell as a model system, the dynamical changes in epigenome and transcriptome pertinent to T cell activation were investigated at the genome scale. Going beyond traditional focus on transcriptional regulation, I provided evidences that post-transcriptional regulation may serve as a major component of the regulatory network. In addition, I explored alternative polyadenylation, another novel aspect of gene regulation, and how it cross-talks with the local chromatin structure. As the renowned theoretical biologist Theodosius Dobzhansky said eloquently, "Nothing in biology makes sense except in the light of evolution''. To better understand this ubiquitous driving force in the biological world, I went beyond molecular events in a single organism, and investigated the dynamical changes of population structure along the evolutionary time scale. To this end, we used HIV virus population dynamics in the host immune system as a model system. The evolution of HIV viral population plays a key role in AIDS immunopathogenesis with its exceptionally high mutation rate. However, the theoretical studies of the effect of recombination have been rather limited. Given the phylogenetic and experimental evidences for the high recombination rate and its important role in HIV evolution and epidemics, I established a mathematical model to study the effect of recombination, and explored the complex behavior of this dynamics system.

How input fluctuations reshape the dynamics of a biological switching system

NASA Astrophysics Data System (ADS)

Hu, Bo; Kessler, David A.; Rappel, Wouter-Jan; Levine, Herbert

2012-12-01

An important task in quantitative biology is to understand the role of stochasticity in biochemical regulation. Here, as an extension of our recent work [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.107.148101 107, 148101 (2011)], we study how input fluctuations affect the stochastic dynamics of a simple biological switch. In our model, the on transition rate of the switch is directly regulated by a noisy input signal, which is described as a non-negative mean-reverting diffusion process. This continuous process can be a good approximation of the discrete birth-death process and is much more analytically tractable. Within this setup, we apply the Feynman-Kac theorem to investigate the statistical features of the output switching dynamics. Consistent with our previous findings, the input noise is found to effectively suppress the input-dependent transitions. We show analytically that this effect becomes significant when the input signal fluctuates greatly in amplitude and reverts slowly to its mean.

Mammalian synthetic biology for studying the cell

PubMed Central

Mathur, Melina; Xiang, Joy S.

2017-01-01

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. PMID:27932576

Plankton Production Biology

DTIC Science & Technology

2012-09-30

understanding of the dynamics of the euphotic zone and the flux of particulate organic carbon (POC) into the mesopelagic (“twilight”) domain below. On p. 13...handicap of satellite-based estimates of the dynamics of primary production”. Moreover, at present we have little hope for predicting accurately from...will permit the study of stage-specific population dynamics (growth rate, production, mortality) of copepod larvae (nauplii) in mixed populations in the

Evolution of regulatory networks towards adaptability and stability in a changing environment

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2014-11-01

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Population dynamics in controlled unsteady-state systems: An application to the degradation of glyphosate in a sequencing batch reactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Devarakonda, M.S.

1988-01-01

Control over population dynamics and organism selection in a biological waste treatment system provides an effective means of engineering process efficiency. Examples of applications of organism selection include control of filamentous organisms, biological nutrient removal, industrial waste treatment requiring the removal of specific substrates, and hazardous waste treatment. Inherently, full scale biological waste treatment systems are unsteady state systems due to the variations in the waste streams and mass flow rates of the substrates. Some systems, however, have the capacity to impose controlled selective pressures on the biological population by means of their operation. An example of such a systemmore » is the Sequencing Batch Reactor (SBR) which was the experimental system utilized in this research work. The concepts of organism selection were studied in detail for the biodegradation of a herbicide waste stream, with glyphosate as the target compound. The SBR provided a reactor configuration capable of exerting the necessary selective pressures to select and enrich for a glyphosate degrading population. Based on results for bench scale SBRs, a hypothesis was developed to explain population dynamics in glyphosate degrading systems.« less

Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Wei, Xile; Deng, Bin; Liu, Chen; Li, Huiyan; Wang, Jiang

2018-03-01

Balance between biological plausibility of dynamical activities and computational efficiency is one of challenging problems in computational neuroscience and neural system engineering. This paper proposes a set of efficient methods for the hardware realization of the conductance-based neuron model with relevant dynamics, targeting reproducing the biological behaviors with low-cost implementation on digital programmable platform, which can be applied in wide range of conductance-based neuron models. Modified GP neuron models for efficient hardware implementation are presented to reproduce reliable pallidal dynamics, which decode the information of basal ganglia and regulate the movement disorder related voluntary activities. Implementation results on a field-programmable gate array (FPGA) demonstrate that the proposed techniques and models can reduce the resource cost significantly and reproduce the biological dynamics accurately. Besides, the biological behaviors with weak network coupling are explored on the proposed platform, and theoretical analysis is also made for the investigation of biological characteristics of the structured pallidal oscillator and network. The implementation techniques provide an essential step towards the large-scale neural network to explore the dynamical mechanisms in real time. Furthermore, the proposed methodology enables the FPGA-based system a powerful platform for the investigation on neurodegenerative diseases and real-time control of bio-inspired neuro-robotics.

Cell junction dynamics in the testis: Sertoli-germ cell interactions and male contraceptive development.

PubMed

Cheng, C Yan; Mruk, Dolores D

2002-10-01

Spermatogenesis is an intriguing but complicated biological process. However, many studies since the 1960s have focused either on the hormonal events of the hypothalamus-pituitary-testicular axis or morphological events that take place in the seminiferous epithelium. Recent advances in biochemistry, cell biology, and molecular biology have shifted attention to understanding some of the key events that regulate spermatogenesis, such as germ cell apoptosis, cell cycle regulation, Sertoli-germ cell communication, and junction dynamics. In this review, we discuss the physiology and biology of junction dynamics in the testis, in particular how these events affect interactions of Sertoli and germ cells in the seminiferous epithelium behind the blood-testis barrier. We also discuss how these events regulate the opening and closing of the blood-testis barrier to permit the timely passage of preleptotene and leptotene spermatocytes across the blood-testis barrier. This is physiologically important since developing germ cells must translocate across the blood-testis barrier as well as traverse the seminiferous epithelium during their development. We also discuss several available in vitro and in vivo models that can be used to study Sertoli-germ cell anchoring junctions and Sertoli-Sertoli tight junctions. An in-depth survey in this subject has also identified several potential targets to be tackled to perturb spermatogenesis, which will likely lead to the development of novel male contraceptives.

Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer.

PubMed

Roberts, James A; Friston, Karl J; Breakspear, Michael

2017-04-01

Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity; stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Learning How to Identify Species in a Situated Learning Scenario: Using Dynamic-Static Visualizations to Prepare Students for Their Visit to the Aquarium

ERIC Educational Resources Information Center

Pfeiffer, Vanessa D. I.; Scheiter, Katharina; Kuhl, Tim; Gemballa, Sven

2011-01-01

This study investigated whether studying dynamic-static visualizations prepared first-year Biology students better for an out-of-classroom experience in an aquarium than learning how to identify species with more traditional instructional materials. During an initial classroom phase, learners either watched underwater videos of 15 freshwater fish…

Relations between phytoplankton growth rates and nutrient dynamics in Lake Norman, North Carolina. Technical report series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodriguez, M.S.

A baseline study of phytoplankton production and nutrient dynamics was conducted on Lake Norman, NC, a 13000-ha, warm-monomictic reservoir, prior to the initiation of thermal inputs from an 1180-MW nuclear electric generation facility. The objective of the study was to identify the major physical, chemical and biological processes controlling nutrient dynamics in Lake Norman, with specific reference to the impact of phytoplankton production on the cycling of carbon, nitrogen and phosphorus.

Modeling of Receptor Tyrosine Kinase Signaling: Computational and Experimental Protocols.

PubMed

Fey, Dirk; Aksamitiene, Edita; Kiyatkin, Anatoly; Kholodenko, Boris N

2017-01-01

The advent of systems biology has convincingly demonstrated that the integration of experiments and dynamic modelling is a powerful approach to understand the cellular network biology. Here we present experimental and computational protocols that are necessary for applying this integrative approach to the quantitative studies of receptor tyrosine kinase (RTK) signaling networks. Signaling by RTKs controls multiple cellular processes, including the regulation of cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis. We describe methods of model building and training on experimentally obtained quantitative datasets, as well as experimental methods of obtaining quantitative dose-response and temporal dependencies of protein phosphorylation and activities. The presented methods make possible (1) both the fine-grained modeling of complex signaling dynamics and identification of salient, course-grained network structures (such as feedback loops) that bring about intricate dynamics, and (2) experimental validation of dynamic models.

Drugs That Target Dynamic Microtubules: A New Molecular Perspective

PubMed Central

Stanton, Richard A.; Gernert, Kim M.; Nettles, James H.; Aneja, Ritu

2011-01-01

Microtubules have long been considered an ideal target for anticancer drugs because of the essential role they play in mitosis, forming the dynamic spindle apparatus. As such, there is a wide variety of compounds currently in clinical use and in development that act as antimitotic agents by altering microtubule dynamics. Although these diverse molecules are known to affect microtubule dynamics upon binding to one of the three established drug domains (taxane, vinca alkaloid, or colchicine site), the exact mechanism by which each drug works is still an area of intense speculation and research. In this study, we review the effects of microtubule-binding chemotherapeutic agents from a new perspective, considering how their mode of binding induces conformational changes and alters biological function relative to the molecular vectors of microtubule assembly or disassembly. These “biological vectors” can thus be used as a spatiotemporal context to describe molecular mechanisms by which microtubule-targeting drugs work. PMID:21381049

TASI: A software tool for spatial-temporal quantification of tumor spheroid dynamics.

PubMed

Hou, Yue; Konen, Jessica; Brat, Daniel J; Marcus, Adam I; Cooper, Lee A D

2018-05-08

Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.

Nonlinear adhesion dynamics of confined lipid membranes

NASA Astrophysics Data System (ADS)

To, Tung; Le Goff, Thomas; Pierre-Louis, Olivier

Lipid membranes, which are ubiquitous objects in biological environments are often confined. For example, they can be sandwiched between a substrate and the cytoskeleton between cell adhesion, or between other membranes in stacks, or in the Golgi apparatus. We present a study of the nonlinear dynamics of membranes in a model system, where the membrane is confined between two flat walls. The dynamics derived from the lubrication approximation is highly nonlinear and nonlocal. The solution of this model in one dimension exhibits frozen states due to oscillatory interactions between membranes caused by the bending rigidity. We develope a kink model for these phenomena based on the historical work of Kawasaki and Otha. In two dimensions, the dynamics is more complex, and depends strongly on the amount of excess area in the system. We discuss the relevance of our findings for experiments on model membranes, and for biological systems. Supported by the grand ANR Biolub.

Protocols for Molecular Dynamics Simulations of RNA Nanostructures.

PubMed

Kim, Taejin; Kasprzak, Wojciech K; Shapiro, Bruce A

2017-01-01

Molecular dynamics (MD) simulations have been used as one of the main research tools to study a wide range of biological systems and bridge the gap between X-ray crystallography or NMR structures and biological mechanism. In the field of RNA nanostructures, MD simulations have been used to fix steric clashes in computationally designed RNA nanostructures, characterize the dynamics, and investigate the interaction between RNA and other biomolecules such as delivery agents and membranes.In this chapter we present examples of computational protocols for molecular dynamics simulations in explicit and implicit solvent using the Amber Molecular Dynamics Package. We also show examples of post-simulation analysis steps and briefly mention selected tools beyond the Amber package. Limitations of the methods, tools, and protocols are also discussed. Most of the examples are illustrated for a small RNA duplex (helix), but the protocols are applicable to any nucleic acid structure, subject only to the computational speed and memory limitations of the hardware available to the user.

Layered acoustofluidic resonators for the simultaneous optical and acoustic characterisation of cavitation dynamics, microstreaming, and biological effects.

PubMed

Pereno, V; Aron, M; Vince, O; Mannaris, C; Seth, A; de Saint Victor, M; Lajoinie, G; Versluis, M; Coussios, C; Carugo, D; Stride, E

2018-05-01

The study of the effects of ultrasound-induced acoustic cavitation on biological structures is an active field in biomedical research. Of particular interest for therapeutic applications is the ability of oscillating microbubbles to promote both cellular and tissue membrane permeabilisation and to improve the distribution of therapeutic agents in tissue through extravasation and convective transport. The mechanisms that underpin the interaction between cavitating agents and tissues are, however, still poorly understood. One challenge is the practical difficulty involved in performing optical microscopy and acoustic emissions monitoring simultaneously in a biologically compatible environment. Here we present and characterise a microfluidic layered acoustic resonator ( μ LAR) developed for simultaneous ultrasound exposure, acoustic emissions monitoring, and microscopy of biological samples. The μ LAR facilitates in vitro ultrasound experiments in which measurements of microbubble dynamics, microstreaming velocity fields, acoustic emissions, and cell-microbubble interactions can be performed simultaneously. The device and analyses presented provide a means of performing mechanistic in vitro studies that may benefit the design of predictable and effective cavitation-based ultrasound treatments.

Principal process analysis of biological models.

PubMed

Casagranda, Stefano; Touzeau, Suzanne; Ropers, Delphine; Gouzé, Jean-Luc

2018-06-14

Understanding the dynamical behaviour of biological systems is challenged by their large number of components and interactions. While efforts have been made in this direction to reduce model complexity, they often prove insufficient to grasp which and when model processes play a crucial role. Answering these questions is fundamental to unravel the functioning of living organisms. We design a method for dealing with model complexity, based on the analysis of dynamical models by means of Principal Process Analysis. We apply the method to a well-known model of circadian rhythms in mammals. The knowledge of the system trajectories allows us to decompose the system dynamics into processes that are active or inactive with respect to a certain threshold value. Process activities are graphically represented by Boolean and Dynamical Process Maps. We detect model processes that are always inactive, or inactive on some time interval. Eliminating these processes reduces the complex dynamics of the original model to the much simpler dynamics of the core processes, in a succession of sub-models that are easier to analyse. We quantify by means of global relative errors the extent to which the simplified models reproduce the main features of the original system dynamics and apply global sensitivity analysis to test the influence of model parameters on the errors. The results obtained prove the robustness of the method. The analysis of the sub-model dynamics allows us to identify the source of circadian oscillations. We find that the negative feedback loop involving proteins PER, CRY, CLOCK-BMAL1 is the main oscillator, in agreement with previous modelling and experimental studies. In conclusion, Principal Process Analysis is a simple-to-use method, which constitutes an additional and useful tool for analysing the complex dynamical behaviour of biological systems.

Derivation and computation of discrete-delay and continuous-delay SDEs in mathematical biology.

PubMed

Allen, Edward J

2014-06-01

Stochastic versions of several discrete-delay and continuous-delay differential equations, useful in mathematical biology, are derived from basic principles carefully taking into account the demographic, environmental, or physiological randomness in the dynamic processes. In particular, stochastic delay differential equation (SDDE) models are derived and studied for Nicholson's blowflies equation, Hutchinson's equation, an SIS epidemic model with delay, bacteria/phage dynamics, and glucose/insulin levels. Computational methods for approximating the SDDE models are described. Comparisons between computational solutions of the SDDEs and independently formulated Monte Carlo calculations support the accuracy of the derivations and of the computational methods.

Dynamics of blood plasma by spectropolarimetry and biochemical techniques

NASA Astrophysics Data System (ADS)

Voloshynska, Katerina; Ilashchuka, Tetjana; Prydij, Olexander; Gruia, Maria

2014-08-01

The aim of the study was to establish objective parameters of the field of laser and incoherent radiation of different spectral ranges (UV, visible, IR) as a non-invasive optical method of interaction with different samples of biological tissues and fluids of patients to determine the dynamics of metabolic syndrome and choosing the best personal treatment. As diagnostic methods have been used ultraviolet spectrometry samples of blood plasma in the liquid state, infrared spectroscopy middle range (2,5 - 25 microns) dry residue of plasma polarization and laser diagnostic technique of thin histological sections of biological tissues.

[Effects of biologically active pectin-containing dietary supplement on gastroduodenal motility in patients with a functional dyspepsia].

PubMed

Loranskaia, T I; Kabanova, I N; Klykova, E V

2002-01-01

For 21 patients with a functional dyspepsia the influencing biologically active additives to nutrition "Pekcecom" on dynamics of clinical symptoms and parameters gastroduodenal motility under the data gastroduodenoscintigraphy was studied. The usage of biologically active additives during 4 weeks was accompanied by deboosting of accelerated gastric emptying for want of statistically significant influencing on a normal and delayed gastric emptying and parameters of duodenal transit.

Malaria resurgence in the East African highlands: Temperature trends revisited

PubMed Central

Pascual, M.; Ahumada, J. A.; Chaves, L. F.; Rodó, X.; Bouma, M.

2006-01-01

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models. PMID:16571662

Cyclic and Linear Monoterpenes in Phospholipid Membranes: Phase Behavior, Bilayer Structure, and Molecular Dynamics.

PubMed

Pham, Quoc Dat; Topgaard, Daniel; Sparr, Emma

2015-10-13

Monoterpenes are abundant in essential oils extracted from plants. These relatively small and hydrophobic molecules have shown important biological functions, including antimicrobial activity and membrane penetration enhancement. The interaction between the monoterpenes and lipid bilayers is considered important to the understanding of the biological functions of monoterpenes. In this study, we investigated the effect of cyclic and linear monoterpenes on the structure and dynamics of lipids in model membranes. We have studied the ternary system 1,2-dimyristoyl-sn-glycero-3-phosphocholine-monoterpene-water as a model with a focus on dehydrated conditions. By combining complementary techniques, including differential scanning calorimetry, solid-state nuclear magnetic resonance, and small- and wide-angle X-ray scattering, bilayer structure, phase transitions, and lipid molecular dynamics were investigated at different water contents. Monoterpenes cause pronounced melting point depression and phase segregation in lipid bilayers, and the extent of these effects depends on the hydration conditions. The addition of a small amount of thymol to the fluid bilayer (volume fraction of 0.03 in the bilayer) leads to an increased order in the acyl chain close to the bilayer interface. The findings are discussed in relation to biological systems and lipid formulations.

Coming out in Class: Challenges and Benefits of Active Learning in a Biology Classroom for LGBTQIA Students

ERIC Educational Resources Information Center

Cooper, Katelyn M.; Brownell, Sara E.

2016-01-01

As we transition our undergraduate biology classrooms from traditional lectures to active learning, the dynamics among students become more important. These dynamics can be influenced by student social identities. One social identity that has been unexamined in the context of undergraduate biology is the spectrum of lesbian, gay, bisexual,…

Bipartite graphs in systems biology and medicine: a survey of methods and applications.

PubMed

Pavlopoulos, Georgios A; Kontou, Panagiota I; Pavlopoulou, Athanasia; Bouyioukos, Costas; Markou, Evripides; Bagos, Pantelis G

2018-04-01

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Infant brain activity while viewing facial movement of point-light displays as measured by near-infrared spectroscopy (NIRS).

PubMed

Ichikawa, Hiroko; Kanazawa, So; Yamaguchi, Masami K; Kakigi, Ryusuke

2010-09-27

Adult observers can quickly identify specific actions performed by an invisible actor from the points of lights attached to the actor's head and major joints. Infants are also sensitive to biological motion and prefer to see it depicted by a dynamic point-light display. In detecting biological motion such as whole body and facial movements, neuroimaging studies have demonstrated the involvement of the occipitotemporal cortex, including the superior temporal sulcus (STS). In the present study, we used the point-light display technique and near-infrared spectroscopy (NIRS) to examine infant brain activity while viewing facial biological motion depicted in a point-light display. Dynamic facial point-light displays (PLD) were made from video recordings of three actors making a facial expression of surprise in a dark room. As in Bassili's study, about 80 luminous markers were scattered over the surface of the actor's faces. In the experiment, we measured infant's hemodynamic responses to these displays using NIRS. We hypothesized that infants would show different neural activity for upright and inverted PLD. The responses were compared to the baseline activation during the presentation of individual still images, which were frames extracted from the dynamic PLD. We found that the concentration of oxy-Hb increased in the right temporal area during the presentation of the upright PLD compared to that of the baseline period. This is the first study to demonstrate that infant's brain activity in face processing is induced only by the motion cue of facial movement depicted by dynamic PLD. (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Polymer Dynamics from Synthetic to Biological Macromolecules

NASA Astrophysics Data System (ADS)

Richter, D.; Niedzwiedz, K.; Monkenbusch, M.; Wischnewski, A.; Biehl, R.; Hoffmann, B.; Merkel, R.

2008-02-01

High resolution neutron scattering together with a meticulous choice of the contrast conditions allows to access the large scale dynamics of soft materials including biological molecules in space and time. In this contribution we present two examples. One from the world of synthetic polymers, the other from biomolecules. First, we will address the peculiar dynamics of miscible polymer blends with very different component glass transition temperatures. Polymethylmetacrylate (PMMA), polyethyleneoxide (PEO) are perfectly miscible but exhibit a difference in the glass transition temperature by 200 K. We present quasielastic neutron scattering investigations on the dynamics of the fast component in the range from angströms to nanometers over a time frame of five orders of magnitude. All data may be consistently described in terms of a Rouse model with random friction, reflecting the random environment imposed by the nearly frozen PMMA matrix on the fast mobile PEO. In the second part we touch on some new developments relating to large scale internal dynamics of proteins by neutron spin echo. We will report results of some pioneering studies which show the feasibility of such experiments on large scale protein motion which will most likely initiate further studies in the future.

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy.

PubMed

Xu, Yao; Havenith, Martina

2015-11-07

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy

NASA Astrophysics Data System (ADS)

Xu, Yao; Havenith, Martina

2015-11-01

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

Complex and unexpected dynamics in simple genetic regulatory networks

NASA Astrophysics Data System (ADS)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

Seamount ecology and dynamics: A multidisciplinary data set from repeated surveys at different seamounts in the Northeast Atlantic and Mediterranean (2003 - 2013).

NASA Astrophysics Data System (ADS)

Mohn, C.; Christiansen, B.; Denda, A.; George, K. H.; Kaufmann, M.; Maranhão, M.; Martin, B.; Metzger, T.; Peine, F.; Schuster, A.; Springer, B.; Stefanowitsch, B.; Turnewitsch, R.; Wehrmann, H.

2016-02-01

Seamounts are amongst the most common physiographic open ocean systems, but remoteness and geographic complexity have limited the number of integrated and multidisciplinary seamount surveys in the past. As a consequence, important aspects of seamount ecology and dynamics remain poorly studied. Here we present a multi-parameter data set from individual and repeated seamount surveys conducted at different sites in the Northeast Atlantic and Eastern Mediterranean between 2003 and 2013. The main objective of these surveys was to establish a collection of ecosystem relevant descriptors and to develop a better understanding of seamount ecosystem composition and variability in different dynamical and bio-geographic environments. Measurements were conducted at four seamounts in the Northeast Atlantic (Ampère, Sedlo, Seine, Senghor) and two seamounts in the Eastern Mediterranean (Anaximenes, Eratosthenes). The data set comprises records from a total number of 11 cruises including physical oceanography (temperature, salinity, pressure, currents), biology (phytoplankton, zooplankton, fish, benthos) and biogeochemistry (sedimentary particle dynamics, carbon flux). The resulting multi-disciplinary data collection provides a unique opportunity for comparative studies of seamount ecosystem structure and dynamics between different physical, biological and biogeochemical regimes

Biological oscillations: Fluorescence monitoring by confocal microscopy

NASA Astrophysics Data System (ADS)

Chattoraj, Shyamtanu; Bhattacharyya, Kankan

2016-09-01

Fluctuations play a vital role in biological systems. Single molecule spectroscopy has recently revealed many new kinds of fluctuations in biological molecules. In this account, we focus on structural fluctuations of an antigen-antibody complex, conformational dynamics of a DNA quadruplex, effects of taxol on dynamics of microtubules, intermittent red-ox oscillations at different organelles in a live cell (mitochondria, lipid droplets, endoplasmic reticulum and cell membrane) and stochastic resonance in gene silencing. We show that there are major differences in these dynamics between a cancer cell and the corresponding non-cancer cell.

Use of EPR to Solve Biochemical Problems

PubMed Central

Sahu, Indra D.; McCarrick, Robert M.; Lorigan, Gary A.

2013-01-01

EPR spectroscopy is a very powerful biophysical tool that can provide valuable structural and dynamic information on a wide variety of biological systems. The intent of this review is to provide a general overview for biochemists and biological researchers on the most commonly used EPR methods and how these techniques can be used to answer important biological questions. The topics discussed could easily fill one or more textbooks; thus, we present a brief background on several important biological EPR techniques and an overview of several interesting studies that have successfully used EPR to solve pertinent biological problems. The review consists of the following sections: an introduction to EPR techniques, spin labeling methods, and studies of naturally occurring organic radicals and EPR active transition metal systems which are presented as a series of case studies in which EPR spectroscopy has been used to greatly further our understanding of several important biological systems. PMID:23961941

Mammalian synthetic biology for studying the cell.

PubMed

Mathur, Melina; Xiang, Joy S; Smolke, Christina D

2017-01-02

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. © 2017 Mathur et al.

A Dynamic Laplacian for Identifying Lagrangian Coherent Structures on Weighted Riemannian Manifolds

NASA Astrophysics Data System (ADS)

Froyland, Gary; Kwok, Eric

2017-06-01

Transport and mixing in dynamical systems are important properties for many physical, chemical, biological, and engineering processes. The detection of transport barriers for dynamics with general time dependence is a difficult, but important problem, because such barriers control how rapidly different parts of phase space (which might correspond to different chemical or biological agents) interact. The key factor is the growth of interfaces that partition phase space into separate regions. The paper Froyland (Nonlinearity 28(10):3587-3622, 2015) introduced the notion of dynamic isoperimetry: the study of sets with persistently small boundary size (the interface) relative to enclosed volume, when evolved by the dynamics. Sets with this minimal boundary size to volume ratio were identified as level sets of dominant eigenfunctions of a dynamic Laplace operator. In this present work we extend the results of Froyland (Nonlinearity 28(10):3587-3622, 2015) to the situation where the dynamics (1) is not necessarily volume preserving, (2) acts on initial agent concentrations different from uniform concentrations, and (3) occurs on a possibly curved phase space. Our main results include generalised versions of the dynamic isoperimetric problem, the dynamic Laplacian, Cheeger's inequality, and the Federer-Fleming theorem. We illustrate the computational approach with some simple numerical examples.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Watershed Dynamics. Student Edition. Cornell Scientific Inquiry Series

ERIC Educational Resources Information Center

Carlsen, William S.; Trautmann, Nancy M.

2004-01-01

Whether you're a stream studies novice or a veteran aquatic monitor, "Watershed Dynamics" gives you abundant practical resources to extend your students' investigations into local water quality and land-use issues. This two-part set is ideal for teaching biological and ecological concepts and research techniques. It also shows how the interplay…

Neurodynamics With Spatial Self-Organization

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1993-01-01

Report presents theoretical study of dynamics of neural network organizing own response in both phase space and in position space. Postulates several mathematical models of dynamics including spatial derivatives representing local interconnections among neurons. Shows how neural responses propagate via these interconnections and how spatial pattern of neural responses formed in homogeneous biological neural network.

A Compact Synchronous Cellular Model of Nonlinear Calcium Dynamics: Simulation and FPGA Synthesis Results.

PubMed

Soleimani, Hamid; Drakakis, Emmanuel M

2017-06-01

Recent studies have demonstrated that calcium is a widespread intracellular ion that controls a wide range of temporal dynamics in the mammalian body. The simulation and validation of such studies using experimental data would benefit from a fast large scale simulation and modelling tool. This paper presents a compact and fully reconfigurable cellular calcium model capable of mimicking Hopf bifurcation phenomenon and various nonlinear responses of the biological calcium dynamics. The proposed cellular model is synthesized on a digital platform for a single unit and a network model. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed cellular model can mimic the biological calcium behaviors with considerably low hardware overhead. The approach has the potential to speed up large-scale simulations of slow intracellular dynamics by sharing more cellular units in real-time. To this end, various networks constructed by pipelining 10 k to 40 k cellular calcium units are compared with an equivalent simulation run on a standard PC workstation. Results show that the cellular hardware model is, on average, 83 times faster than the CPU version.

Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

NASA Astrophysics Data System (ADS)

Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng

2016-04-01

Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

The biomolecular corona of nanoparticles in circulating biological media

NASA Astrophysics Data System (ADS)

Pozzi, D.; Caracciolo, G.; Digiacomo, L.; Colapicchioni, V.; Palchetti, S.; Capriotti, A. L.; Cavaliere, C.; Zenezini Chiozzi, R.; Puglisi, A.; Laganà, A.

2015-08-01

When nanoparticles come into contact with biological media, they are covered by a biomolecular `corona', which confers a new identity to the particles. In all the studies reported so far nanoparticles are incubated with isolated plasma or serum that are used as a model for protein adsorption. Anyway, bodily fluids are dynamic in nature so the question arises on whether the incubation protocol, i.e. dynamic vs. static incubation, could affect the composition and structure of the biomolecular corona. Here we let multicomponent liposomes interact with fetal bovine serum (FBS) both statically and dynamically, i.e. in contact with circulating FBS (~40 cm s-1). The structure and composition of the liposome-protein corona, as determined by dynamic light scattering, electrophoretic light scattering and liquid chromatography tandem mass spectrometry, were found to be dependent on the incubation protocol. Specifically, following dynamic exposure to FBS, multicomponent liposomes were less enriched in complement proteins and appreciably more enriched in apolipoproteins and acute phase proteins (e.g. alpha-1-antitrypsin and inter-alpha-trypsin inhibitor heavy chain H3) that are involved in relevant interactions between nanoparticles and living systems. Supported by our results, we speculate that efficient predictive modeling of nanoparticle behavior in vivo will require accurate knowledge of nanoparticle-specific protein fingerprints in circulating biological media.When nanoparticles come into contact with biological media, they are covered by a biomolecular `corona', which confers a new identity to the particles. In all the studies reported so far nanoparticles are incubated with isolated plasma or serum that are used as a model for protein adsorption. Anyway, bodily fluids are dynamic in nature so the question arises on whether the incubation protocol, i.e. dynamic vs. static incubation, could affect the composition and structure of the biomolecular corona. Here we let multicomponent liposomes interact with fetal bovine serum (FBS) both statically and dynamically, i.e. in contact with circulating FBS (~40 cm s-1). The structure and composition of the liposome-protein corona, as determined by dynamic light scattering, electrophoretic light scattering and liquid chromatography tandem mass spectrometry, were found to be dependent on the incubation protocol. Specifically, following dynamic exposure to FBS, multicomponent liposomes were less enriched in complement proteins and appreciably more enriched in apolipoproteins and acute phase proteins (e.g. alpha-1-antitrypsin and inter-alpha-trypsin inhibitor heavy chain H3) that are involved in relevant interactions between nanoparticles and living systems. Supported by our results, we speculate that efficient predictive modeling of nanoparticle behavior in vivo will require accurate knowledge of nanoparticle-specific protein fingerprints in circulating biological media. Electronic supplementary information (ESI) available: Table S1: estimation of the corona thickness, sk, of elementary units (liposome-protein corona) clustered in k-fold equilibrium aggregates (t > 15 min). Tables S2 and S3: the full list of the most abundant corona proteins identified on the surface of multicomponent liposomes following dynamic and static incubation with fetal bovine serum. Table S4: the list of the unique proteins bound to MC liposomes following 90 min incubation with FBS under dynamic and static incubation. See DOI: 10.1039/c5nr03701h

A computer lab exploring evolutionary aspects of chromatin structure and dynamics for an undergraduate chromatin course*.

PubMed

Eirín-López, José M

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a vast body of knowledge to the classroom. With this aim, the present work describes a multidisciplinary computer lab designed to introduce undergraduate students to the dynamic nature of chromatin, within the context of the one semester course "Chromatin: Structure, Function and Evolution." This exercise is organized in three parts including (a) molecular evolutionary biology of histone families (using the H1 family as example), (b) histone structure and variation across different animal groups, and (c) effect of histone diversity on nucleosome structure and chromatin dynamics. By using freely available bioinformatic tools that can be run on common computers, the concept of chromatin dynamics is interactively illustrated from a comparative/evolutionary perspective. At the end of this computer lab, students are able to translate the bioinformatic information into a biochemical context in which the relevance of histone primary structure on chromatin dynamics is exposed. During the last 8 years this exercise has proven to be a powerful approach for teaching chromatin structure and dynamics, allowing students a higher degree of independence during the processes of learning and self-assessment. Copyright © 2013 International Union of Biochemistry and Molecular Biology, Inc.

Combining protein sequence, structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily.

PubMed

Dong, Zheng; Zhou, Hongyu; Tao, Peng

2018-02-01

PAS domains are widespread in archaea, bacteria, and eukaryota, and play important roles in various functions. In this study, we aim to explore functional evolutionary relationship among proteins in the PAS domain superfamily in view of the sequence-structure-dynamics-function relationship. We collected protein sequences and crystal structure data from RCSB Protein Data Bank of the PAS domain superfamily belonging to three biological functions (nucleotide binding, photoreceptor activity, and transferase activity). Protein sequences were aligned and then used to select sequence-conserved residues and build phylogenetic tree. Three-dimensional structure alignment was also applied to obtain structure-conserved residues. The protein dynamics were analyzed using elastic network model (ENM) and validated by molecular dynamics (MD) simulation. The result showed that the proteins with same function could be grouped by sequence similarity, and proteins in different functional groups displayed statistically significant difference in their vibrational patterns. Interestingly, in all three functional groups, conserved amino acid residues identified by sequence and structure conservation analysis generally have a lower fluctuation than other residues. In addition, the fluctuation of conserved residues in each biological function group was strongly correlated with the corresponding biological function. This research suggested a direct connection in which the protein sequences were related to various functions through structural dynamics. This is a new attempt to delineate functional evolution of proteins using the integrated information of sequence, structure, and dynamics. © 2017 The Protein Society.

Biologically active ligands for yersinia outer protein H (YopH): feature based pharmacophore screening, docking and molecular dynamics studies.

PubMed

Tamilvanan, Thangaraju; Hopper, Waheeta

2014-01-01

Yersinia pestis, a Gram negative bacillus, spreads via lymphatic to lymph nodes and to all organs through the bloodstream, causing plague. Yersinia outer protein H (YopH) is one of the important effector proteins, which paralyzes lymphocytes and macrophages by dephosphorylating critical tyrosine kinases and signal transduction molecules. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse sets of YopH inhibitors, which would be useful for designing of potential antitoxin. In this study, we have selected 60 biologically active inhibitors of YopH to perform Ligand based pharmacophore study to elucidate the important structural features responsible for biological activity. Pharmacophore model demonstrated the importance of two acceptors, one hydrophobic and two aromatic features toward the biological activity. Based on these features, different databases were screened to identify novel compounds and these ligands were subjected for docking, ADME properties and Binding energy prediction. Post docking validation was performed using molecular dynamics simulation for selected ligands to calculate the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The ligands, ASN03270114, Mol_252138, Mol_31073 and ZINC04237078 may act as inhibitors against YopH of Y. pestis.

Dynamics of adsorption in micellar and non micellar solutions of derivatives of lysosomotropic substances.

PubMed

Dopierala, Katarzyna; Prochaska, Krystyna

2010-04-22

Dynamics of adsorption in micellar and non micellar solutions of derivatives of lysosomotropic substances was studied. The following compounds were considered in our research work: alkyl N,N-dimethyl-alaninates methobromides (DMALM-n), alkyl N,N-dimethylglycinates methobromides (DMGM-n), fatty acids N,N-dimethylaminoethylesters methobromides (DMM-n), fatty acids N,N-dimethylaminopropylesters methobromides (DMPM-n), fatty acids 1-dimethylamino-2-propyl methobromides (DMP(2)M-n), and derivatives of aminoesters with double alkyl chains (M(2)M-n). The examined compounds show interesting biological properties which can be useful, especially in medicine. The exact mechanism of interaction of such compounds with biological membrane is not fully known. However, it is supposed that the presence of micelles has an important role in biological systems. In this paper we show the results of dynamic surface tension measurements in solutions containing the investigated compounds at concentrations above and below cmc. Moreover, we analyzed the influence of the chemical structure of molecules on the diameters of the micelles formed in the solutions. It was found that adsorption dynamics for the studied compounds is strongly affected by the chemical structure of the considered derivatives, especially by the presence of the ester bond, linearity of the molecule, as well as its hydrophobicity. The obtained results show that the structure of the bromide M(2)M-n with two short hydrocarbon chains favors a faster and more efficient adsorption of the molecules at the air/water interface, compared with compounds having one long alkyl chain. Moreover, the double chained derivatives of the M(2)M-n type do not form typical spherical micelles but bilayer structures probably exist in these solutions. The micelles present in the solutions influence the dynamics of adsorption drastically. Moreover, the obtained results indicated that the compounds with especially high biological activity form rather small aggregates. Copyright 2010 Elsevier B.V. All rights reserved.

[The influence of different types of physical exertions on the mature males' biological age].

PubMed

Sirotin, A B; Belozerova, L M; Sergeeva, I G; Zhukov, V N; Kolegova, N G

2014-01-01

We studied the biological age according to anthropometric indexes, mental, physical and both the types of working efficiency in 122 males at the age of 50-59 years. All of them were devided into 5 groups: untrained individuals, going in for general physical training, sport veterans, specializing in endurance training, sport plays representatives, weight-lifters. We found out a younger biological age in sport veterans, who were carrying out dynamic exertions.

Nonlinear dynamics based digital logic and circuits.

PubMed

Kia, Behnam; Lindner, John F; Ditto, William L

2015-01-01

We discuss the role and importance of dynamics in the brain and biological neural networks and argue that dynamics is one of the main missing elements in conventional Boolean logic and circuits. We summarize a simple dynamics based computing method, and categorize different techniques that we have introduced to realize logic, functionality, and programmability. We discuss the role and importance of coupled dynamics in networks of biological excitable cells, and then review our simple coupled dynamics based method for computing. In this paper, for the first time, we show how dynamics can be used and programmed to implement computation in any given base, including but not limited to base two.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

PubMed

Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

2014-10-06

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

PubMed Central

Read, Mark; Andrews, Paul S.; Timmis, Jon; Kumar, Vipin

2014-01-01

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology. PMID:25142524

In Silico Dynamics: computer simulation in a Virtual Embryo (SOT)

EPA Science Inventory

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require preci...

Biologically inspired dynamic material systems.

PubMed

Studart, André R

2015-03-09

Numerous examples of material systems that dynamically interact with and adapt to the surrounding environment are found in nature, from hair-based mechanoreceptors in animals to self-shaping seed dispersal units in plants to remodeling bone in vertebrates. Inspired by such fascinating biological structures, a wide range of synthetic material systems have been created to replicate the design concepts of dynamic natural architectures. Examples of biological structures and their man-made counterparts are herein revisited to illustrate how dynamic and adaptive responses emerge from the intimate microscale combination of building blocks with intrinsic nanoscale properties. By using top-down photolithographic methods and bottom-up assembly approaches, biologically inspired dynamic material systems have been created 1) to sense liquid flow with hair-inspired microelectromechanical systems, 2) to autonomously change shape by utilizing plantlike heterogeneous architectures, 3) to homeostatically influence the surrounding environment through self-regulating adaptive surfaces, and 4) to spatially concentrate chemical species by using synthetic microcompartments. The ever-increasing complexity and remarkable functionalities of such synthetic systems offer an encouraging perspective to the rich set of dynamic and adaptive properties that can potentially be implemented in future man-made material systems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interlinkages between bacterial populations dynamics and the operational parameters in a moving bed membrane bioreactor treating urban sewage.

PubMed

Reboleiro-Rivas, P; Martín-Pascual, J; Morillo, J A; Juárez-Jiménez, B; Poyatos, J M; Rodelas, B; González-López, J

2016-01-01

Bacteria are key players in biological wastewater treatments (WWTs), thus a firm knowledge of the bacterial population dynamics is crucial to understand environmental/operational factors affecting the efficiency and stability of the biological depuration process. Unfortunately, little is known about the microbial ecology of the advanced biological WWTs combining suspended biomass (SB) and attached biofilms (AB). This study explored in depth the bacterial community structure and population dynamics in each biomass fraction from a pilot-scale moving bed membrane bioreactor (MBMBR) treating municipal sewage, by means of temperature-gradient gel electrophoresis (TGGE) and 454-pyrosequencing. Eight experimental phases were conducted, combining different carrier filling ratios, hydraulic retention times and concentrations of mixed liquor total suspended solids. The bacterial community, dominated by Proteobacteria (20.9-53.8%) and Actinobacteria (20.6-57.6%), was very similar in both biomass fractions and able to maintain its functional stability under all the operating conditions, ensuring a successful and steady depuration process. Multivariate statistical analysis demonstrated that solids concentration, carrier filling ratio, temperature and organic matter concentration in the influent were the significant factors explaining population dynamics. Bacterial diversity increased as carrier filling ratio increased (from 20% to 35%, v/v), and solids concentration was the main factor triggering the shifts of the community structure. These findings provide new insights on the influence of operational parameters on the biology of the innovative MBMBRs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Study of the benzocaine transfer from aqueous solution to the interior of a biological membrane.

PubMed

Porasso, Rodolfo D; Bennett, W F Drew; Oliveira-Costa, S D; López Cascales, J J

2009-07-23

The precise molecular mechanism of general anesthetics remains unknown. It is therefore important to understand where molecules with anesthetic properties localize within biological membranes. We have determined the free energy profile of a benzocaine molecule (BZC) across a biological membrane using molecular dynamics simulation. We use an asymmetric phospholipid bilayer with DPPS in one leaflet of a DPPC bilayer (Lopez Cascales et al. J. Phys. Chem. B 2006, 110, 2358-2363) to model a biological bilayer. From the free energy profile, we predict the zone of actuation of a benzocaine is located in the hydrocarbon region or at the end of the lipid head, depending of the presence of charged lipids (DPPS) in the leaflet. We observe a moderate increase in the disorder of the membrane and in particular an increase in the disorder of DPPS. Static and dynamic physicochemical properties of the benzocaine, such as its dipole orientation, translational diffusion coefficient, and rotational relaxation time were measured.

On the structural affinity of macromolecules with different biological properties: molecular dynamics simulations of a series of TEM-1 mutants.

PubMed

Giampaolo, Alessia Di; Mazza, Fernando; Daidone, Isabella; Amicosante, Gianfranco; Perilli, Mariagrazia; Aschi, Massimiliano

2013-07-12

Molecular Dynamics simulations have been carried out in order to provide a molecular rationalization of the biological and thermodynamic differences observed for a class of TEM β-lactamases. In particular we have considered the TEM-1(wt), the single point mutants TEM-40 and TEM-19 representative of IRT and ESBL classes respectively, and TEM-1 mutant M182T, TEM-32 and TEM-20 which differ from the first three for the additional of M182T mutation. Results indicate that most of the thermodynamic, and probably biological behaviour of these systems arise from subtle effects which, starting from the alterations of the local interactions, produce drastic modifications of the conformational space spanned by the enzymes. The present study suggests that systems showing essentially the same secondary and tertiary structure may differentiate their chemical-biological activity essentially (and probably exclusively) on the basis of the thermal fluctuations occurring in their physiological environment. Copyright © 2013 Elsevier Inc. All rights reserved.

New scaling relation for information transfer in biological networks

PubMed Central

Kim, Hyunju; Davies, Paul; Walker, Sara Imari

2015-01-01

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

Nonlinear dynamics and damage induced properties of soft matter with application in oncology

NASA Astrophysics Data System (ADS)

Naimark, O.

2017-09-01

Molecular-morphological signs of oncogenesis could be linked to multiscale collective effects in molecular, cell and tissue related to defects (damage) dynamics. It was shown that nonlinear behavior of biological systems can be linked to the existence of characteristic collective open state modes providing the coherent expression dynamics. New type of criticality in nonequilibrium systems with defects—structural-scaling transition allows the definition of the `driving force' for a biological soft matter related to consolidated open states. The set of collective open states (breathers, autosolitons and blow-up modes) in the molecular ensembles provides the collective expression dynamics to attract the entire system (cell, tissue) toward a few preferred global states. The co-existence of three types of collective modes determines the multifractal scenario of biological soft matter dynamics. The appearance of `globally convergent' dynamics corresponding to the coherent behavior of multiscale blow-up open states (blow-up gene expression) leads to anomalous localized softening (blow-up localized damage) and the subjection of the cells (or tissue) to monofractal dynamics. This dynamics can be associated with cancer progression.

Interacting particle systems on graphs

NASA Astrophysics Data System (ADS)

Sood, Vishal

In this dissertation, the dynamics of socially or biologically interacting populations are investigated. The individual members of the population are treated as particles that interact via links on a social or biological network represented as a graph. The effect of the structure of the graph on the properties of the interacting particle system is studied using statistical physics techniques. In the first chapter, the central concepts of graph theory and social and biological networks are presented. Next, interacting particle systems that are drawn from physics, mathematics and biology are discussed in the second chapter. In the third chapter, the random walk on a graph is studied. The mean time for a random walk to traverse between two arbitrary sites of a random graph is evaluated. Using an effective medium approximation it is found that the mean first-passage time between pairs of sites, as well as all moments of this first-passage time, are insensitive to the density of links in the graph. The inverse of the mean-first passage time varies non-monotonically with the density of links near the percolation transition of the random graph. Much of the behavior can be understood by simple heuristic arguments. Evolutionary dynamics, by which mutants overspread an otherwise uniform population on heterogeneous graphs, are studied in the fourth chapter. Such a process underlies' epidemic propagation, emergence of fads, social cooperation or invasion of an ecological niche by a new species. The first part of this chapter is devoted to neutral dynamics, in which the mutant genotype does not have a selective advantage over the resident genotype. The time to extinction of one of the two genotypes is derived. In the second part of this chapter, selective advantage or fitness is introduced such that the mutant genotype has a higher birth rate or a lower death rate. This selective advantage leads to a dynamical competition in which selection dominates for large populations, while for small populations the dynamics are similar to the neutral case. The likelihood for the fitter mutants to drive the resident genotype to extinction is calculated.

BROMOC suite: Monte Carlo/Brownian dynamics suite for studies of ion permeation and DNA transport in biological and artificial pores with effective potentials.

PubMed

De Biase, Pablo M; Markosyan, Suren; Noskov, Sergei

2015-02-05

The transport of ions and solutes by biological pores is central for cellular processes and has a variety of applications in modern biotechnology. The time scale involved in the polymer transport across a nanopore is beyond the accessibility of conventional MD simulations. Moreover, experimental studies lack sufficient resolution to provide details on the molecular underpinning of the transport mechanisms. BROMOC, the code presented herein, performs Brownian dynamics simulations, both serial and parallel, up to several milliseconds long. BROMOC can be used to model large biological systems. IMC-MACRO software allows for the development of effective potentials for solute-ion interactions based on radial distribution function from all-atom MD. BROMOC Suite also provides a versatile set of tools to do a wide variety of preprocessing and postsimulation analysis. We illustrate a potential application with ion and ssDNA transport in MspA nanopore. © 2014 Wiley Periodicals, Inc.

Chemotaxis in densely populated tissue determines germinal center anatomy and cell motility: a new paradigm for the development of complex tissues.

PubMed

Hawkins, Jared B; Jones, Mark T; Plassmann, Paul E; Thorley-Lawson, David A

2011-01-01

Germinal centers (GCs) are complex dynamic structures that form within lymph nodes as an essential process in the humoral immune response. They represent a paradigm for studying the regulation of cell movement in the development of complex anatomical structures. We have developed a simulation of a modified cyclic re-entry model of GC dynamics which successfully employs chemotaxis to recapitulate the anatomy of the primary follicle and the development of a mature GC, including correctly structured mantle, dark and light zones. We then show that correct single cell movement dynamics (including persistent random walk and inter-zonal crossing) arise from this simulation as purely emergent properties. The major insight of our study is that chemotaxis can only achieve this when constrained by the known biological properties that cells are incompressible, exist in a densely packed environment, and must therefore compete for space. It is this interplay of chemotaxis and competition for limited space that generates all the complex and biologically accurate behaviors described here. Thus, from a single simple mechanism that is well documented in the biological literature, we can explain both higher level structure and single cell movement behaviors. To our knowledge this is the first GC model that is able to recapitulate both correctly detailed anatomy and single cell movement. This mechanism may have wide application for modeling other biological systems where cells undergo complex patterns of movement to produce defined anatomical structures with sharp tissue boundaries.

The Self-Organizing Psyche: Nonlinear and Neurobiological Contributions to Psychoanalysis

NASA Astrophysics Data System (ADS)

Stein, A. H.

Sigmund Freud attempted to align nineteenth century biology (and the dynamically conservative, continuous, Newtonian mechanics that underlie it) with discontinuous conscious experience. His tactics both set the future course for psychoanalytic development and introduced seemingly intractable complications into its metapsychology. In large part, these arose from what we now recognize were biological errors and dynamical oversimplifications amid his physical assumptions. Their correction, brought about by integrating nonlinear dynamics and neuro-biological research findings with W. Bion's reading of metapsychology, fundamentally supports a psychoanalysis based upon D. W. Winnicott's ideas surrounding play within transitional space.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Dynamic microenvironments: the fourth dimension.

PubMed

Tibbitt, Mark W; Anseth, Kristi S

2012-11-14

The extracellular space, or cell microenvironment, choreographs cell behavior through myriad controlled signals, and aberrant cues can result in dysfunction and disease. For functional studies of human cell biology or expansion and delivery of cells for therapeutic purposes, scientists must decipher this intricate map of microenvironment biology and develop ways to mimic these functions in vitro. In this Perspective, we describe technologies for four-dimensional (4D) biology: cell-laden matrices engineered to recapitulate tissue and organ function in 3D space and over time.

ADAM: analysis of discrete models of biological systems using computer algebra.

PubMed

Hinkelmann, Franziska; Brandon, Madison; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

2011-07-20

Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics.

Acquire an Bruker Dimension FastScanTM Atomic Force Microscope (AFM) for Materials, Physical and Biological Science Research and Education

DTIC Science & Technology

2016-04-14

study dynamic events such as melting, evaporation, crystallization, dissolution, self-assembly, membrane disruption, sample movement tracking. To... polymeric hairy nanopraticle, suprastructures REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S...the AFM will permit us to study dynamic events such as melting, evaporation, crystallization, dissolution, self-assembly, membrane disruption, sample

Brownian dynamics simulation of protein diffusion in crowded environments

NASA Astrophysics Data System (ADS)

Mereghetti, Paolo; Wade, Rebecca C.

2013-02-01

High macromolecular concentrations are a distinguishing feature of living organisms. Understanding how the high concentration of solutes affects the dynamic properties of biological macromolecules is fundamental for the comprehension of biological processes in living systems. We first describe the development of a Brownian dynamics simulation methodology to investigate the dynamic and structural properties of protein solutions using atomic-detail protein structures. We then discuss insights obtained from applying this approach to simulation of solutions of a range of types of proteins.

Watershed Dynamics. Teacher Edition (Includes the Full Student Edition). Cornell Scientific Inquiry Series

ERIC Educational Resources Information Center

Carlsen, William S.; Trautmann, Nancy M.

2004-01-01

Whether you're a stream studies novice or a veteran aquatic monitor, "Watershed Dynamics" gives you abundant practical resources to extend your students' investigations into local water quality and land-use issues. This two-part set is ideal for teaching biological and ecological concepts and research techniques. It also shows how the interplay…

Estimation of the spatial autocorrelation function: consequences of sampling dynamic populations in space and time

Treesearch

Patrick C. Tobin

2004-01-01

The estimation of spatial autocorrelation in spatially- and temporally-referenced data is fundamental to understanding an organism's population biology. I used four sets of census field data, and developed an idealized space-time dynamic system, to study the behavior of spatial autocorrelation estimates when a practical method of sampling is employed. Estimates...

Bimolecular dynamics by computer analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eilbeck, J.C.; Lomdahl, P.S.; Scott, A.C.

1984-01-01

As numerical tools (computers and display equipment) become more powerful and the atomic structures of important biological molecules become known, the importance of detailed computation of nonequilibrium biomolecular dynamics increases. In this manuscript we report results from a well developed study of the hydrogen bonded polypeptide crystal acetanilide, a model protein. Directions for future research are suggested. 9 references, 6 figures.

Computational and theoretical approaches for studies of a lipid recognition protein on biological membranes

PubMed Central

Yamamoto, Eiji

2017-01-01

Many cellular functions, including cell signaling and related events, are regulated by the association of peripheral membrane proteins (PMPs) with biological membranes containing anionic lipids, e.g., phosphatidylinositol phosphate (PIP). This association is often mediated by lipid recognition modules present in many PMPs. Here, I summarize computational and theoretical approaches to investigate the molecular details of the interactions and dynamics of a lipid recognition module, the pleckstrin homology (PH) domain, on biological membranes. Multiscale molecular dynamics simulations using combinations of atomistic and coarse-grained models yielded results comparable to those of actual experiments and could be used to elucidate the molecular mechanisms of the formation of protein/lipid complexes on membrane surfaces, which are often difficult to obtain using experimental techniques. Simulations revealed some modes of membrane localization and interactions of PH domains with membranes in addition to the canonical binding mode. In the last part of this review, I address the dynamics of PH domains on the membrane surface. Local PIP clusters formed around the proteins exhibit anomalous fluctuations. This dynamic change in protein-lipid interactions cause temporally fluctuating diffusivity of proteins, i.e., the short-term diffusivity of the bound protein changes substantially with time, and may in turn contribute to the formation/dissolution of protein complexes in membranes. PMID:29159013

The First Time Ever I Saw Your Feet: Inversion Effect in Newborns' Sensitivity to Biological Motion

ERIC Educational Resources Information Center

Bardi, Lara; Regolin, Lucia; Simion, Francesca

2014-01-01

Inversion effect in biological motion perception has been recently attributed to an innate sensitivity of the visual system to the gravity-dependent dynamic of the motion. However, the specific cues that determine the inversion effect in naïve subjects were never investigated. In the present study, we have assessed the contribution of the local…

Dynamical role of phosphorylation on serine/threonine-proline Pin1 substrates from constant force molecular dynamics simulations.

PubMed

Velazquez, Hector A; Hamelberg, Donald

2015-02-21

Cis-trans isomerization of peptidyl-prolyl bonds of the protein backbone plays an important role in numerous biological processes. Cis-trans isomerization can be the rate-limiting step due its extremely slow dynamics, compared to the millisecond time scale of many processes, and is catalyzed by a widely studied family of peptidyl-prolyl cis-trans isomerase enzymes. Also, mechanical forces along the peptide chain can speed up the rate of isomerization, resulting in "mechanical catalysis," and have been used to study peptidyl-prolyl cis-trans isomerization and other mechanical properties of proteins. Here, we use constant force molecular dynamics simulations to study the dynamical effects of phosphorylation on serine/threonine-proline protein motifs that are involved in the function of many proteins and have been implicated in many aberrant biological processes. We show that the rate of cis-trans isomerization is slowed down by phosphorylation, in excellent agreement with experiments. We use a well-grounded theory to describe the force dependent rate of isomerization. The calculated rates at zero force are also in excellent agreement with experimentally measured rates, providing additional validation of the models and force field parameters. Our results suggest that the slowdown in the rate upon phosphorylation is mainly due to an increase in the friction along the peptidyl-prolyl bond angle during isomerization. Our results provide a microscopic description of the dynamical effects of post-translational phosphorylation on cis-trans isomerization and insights into the properties of proteins under tension.

Dynamical role of phosphorylation on serine/threonine-proline Pin1 substrates from constant force molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Velazquez, Hector A.; Hamelberg, Donald

2015-02-01

Cis-trans isomerization of peptidyl-prolyl bonds of the protein backbone plays an important role in numerous biological processes. Cis-trans isomerization can be the rate-limiting step due its extremely slow dynamics, compared to the millisecond time scale of many processes, and is catalyzed by a widely studied family of peptidyl-prolyl cis-trans isomerase enzymes. Also, mechanical forces along the peptide chain can speed up the rate of isomerization, resulting in "mechanical catalysis," and have been used to study peptidyl-prolyl cis-trans isomerization and other mechanical properties of proteins. Here, we use constant force molecular dynamics simulations to study the dynamical effects of phosphorylation on serine/threonine-proline protein motifs that are involved in the function of many proteins and have been implicated in many aberrant biological processes. We show that the rate of cis-trans isomerization is slowed down by phosphorylation, in excellent agreement with experiments. We use a well-grounded theory to describe the force dependent rate of isomerization. The calculated rates at zero force are also in excellent agreement with experimentally measured rates, providing additional validation of the models and force field parameters. Our results suggest that the slowdown in the rate upon phosphorylation is mainly due to an increase in the friction along the peptidyl-prolyl bond angle during isomerization. Our results provide a microscopic description of the dynamical effects of post-translational phosphorylation on cis-trans isomerization and insights into the properties of proteins under tension.

Linking Biological and Cognitive Aging: Toward Improving Characterizations of Developmental Time

PubMed Central

DeCarlo, Correne A.; Dixon, Roger A.

2011-01-01

Objectives. Chronological age is the most frequently employed predictor in life-span developmental research, despite repeated assertions that it is best conceived as a proxy for true mechanistic changes that influence cognition across time. The present investigation explores the potential that selected functional biomarkers may contribute to the more effective conceptual and operational definitions of developmental time. Methods. We used data from the Victoria Longitudinal Study to explore both static and dynamic biological or physiological markers that arguably influence process-specific mechanisms underlying cognitive changes in late life. Multilevel models were fit to test the dynamic coupling between change in theoretically relevant biomarkers (e.g., grip strength, pulmonary function) and change in select cognitive measures (e.g., executive function, episodic and semantic memory). Results. Results showed that, independent of the passage of developmental time (indexed as years in study), significant time-varying covariation was observed linking corresponding declines for select cognitive outcomes and biological markers. Discussion. Our findings support the interpretation that cognitive decline is not due to chronological aging per se but rather reflects multiple causal factors from a broad range of biological and physical health domains that operate along the age continuum. PMID:21743053

Atomic detail brownian dynamics simulations of concentrated protein solutions with a mean field treatment of hydrodynamic interactions.

PubMed

Mereghetti, Paolo; Wade, Rebecca C

2012-07-26

High macromolecular concentrations are a distinguishing feature of living organisms. Understanding how the high concentration of solutes affects the dynamic properties of biological macromolecules is fundamental for the comprehension of biological processes in living systems. In this paper, we describe the implementation of mean field models of translational and rotational hydrodynamic interactions into an atomically detailed many-protein brownian dynamics simulation method. Concentrated solutions (30-40% volume fraction) of myoglobin, hemoglobin A, and sickle cell hemoglobin S were simulated, and static structure factors, oligomer formation, and translational and rotational self-diffusion coefficients were computed. Good agreement of computed properties with available experimental data was obtained. The results show the importance of both solvent mediated interactions and weak protein-protein interactions for accurately describing the dynamics and the association properties of concentrated protein solutions. Specifically, they show a qualitative difference in the translational and rotational dynamics of the systems studied. Although the translational diffusion coefficient is controlled by macromolecular shape and hydrodynamic interactions, the rotational diffusion coefficient is affected by macromolecular shape, direct intermolecular interactions, and both translational and rotational hydrodynamic interactions.

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Molecular dynamics simulations: advances and applications

PubMed Central

Hospital, Adam; Goñi, Josep Ramon; Orozco, Modesto; Gelpí, Josep L

2015-01-01

Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed. PMID:26604800

Generation of Rab-based transgenic lines for in vivo studies of endosome biology in zebrafish

PubMed Central

Clark, Brian S.; Winter, Mark; Cohen, Andrew R.; Link, Brian A.

2011-01-01

The Rab family of small GTPases function as molecular switches regulating membrane and protein trafficking. Individual Rab isoforms define and are required for specific endosomal compartments. To facilitate in vivo investigation of specific Rab proteins, and endosome biology in general, we have generated transgenic zebrafish lines to mark and manipulate Rab proteins. We also developed software to track and quantify endosome dynamics within time-lapse movies. The established transgenic lines ubiquitously express EGFP fusions of Rab5c (early endosomes), Rab11a (recycling endosomes), and Rab7 (late endosomes) to study localization and dynamics during development. Additionally, we generated UAS-based transgenic lines expressing constitutive active (CA) and dominant negative (DN) versions for each of these Rab proteins. Predicted localization and functional consequences for each line were verified through a variety of assays, including lipophilic dye uptake and Crumbs2a localization. In summary, we have established a toolset for in vivo analyses of endosome dynamics and functions. PMID:21976318

All-atom molecular dynamics of the HBV capsid reveals insights into biological function and cryo-EM resolution limits

PubMed Central

Perilla, Juan R; Schlicksup, Christopher John; Venkatakrishnan, Balasubramanian; Zlotnick, Adam; Schulten, Klaus

2018-01-01

The hepatitis B virus capsid represents a promising therapeutic target. Experiments suggest the capsid must be flexible to function; however, capsid structure and dynamics have not been thoroughly characterized in the absence of icosahedral symmetry constraints. Here, all-atom molecular dynamics simulations are leveraged to investigate the capsid without symmetry bias, enabling study of capsid flexibility and its implications for biological function and cryo-EM resolution limits. Simulation results confirm flexibility and reveal a propensity for asymmetric distortion. The capsid’s influence on ionic species suggests a mechanism for modulating the display of cellular signals and implicates the capsid’s triangular pores as the location of signal exposure. A theoretical image reconstruction performed using simulated conformations indicates how capsid flexibility may limit the resolution of cryo-EM. Overall, the present work provides functional insight beyond what is accessible to experimental methods and raises important considerations regarding asymmetry in structural studies of icosahedral virus capsids. PMID:29708495

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Development of a Hands-On Survey Course in the Physics of Living Systems

NASA Astrophysics Data System (ADS)

Matthews, Megan; Goldman, Daniel I.

Due to the widespread availability and technological capabilities of modern smartphones, many biophysical systems can be investigated using easily accessible, low-cost, and/or ``homemade'' equipment. Our survey course is structured to provide students with an overview of research in the physics of living systems, emphasizing the interplay between measurement, mechanism, and modeling required to understand principles at the intersection of physics and biology. The course proceeds through seven modules each consisting of one week of lectures and one week of hands-on experiments, called ``microlabs''. Using smartphones, Arduinos, and 3D printed materials students create their own laboratory equipment, including a 150X van Leeuwenhoek microscope, a shaking incubator, and an oscilloscope, and then use them to study biological systems ranging in length scales from nanometers to meters. These systems include population dynamics of rotifer/algae cultures, experimental evolution of multicellularity in budding yeast, and the bio- & neuromechanics involved in animal locomotion, among others. In each module, students are introduced to fundamental biological and physical concepts as well as theoretical and computational tools (nonlinear dynamics, molecular dynamics simulation, and statistical mechanics). At the end of the course, students apply these concepts and tools to the creation of their own microlab that integrates hands-on experimentation and modeling in the study of their chosen biophysical system.

Intravital microscopy: a novel tool to study cell biology in living animals.

PubMed

Weigert, Roberto; Sramkova, Monika; Parente, Laura; Amornphimoltham, Panomwat; Masedunskas, Andrius

2010-05-01

Intravital microscopy encompasses various optical microscopy techniques aimed at visualizing biological processes in live animals. In the last decade, the development of non-linear optical microscopy resulted in an enormous increase of in vivo studies, which have addressed key biological questions in fields such as neurobiology, immunology and tumor biology. Recently, few studies have shown that subcellular processes can be imaged dynamically in the live animal at a resolution comparable to that achieved in cell cultures, providing new opportunities to study cell biology under physiological conditions. The overall aim of this review is to give the reader a general idea of the potential applications of intravital microscopy with a particular emphasis on subcellular imaging. An overview of some of the most exciting studies in this field will be presented using resolution as a main organizing criterion. Indeed, first we will focus on those studies in which organs were imaged at the tissue level, then on those focusing on single cells imaging, and finally on those imaging subcellular organelles and structures.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

Energy Landscape of the Prion Protein Helix 1 Probed by Metadynamics and NMR

PubMed Central

Camilloni, Carlo; Schaal, Daniel; Schweimer, Kristian; Schwarzinger, Stephan; De Simone, Alfonso

2012-01-01

The characterization of the structural dynamics of proteins, including those that present a substantial degree of disorder, is currently a major scientific challenge. These dynamics are biologically relevant and govern the majority of functional and pathological processes. We exploited a combination of enhanced molecular simulations of metadynamics and NMR measurements to study heterogeneous states of proteins and peptides. In this way, we determined the structural ensemble and free-energy landscape of the highly dynamic helix 1 of the prion protein (PrP-H1), whose misfolding and aggregation are intimately connected to a group of neurodegenerative disorders known as transmissible spongiform encephalopathies. Our combined approach allowed us to dissect the factors that govern the conformational states of PrP-H1 in solution, and the implications of these factors for prion protein misfolding and aggregation. The results underline the importance of adopting novel integrated approaches that take advantage of experiments and theory to achieve a comprehensive characterization of the structure and dynamics of biological macromolecules. PMID:22225810

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms☆

PubMed Central

Mori, Takaharu; Miyashita, Naoyuki; Im, Wonpil; Feig, Michael; Sugita, Yuji

2016-01-01

This paper reviews various enhanced conformational sampling methods and explicit/implicit solvent/membrane models, as well as their recent applications to the exploration of the structure and dynamics of membranes and membrane proteins. Molecular dynamics simulations have become an essential tool to investigate biological problems, and their success relies on proper molecular models together with efficient conformational sampling methods. The implicit representation of solvent/membrane environments is reasonable approximation to the explicit all-atom models, considering the balance between computational cost and simulation accuracy. Implicit models can be easily combined with replica-exchange molecular dynamics methods to explore a wider conformational space of a protein. Other molecular models and enhanced conformational sampling methods are also briefly discussed. As application examples, we introduce recent simulation studies of glycophorin A, phospholamban, amyloid precursor protein, and mixed lipid bilayers and discuss the accuracy and efficiency of each simulation model and method. This article is part of a Special Issue entitled: Membrane Proteins. Guest Editors: J.C. Gumbart and Sergei Noskov. PMID:26766517

Static and dynamic body image in bulimia nervosa: mental representation of body dimensions and biological motion patterns.

PubMed

Vocks, Silja; Legenbauer, Tanja; Rüddel, Heinz; Troje, Nikolaus F

2007-01-01

The aim of the present study was to find out whether in bulimia nervosa the perceptual component of a disturbed body image is restricted to the overestimation of one's own body dimensions (static body image) or can be extended to a misperception of one's own motion patterns (dynamic body image). Participants with bulimia nervosa (n = 30) and normal controls (n = 55) estimated their body dimensions by means of a photo distortion technique and their walking patterns using a biological motion distortion device. Not only did participants with bulimia nervosa overestimate their own body dimensions, but also they perceived their own motion patterns corresponding to a higher BMI than did controls. Static body image was correlated with shape/weight concerns and drive for thinness, whereas dynamic body image was associated with social insecurity and body image avoidance. In bulimia nervosa, body image disturbances can be extended to a dynamic component. (c) 2006 by Wiley Periodicals, Inc.

Neurobiology of dynamic psychotherapy: an integration possible?

PubMed

Mundo, Emanuela

2006-01-01

In the last decades, Kandel's innovative experiments have demonstrated that brain structures and synaptic connections are dynamic. Synapses can be modified by a wide variety of environmental factors, including learning and memory processes. The hypothesis that dynamic psychotherapy process involves memory and learning processes has opened the possibility of a dialogue between neuroscience and psychoanalysis and related psychotherapy techniques. The primary aim of the present article is to critically review the more recent data on neurobiological effects of dynamic psychotherapy in psychiatric disorders. Relevant literature has been selected using the databases currently available online (i.e., PubMed). The literature search has been limited to the past 10 years and to genetic, molecular biology, and neuroimaging studies that have addressed the issue of changes induced by psychotherapy. Most of the genetic studies on mental disorders have demonstrated that psychiatric conditions result from a complex interaction of genetic susceptibility and environmental effects. For none of the many psychiatric conditions investigated has a purely genetic background been found. Molecular biology studies have indicated that gene expression is influenced by several environmental factors, including early experiences, traumas, learning, and memory processes. Neuroimaging studies (using fMRI and PET) have found that not only cognitive but also dynamic psychotherapy has measurable effects on the brain. In addition, psychotherapy may modify brain function and metabolism in specific brain areas. Most of these studies have considered patients with major depressive disorders and compared the effects of psychotherapy with the effect of standard pharmacotherapy. In conclusion, recent results from neuroscience studies have suggested that dynamic psychotherapy has a significant impact on brain function and metabolism in specific brain areas. The possible applications and developments of this new area of research toward the conceptualization of an integrative approach to treatment of psychiatric disorders are discussed.

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty.

PubMed

Mdluli, Thembi; Buzzard, Gregery T; Rundell, Ann E

2015-09-01

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm's scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty

PubMed Central

Mdluli, Thembi; Buzzard, Gregery T.; Rundell, Ann E.

2015-01-01

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements. PMID:26379275

Dynamical systems, attractors, and neural circuits.

PubMed

Miller, Paul

2016-01-01

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

Inference of physical/biological dynamics from synthetic ocean colour images

NASA Technical Reports Server (NTRS)

Eert, J.; Holloway, G.; Gower, J. F. R.; Denman, K.; Abbott, M.

1987-01-01

High resolution numerical experiments with well resolved eddies are performed including advection of a biologically active plankton field. Shelf wave propagation and bottom topographic features are included. The resulting synthetic ocean color fields are examined for sensitivity to the (known) underlying physical dynamics.

A framework for stochastic simulations and visualization of biological electron-transfer dynamics

NASA Astrophysics Data System (ADS)

Nakano, C. Masato; Byun, Hye Suk; Ma, Heng; Wei, Tao; El-Naggar, Mohamed Y.

2015-08-01

Electron transfer (ET) dictates a wide variety of energy-conversion processes in biological systems. Visualizing ET dynamics could provide key insight into understanding and possibly controlling these processes. We present a computational framework named VizBET to visualize biological ET dynamics, using an outer-membrane Mtr-Omc cytochrome complex in Shewanella oneidensis MR-1 as an example. Starting from X-ray crystal structures of the constituent cytochromes, molecular dynamics simulations are combined with homology modeling, protein docking, and binding free energy computations to sample the configuration of the complex as well as the change of the free energy associated with ET. This information, along with quantum-mechanical calculations of the electronic coupling, provides inputs to kinetic Monte Carlo (KMC) simulations of ET dynamics in a network of heme groups within the complex. Visualization of the KMC simulation results has been implemented as a plugin to the Visual Molecular Dynamics (VMD) software. VizBET has been used to reveal the nature of ET dynamics associated with novel nonequilibrium phase transitions in a candidate configuration of the Mtr-Omc complex due to electron-electron interactions.

Protonation-induced stereoisomerism in nicotine: Conformational studies using classical (AMBER) and ab initio (Car Parrinello) molecular dynamics

NASA Astrophysics Data System (ADS)

Hammond, Philip S.; Wu, Yudong; Harris, Rebecca; Minehardt, Todd J.; Car, Roberto; Schmitt, Jeffrey D.

2005-01-01

A variety of biologically active small molecules contain prochiral tertiary amines, which become chiral centers upon protonation. S-nicotine, the prototypical nicotinic acetylcholine receptor agonist, produces two diastereomers on protonation. Results, using both classical (AMBER) and ab initio (Car-Parrinello) molecular dynamical studies, illustrate the significant differences in conformational space explored by each diastereomer. As is expected, this phenomenon has an appreciable effect on nicotine's energy hypersurface and leads to differentiation in molecular shape and divergent sampling. Thus, protonation induced isomerism can produce dynamic effects that may influence the behavior of a molecule in its interaction with a target protein. We also examine differences in the conformational dynamics for each diastereomer as quantified by both molecular dynamics methods.

Biological sensing and control of emission dynamics of quantum dot bioconjugates using arrays of long metallic nanorods.

PubMed

Sadeghi, Seyed M; Gutha, Rithvik R; Wing, Waylin J; Sharp, Christina; Capps, Lucas; Mao, Chuanbin

2017-01-01

We study biological sensing using plasmonic and photonic-plasmonic resonances of arrays of ultralong metallic nanorods and analyze the impact of these resonances on emission dynamics of quantum dot bioconjugates. We demonstrate that the LSPRs and plasmonic lattice modes of such array can be used to detect a single self-assembled monolayer of alkanethiol at the visible (550 nm) and near infrared (770 nm) range with well resolved shifts. We study adsorption of streptavidin-quantum dot conjugates to this monolayer, demonstrating that formation of nearly two dimensional arrays of quantum dots with limited emission blinking can lead to extra well-defined wavelength shifts in these modes. Using spectrally-resolved lifetime measurements we study the emission dynamics of such quantum dot bioconjugates within their monodispersed size distribution. We show that, despite their close vicinity to the nanorods, the rate of energy transfer from these quantum dots to nanorods is rather weak, while the plasmon field enhancement can be strong. Our results reveal that the nanorods present a strongly wavelength or size-dependent non-radiative decay channel to the quantum dot bioconjugates.

Viscoelastic characterization of soft biological materials

NASA Astrophysics Data System (ADS)

Nayar, Vinod Timothy

Progressive and irreversible retinal diseases are among the primary causes of blindness in the United States, attacking the cells in the eye that transform environmental light into neural signals for the optic pathway. Medical implants designed to restore visual function to afflicted patients can cause mechanical stress and ultimately damage to the host tissues. Research shows that an accurate understanding of the mechanical properties of the biological tissues can reduce damage and lead to designs with improved safety and efficacy. Prior studies on the mechanical properties of biological tissues show characterization of these materials can be affected by environmental, length-scale, time, mounting, stiffness, size, viscoelastic, and methodological conditions. Using porcine sclera tissue, the effects of environmental, time, and mounting conditions are evaluated when using nanoindentation. Quasi-static tests are used to measure reduced modulus during extended exposure to phosphate-buffered saline (PBS), as well as the chemical and mechanical analysis of mounting the sample to a solid substrate using cyanoacrylate. The less destructive nature of nanoindentation tests allows for variance of tests within a single sample to be compared to the variance between samples. The results indicate that the environmental, time, and mounting conditions can be controlled for using modified nanoindentation procedures for biological samples and are in line with averages modulus values from previous studies but with increased precision. By using the quasi-static and dynamic characterization capabilities of the nanoindentation setup, the additional stiffness and viscoelastic variables are measured. Different quasi-static control methods were evaluated along with maximum load parameters and produced no significant difference in reported reduced modulus values. Dynamic characterization tests varied frequency and quasi-static load, showing that the agar could be modeled as a linearly elastic material. The effects of sample stiffness were evaluated by testing both the quasi-static and dynamic mechanical properties of different concentration agar samples, ranging from 0.5% to 5.0%. The dynamic nanoindentation protocol showed some sensitivity to sample stiffness, but characterization remained consistently applicable to soft biological materials. Comparative experiments were performed on both 0.5% and 5.0% agar as well as porcine eye tissue samples using published dynamic macrocompression standards. By comparing these new tests to those obtained with nanoindentation, the effects due to length-scale, stiffness, size, viscoelastic, and methodological conditions are evaluated. Both testing methodologies can be adapted for the environmental and mounting conditions, but the limitations of standardized macro-scale tests are explored. The factors affecting mechanical characterization of soft and thin viscoelastic biological materials are researched and a comprehensive protocol is presented. This work produces material mechanical properties for use in improving future medical implant designs on a wide variety of biological tissue and materials.

Perspective: A Dynamics-Based Classification of Ventricular Arrhythmias

PubMed Central

Weiss, James N.; Garfinkel, Alan; Karagueuzian, Hrayr S.; Nguyen, Thao P.; Olcese, Riccardo; Chen, Peng-Sheng; Qu, Zhilin

2015-01-01

Despite key advances in the clinical management of life-threatening ventricular arrhythmias, culminating with the development of implantable cardioverter-defibrillators and catheter ablation techniques, pharmacologic/biologic therapeutics have lagged behind. The fundamental issue is that biological targets are molecular factors. Diseases, however, represent emergent properties at the scale of the organism that result from dynamic interactions between multiple constantly changing molecular factors. For a pharmacologic/biologic therapy to be effective, it must target the dynamic processes that underlie the disease. Here we propose a classification of ventricular arrhythmias that is based on our current understanding of the dynamics occurring at the subcellular, cellular, tissue and organism scales, which cause arrhythmias by simultaneously generating arrhythmia triggers and exacerbating tissue vulnerability. The goal is to create a framework that systematically links these key dynamic factors together with fixed factors (structural and electrophysiological heterogeneity) synergistically promoting electrical dispersion and increased arrhythmia risk to molecular factors that can serve as biological targets. We classify ventricular arrhythmias into three primary dynamic categories related generally to unstable Ca cycling, reduced repolarization, and excess repolarization, respectively. The clinical syndromes, arrhythmia mechanisms, dynamic factors and what is known about their molecular counterparts are discussed. Based on this framework, we propose a computational-experimental strategy for exploring the links between molecular factors, fixed factors and dynamic factors that underlie life-threatening ventricular arrhythmias. The ultimate objective is to facilitate drug development by creating an in silico platform to evaluate and predict comprehensively how molecular interventions affect not only a single targeted arrhythmia, but all primary arrhythmia dynamics categories as well as normal cardiac excitation-contraction coupling. PMID:25769672

Review of Canadian Light Source facilities for biological applications

NASA Astrophysics Data System (ADS)

Grochulski, Pawel; Fodje, Michel; Labiuk, Shaun; Wysokinski, Tomasz W.; Belev, George; Korbas, Malgorzata; Rosendahl, Scott M.

2017-11-01

The newly-created Biological and Life Sciences Department at the Canadian Light Source (CLS) encompasses four sets of beamlines devoted to biological studies ranging in scope from the atomic scale to cells, tissues and whole organisms. The Canadian Macromolecular Crystallography Facility (CMCF) consists of two beamlines devoted primarily to crystallographic studies of proteins and other macromolecules. The Mid-Infrared Spectromicroscopy (Mid-IR) beamline focusses on using infrared energy to obtain biochemical, structural and dynamical information about biological systems. The Bio-Medical Imaging and Therapy (BMIT) facility consists of two beamlines devoted to advanced imaging and X-ray therapy techniques. The Biological X-ray Absorption Spectroscopy (BioXAS) facility is being commissioned and houses three beamlines devoted to X-ray absorption spectroscopy and multi-mode X-ray fluorescence imaging. Together, these beamlines provide CLS Users with a powerful array of techniques to study today's most pressing biological questions. We describe these beamlines along with their current powerful features and envisioned future capabilities.

The multicolored Asian lady beetle, Harmonia axyridis: A review of its biology, uses in biological control, and non-target impacts

PubMed Central

Koch, R L

2003-01-01

Throughout the last century, the multicolored Asian lady beetle, Harmonia axyridis (Pallas) has been studied quite extensively, with topics ranging from genetics and evolution to population dynamics and applied biological control being covered. Much of the early work on H. axyridis was conducted in the native Asian range. From the 1980's to the present, numerous European and North American studies have added to the body of literature on H. axyridis. H. axyridis has recently gained attention in North America both as a biological control agent and as a pest. This literature review was compiled for two reasons. First, to assist other researchers as a reference, summarizing most of the voluminous body of literature on H. axyridis pertaining to its biology, life history, uses in biological control, and potential non-target impacts. Secondly, to be a case study on the impacts of an exotic generalist predator. PMID:15841248

Differences in the Nature of Body Image Disturbances between Female Obese Individuals with versus without a Comorbid Binge Eating Disorder: An Exploratory Study Including Static and Dynamic Aspects of Body Image

ERIC Educational Resources Information Center

Legenbauer, Tanja; Vocks, Silja; Betz, Sabrina; Puigcerver, Maria Jose Baguena; Benecke, Andrea; Troje, Nikolaus F.; Ruddel, Heinz

2011-01-01

Various components of body image were measured to assess body image disturbances in patients with obesity. To overcome limitations of previous studies, a photo distortion technique and a biological motion distortion device were included to assess static and dynamic aspects of body image. Questionnaires assessed cognitive-affective aspects, bodily…

The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems.

PubMed

Koehl, Patrice; Poitevin, Frédéric; Navaza, Rafael; Delarue, Marc

2017-03-14

Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.

Informing Biological Design by Integration of Systems and Synthetic Biology

PubMed Central

Smolke, Christina D.; Silver, Pamela A.

2011-01-01

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. PMID:21414477

THR ROLE OF SEABED DYNAMICS IN STRUCTURING A MESOHALINE MACROBENTIC INFAUNAL COMMUNITY

EPA Science Inventory

Estuaries are dynamic physical environments. The stability of the sediment-water interface is influenced by sources and rates of sediment delivery and reworking of sediments by currents, tides, waves and biology, but effects of disruption of this interface on benthic biology are...

The activities of the C-terminal regions of the formin protein disheveled-associated activator of morphogenesis (DAAM) in actin dynamics.

PubMed

Vig, Andrea Teréz; Földi, István; Szikora, Szilárd; Migh, Ede; Gombos, Rita; Tóth, Mónika Ágnes; Huber, Tamás; Pintér, Réka; Talián, Gábor Csaba; Mihály, József; Bugyi, Beáta

2017-08-18

Disheveled-associated activator of morphogenesis (DAAM) is a diaphanous-related formin protein essential for the regulation of actin cytoskeleton dynamics in diverse biological processes. The conserved formin homology 1 and 2 (FH1-FH2) domains of DAAM catalyze actin nucleation and processively mediate filament elongation. These activities are indirectly regulated by the N- and C-terminal regions flanking the FH1-FH2 domains. Recently, the C-terminal diaphanous-autoregulatory domain (DAD) and the C terminus (CT) of formins have also been shown to regulate actin assembly by directly interacting with actin. Here, to better understand the biological activities of DAAM, we studied the role of DAD-CT regions of Drosophila DAAM in its interaction with actin with in vitro biochemical and in vivo genetic approaches. We found that the DAD-CT region binds actin in vitro and that its main actin-binding element is the CT region, which does not influence actin dynamics on its own. However, we also found that it can tune the nucleating activity and the filament end-interaction properties of DAAM in an FH2 domain-dependent manner. We also demonstrate that DAD-CT makes the FH2 domain more efficient in antagonizing with capping protein. Consistently, in vivo data suggested that the CT region contributes to DAAM-mediated filopodia formation and dynamics in primary neurons. In conclusion, our results demonstrate that the CT region of DAAM plays an important role in actin assembly regulation in a biological context. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Linking Adverse Outcome Pathways to Dynamic Energy Budgets: A Conceptual Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murphy, Cheryl; Nisbet, Roger; Antczak, Philipp

Ecological risk assessment quantifies the likelihood of undesirable impacts of stressors, primarily at high levels of biological organization. Data used to inform ecological risk assessments come primarily from tests on individual organisms or from suborganismal studies, indicating a disconnect between primary data and protection goals. We know how to relate individual responses to population dynamics using individual-based models, and there are emerging ideas on how to make connections to ecosystem services. However, there is no established methodology to connect effects seen at higher levels of biological organization with suborganismal dynamics, despite progress made in identifying Adverse Outcome Pathways (AOPs) thatmore » link molecular initiating events to ecologically relevant key events. This chapter is a product of a working group at the National Center for Mathematical and Biological Synthesis (NIMBioS) that assessed the feasibility of using dynamic energy budget (DEB) models of individual organisms as a “pivot” connecting suborganismal processes to higher level ecological processes. AOP models quantify explicit molecular, cellular or organ-level processes, but do not offer a route to linking sub-organismal damage to adverse effects on individual growth, reproduction, and survival, which can be propagated to the population level through individual-based models. DEB models describe these processes, but use abstract variables with undetermined connections to suborganismal biology. We propose linking DEB and quantitative AOP models by interpreting AOP key events as measures of damage-inducing processes in a DEB model. Here, we present a conceptual model for linking AOPs to DEB models and review existing modeling tools available for both AOP and DEB.« less

Variation in External Representations as Part of the Classroom Lecture: An Investigation of Virtual Cell Animations in Introductory Photosynthesis Instruction

ERIC Educational Resources Information Center

Goff, Eric E.; Reindl, Katie M.; Johnson, Christina; McClean, Phillip; Offerdahl, Erika G.; Schroeder, Noah L.; White, Alan R.

2017-01-01

The use of external representations (ERs) to introduce concepts in undergraduate biology has become increasingly common. Two of the most prevalent are static images and dynamic animations. While previous studies comparing static images and dynamic animations have resulted in somewhat conflicting findings in regards to learning outcomes, the…

A Model to Study Articular Cartilage Mechanical and Biological Responses to Sliding Loads.

PubMed

Schätti, Oliver R; Gallo, Luigi M; Torzilli, Peter A

2016-08-01

In physiological conditions, joint function involves continuously moving contact areas over the tissue surface. Such moving contacts play an important role for the durability of the tissue. It is known that in pathological joints these motion paths and contact mechanics change. Nevertheless, limited information exists on the impact of such physiological and pathophysiological dynamic loads on cartilage mechanics and its subsequent biological response. We designed and validated a mechanical device capable of applying simultaneous compression and sliding forces onto cartilage explants to simulate moving joint contact. Tests with varying axial loads (1-4 kg) and sliding speeds (1-20 mm/s) were performed on mature viable bovine femoral condyles to investigate cartilage mechanobiological responses. High loads and slow sliding speeds resulted in highest cartilage deformations. Contact stress and effective cartilage moduli increased with increasing load and increasing speed. In a pilot study, changes in gene expression of extracellular matrix proteins were correlated with strain, contact stress and dynamic effective modulus. This study describes a mechanical test system to study the cartilage response to reciprocating sliding motion and will be helpful in identifying mechanical and biological mechanisms leading to the initiation and development of cartilage degeneration.

Incorporating modeling and simulations in undergraduate biophysical chemistry course to promote understanding of structure-dynamics-function relationships in proteins.

PubMed

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and simulations. In particular, modern computational tools are employed to elucidate the relationship between structure, dynamics, and function in proteins. Computer-based laboratory protocols that we introduced in three modules allow students to visualize the secondary, super-secondary, and tertiary structures of proteins, analyze non-covalent interactions in protein-ligand complexes, develop three-dimensional structural models (homology model) for new protein sequences and evaluate their structural qualities, and study proteins' intrinsic dynamics to understand their functions. In the fourth module, students are assigned to an authentic research problem, where they apply their laboratory skills (acquired in modules 1-3) to answer conceptual biophysical questions. Through this process, students gain in-depth understanding of protein dynamics-the missing link between structure and function. Additionally, the requirement of term papers sharpens students' writing and communication skills. Finally, these projects result in new findings that are communicated in peer-reviewed journals. © 2016 The International Union of Biochemistry and Molecular Biology.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Adsorption properties of biologically active derivatives of quaternary ammonium surfactants and their mixtures at aqueous/air interface II. Dynamics of adsorption, micelles dissociation and cytotoxicity of QDLS.

PubMed

Rojewska, Monika; Prochaska, Krystyna; Olejnik, Anna; Rychlik, Joanna

2014-07-01

The main aim of our study was analysis of adsorption dynamics of mixtures containing quaternary derivatives of lysosomotropic substance (QDLS). Two types of equimolar mixtures were considered: the ones containing two derivatives of lysosomotropic substances (DMALM-12 and DMGM-12) as well as the catanionic mixtures i.e. the systems containing QDLS and DBSNa. Dynamic surface tension measurements of surfactant mixtures were made. The results suggested that the diffusivity of the mixed system could be treated as the average value of rates of diffusion of individual components, micelles and ion pairs, which are present in the mixtures studied. Moreover, an attempt was made to explain the influence of the presence of micelles in the mixtures on their adsorption dynamics. The compounds examined show interesting biological properties which can be useful, especially for drug delivery in medical treatment. In vitro cytotoxic activities of the mixtures studied towards human cancer cells were evaluated. Most of the mixtures showed a high antiproliferative potential, especially the ones containing DMALM-12. Each cancer cell line used demonstrated different sensitivity to the same dose of the mixtures tested. Copyright © 2014 Elsevier B.V. All rights reserved.

Combined phosphorescence-holographic approach for singlet oxygen detection in biological media

NASA Astrophysics Data System (ADS)

Semenova, I. V.; Belashov, A. V.; Beltukova, D. M.; Petrov, N. V.; Vasyutinskii, O. S.

2015-06-01

The paper presents a novel combined approach aimed to detect and monitor singlet oxygen molecules in biological specimens by means of the simultaneous recording and monitoring of their deactivation dynamics in the two complementary channels: radiative and nonradiative. The approach involves both the direct registration of phosphorescence at the wavelength of about 1270 nm caused by radiative relaxation of excited singlet oxygen molecules and holographic recording of thermal disturbances in the medium produced by their nonradiative relaxation. The data provides a complete set of information on singlet oxygen location and dynamics in the medium. The approach was validated in the case study of photosensitized generation of singlet oxygen in onion cell structures.

Family Dynamics of the Stay-at-Home Father and Working Mother Relationship.

PubMed

Rushing, Cassie; Powell, Lisa

2015-09-01

A phenomenological qualitative study was utilized to explore family dynamics in stay-at-home father and working mother households. A total of 20 working mothers were asked to describe family interactions and daily routines with regard to their stay-at-home father and working mother dynamic. All participants were married, heterosexual women with biological children ages 1 to 4 and who worked outside the home and the father stayed home as primary caretaker and did not contribute financially. The study indicated that the family dynamic of a working mother and stay-at-home father provided a positive parent-child relationship, enhanced parenting cohesion, and enhanced quality time. © The Author(s) 2014.

Evolutionary game theory for physical and biological scientists. II. Population dynamics equations can be associated with interpretations

PubMed Central

Liao, David; Tlsty, Thea D.

2014-01-01

The use of mathematical equations to analyse population dynamics measurements is being increasingly applied to elucidate complex dynamic processes in biological systems, including cancer. Purely ‘empirical’ equations may provide sufficient accuracy to support predictions and therapy design. Nevertheless, interpretation of fitting equations in terms of physical and biological propositions can provide additional insights that can be used both to refine models that prove inconsistent with data and to understand the scope of applicability of models that validate. The purpose of this tutorial is to assist readers in mathematically associating interpretations with equations and to provide guidance in choosing interpretations and experimental systems to investigate based on currently available biological knowledge, techniques in mathematical and computational analysis and methods for in vitro and in vivo experiments. PMID:25097752

A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

PubMed

Nikdel, Ali; Braatz, Richard D; Budman, Hector M

2018-05-01

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).

Cell-Free Optogenetic Gene Expression System.

PubMed

Jayaraman, Premkumar; Yeoh, Jing Wui; Jayaraman, Sudhaghar; Teh, Ai Ying; Zhang, Jingyun; Poh, Chueh Loo

2018-04-20

Optogenetic tools provide a new and efficient way to dynamically program gene expression with unmatched spatiotemporal precision. To date, their vast potential remains untapped in the field of cell-free synthetic biology, largely due to the lack of simple and efficient light-switchable systems. Here, to bridge the gap between cell-free systems and optogenetics, we studied our previously engineered one component-based blue light-inducible Escherichia coli promoter in a cell-free environment through experimental characterization and mathematical modeling. We achieved >10-fold dynamic expression and demonstrated rapid and reversible activation of the target gene to generate oscillatory response. The deterministic model developed was able to recapitulate the system behavior and helped to provide quantitative insights to optimize dynamic response. This in vitro optogenetic approach could be a powerful new high-throughput screening technology for rapid prototyping of complex biological networks in both space and time without the need for chemical induction.

Transport properties and efficiency of elastically coupled particles in asymmetric periodic potentials

NASA Astrophysics Data System (ADS)

Igarashi, Akito; Tsukamoto, Shinji

2000-02-01

Biological molecular motors drive unidirectional transport and transduce chemical energy to mechanical work. In order to identify this energy conversion which is a common feature of molecular motors, many workers have studied various physical models, which consist of Brownian particles in spatially periodic potentials. Most of the models are, however, based on "single-particle" dynamics and too simple as models for biological motors, especially for actin-myosin motors, which cause muscle contraction. In this paper, particles coupled by elastic strings in an asymmetric periodic potential are considered as a model for the motors. We investigate the dynamics of the model and calculate the efficiency of energy conversion with the use of molecular dynamical method. In particular, we find that the velocity and efficiency of the elastically coupled particles where the natural length of the springs is incommensurable with the period of the periodic potential are larger than those of the corresponding single particle model.

Dynamic sensitivity analysis of biological systems

PubMed Central

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016

Systems cell biology

PubMed Central

Mast, Fred D.; Ratushny, Alexander V.

2014-01-01

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology. PMID:25225336

Nonlinear dynamics in ecosystem response to climatic change: Case studies and policy implications

USGS Publications Warehouse

Burkett, Virginia R.; Wilcox, Douglas A.; Stottlemyer, Robert; Barrow, Wylie; Fagre, Dan; Baron, Jill S.; Price, Jeff; Nielsen, Jennifer L.; Allen, Craig D.; Peterson, David L.; Ruggerone, Greg; Doyle, Thomas

2005-01-01

Many biological, hydrological, and geological processes are interactively linked in ecosystems. These ecological phenomena normally vary within bounded ranges, but rapid, nonlinear changes to markedly different conditions can be triggered by even small differences if threshold values are exceeded. Intrinsic and extrinsic ecological thresholds can lead to effects that cascade among systems, precluding accurate modeling and prediction of system response to climate change. Ten case studies from North America illustrate how changes in climate can lead to rapid, threshold-type responses within ecological communities; the case studies also highlight the role of human activities that alter the rate or direction of system response to climate change. Understanding and anticipating nonlinear dynamics are important aspects of adaptation planning since responses of biological resources to changes in the physical climate system are not necessarily proportional and sometimes, as in the case of complex ecological systems, inherently nonlinear.

Locomotion at Low Reynolds Number: Dynamics in Newtonian and Non-Newtonian Systems with Biomedical Applications

NASA Astrophysics Data System (ADS)

Gagnon, David A.

Swimming microorganisms such as bacteria, spermatozoa, algae, and nematodes are critical to ubiquitous biological phenomena such as disease and infection, ecosystem dynamics, and mammalian fertilization. While there has been much scientific and practical interest in studying these swimmers in Newtonian (water-like) fluids, there are fewer systematic experimental studies on swimming through non-Newtonian (non-water-like) fluids with biologically-relevant mechanical properties. These organisms commonly swim through viscoelastic, structured, or shear-rate-dependent fluids, such as blood, mucus, and living tissues. Furthermore, the small length scales of these organisms dictate that their motion is dominated by viscous forces and inertia is negligible. Using rheology, microscopy, particle tracking, and image processing techniques, we examine the interaction of low Reynolds number swimmers and non-Newtonian fluids including viscoelastic, locally-anisotropic, and shear-thinning fluids. We then apply our understanding of locomotion to the study of the genetic disease Spinal Muscular Atrophy.

Between soap bubbles and vesicles: The dynamics of freely floating smectic bubbles

NASA Astrophysics Data System (ADS)

Stannarius, Ralf; May, Kathrin; Harth, Kirsten; Trittel, Torsten

2013-03-01

The dynamics of droplets and bubbles, particularly on microscopic scales, are of considerable importance in biological, environmental, and technical contexts. We introduce freely floating bubbles of smectic liquid crystals and report their unique dynamic properties. Smectic bubbles can be used as simple models for dynamic studies of fluid membranes. In equilibrium, they form minimal surfaces like soap films. However, shape transformations of closed smectic membranes that change the surface area involve the formation and motion of molecular layer dislocations. These processes are slow compared to the capillary wave dynamics, therefore the effective surface tension is zero like in vesicles. Freely floating smectic bubbles are prepared from collapsing catenoid films and their dynamics is studied with optical high-speed imaging. Experiments are performed under normal gravity and in microgravity during parabolic flights. Supported by DLR within grant OASIS-Co.

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

PubMed Central

Rieffel, John A.; Valero-Cuevas, Francisco J.; Lipson, Hod

2010-01-01

Traditional engineering approaches strive to avoid, or actively suppress, nonlinear dynamic coupling among components. Biological systems, in contrast, are often rife with these dynamics. Could there be, in some cases, a benefit to high degrees of dynamical coupling? Here we present a distributed robotic control scheme inspired by the biological phenomenon of tensegrity-based mechanotransduction. This emergence of morphology-as-information-conduit or ‘morphological communication’, enabled by time-sensitive spiking neural networks, presents a new paradigm for the decentralized control of large, coupled, modular systems. These results significantly bolster, both in magnitude and in form, the idea of morphological computation in robotic control. Furthermore, they lend further credence to ideas of embodied anatomical computation in biological systems, on scales ranging from cellular structures up to the tendinous networks of the human hand. PMID:19776146

Integrative, Dynamic Structural Biology at Atomic Resolution—It’s About Time

PubMed Central

van den Bedem, Henry; Fraser, James S.

2015-01-01

Biomolecules adopt a dynamic ensemble of conformations, each with the potential to interact with binding partners or perform the chemical reactions required for a multitude of cellular functions. Recent advances in X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, and other techniques are helping us realize the dream of seeing—in atomic detail—how different parts of biomolecules exchange between functional sub-states using concerted motions. Integrative structural biology has advanced our understanding of the formation of large macromolecular complexes and how their components interact in assemblies by leveraging data from many low-resolution methods. Here, we review the growing opportunities for integrative, dynamic structural biology at the atomic scale, contending there is increasing synergistic potential between X-ray crystallography, NMR, and computer simulations to reveal a structural basis for protein conformational dynamics at high resolution. PMID:25825836

Synthetic biology: programming cells for biomedical applications.

PubMed

Hörner, Maximilian; Reischmann, Nadine; Weber, Wilfried

2012-01-01

The emerging field of synthetic biology is a novel biological discipline at the interface between traditional biology, chemistry, and engineering sciences. Synthetic biology aims at the rational design of complex synthetic biological devices and systems with desired properties by combining compatible, modular biological parts in a systematic manner. While the first engineered systems were mainly proof-of-principle studies to demonstrate the power of the modular engineering approach of synthetic biology, subsequent systems focus on applications in the health, environmental, and energy sectors. This review describes recent approaches for biomedical applications that were developed along the synthetic biology design hierarchy, at the level of individual parts, of devices, and of complex multicellular systems. It describes how synthetic biological parts can be used for the synthesis of drug-delivery tools, how synthetic biological devices can facilitate the discovery of novel drugs, and how multicellular synthetic ecosystems can give insight into population dynamics of parasites and hosts. These examples demonstrate how this new discipline could contribute to novel solutions in the biopharmaceutical industry.

Dynamic biological exposure indexes for n-hexane and 2,5-hexanedione, suggested by a physiologically based pharmacokinetic model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perbellini, L.; Mozzo, P.; Olivato, D.

Biological exposure index (BEI) of n-hexane was studied for accuracy using a physiologically based pharmacokinetic (PB-PK) model. The kinetics of n-hexane in alveolar air, blood, urine, and other tissues were simulated for different values of alveolar ventilations and also for constant and variable exposures. The kinetics of 2,5-hexanedione, the toxic n-hexane metabolite, were also simulated. The ranges of n-hexane concentrations in biological media and the urinary concentrations of 2,5-hexanedione are discussed in connection with a mean n-hexane exposure of 180 mg/m3 (50 ppm) (threshold limit value (TLV) suggested by American Conference of Governmental Industrial Hygienists (ACGIH) for 1988-89). The experimentalmore » and field data as well as those predicted by simulation with the PB-PK model were comparable. The physiological-pharmacokinetic simulations are used to propose the dynamic BEIs of n-hexane and 2,5-hexanedione. The use of simulation with PB-PK models enables a better understanding of the limits, advantages, and issues associated with biological monitoring of exposures to industrial solvents.« less

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

PubMed

Erguler, Kamil; Stumpf, Michael P H

2011-05-01

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

Enzymatic control of biological deposits in papermaking.

PubMed

Hatcher, H J

1984-01-01

Deposit control in the pulp and paper industry has traditionally been accomplished by the use of toxic biocides. A method has been found whereby biological deposits can be controlled by the use of an enzyme-based product. Numerous field studies have been conducted successfully and photographs prepared illustrating the process. The dynamics of deposit formation and problems associated with such formations have been the subject of considerable study. Development and control of deposit problems under different paper mill conditions using the chemical-biochemical approach will be discussed.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

PubMed

Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E; Conolly, Rory B

2010-02-01

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

Fractal avalanche ruptures in biological membranes

NASA Astrophysics Data System (ADS)

Gözen, Irep; Dommersnes, Paul; Czolkos, Ilja; Jesorka, Aldo; Lobovkina, Tatsiana; Orwar, Owe

2010-11-01

Bilayer membranes envelope cells as well as organelles, and constitute the most ubiquitous biological material found in all branches of the phylogenetic tree. Cell membrane rupture is an important biological process, and substantial rupture rates are found in skeletal and cardiac muscle cells under a mechanical load. Rupture can also be induced by processes such as cell death, and active cell membrane repair mechanisms are essential to preserve cell integrity. Pore formation in cell membranes is also at the heart of many biomedical applications such as in drug, gene and short interfering RNA delivery. Membrane rupture dynamics has been studied in bilayer vesicles under tensile stress, which consistently produce circular pores. We observed very different rupture mechanics in bilayer membranes spreading on solid supports: in one instance fingering instabilities were seen resulting in floral-like pores and in another, the rupture proceeded in a series of rapid avalanches causing fractal membrane fragmentation. The intermittent character of rupture evolution and the broad distribution in avalanche sizes is consistent with crackling-noise dynamics. Such noisy dynamics appear in fracture of solid disordered materials, in dislocation avalanches in plastic deformations and domain wall magnetization avalanches. We also observed similar fractal rupture mechanics in spreading cell membranes.

Disentangling physical and biological drivers of phytoplankton dynamics in a coastal system.

PubMed

Cianelli, Daniela; D'Alelio, Domenico; Uttieri, Marco; Sarno, Diana; Zingone, Adriana; Zambianchi, Enrico; d'Alcalà, Maurizio Ribera

2017-11-20

This proof-of-concept study integrates the surface currents measured by high-frequency coastal radars with plankton time-series data collected at a fixed sampling point from the Mediterranean Sea (MareChiara Long Term Ecological Research site in the Gulf of Naples) to characterize the spatial origin of phytoplankton assemblages and to scrutinize the processes ruling their dynamics. The phytoplankton community generally originated from the coastal waters whereby species succession was mainly regulated by biological factors (life-cycle processes, species-specific physiological performances and inter-specific interactions). Physical factors, e.g. the alternation between coastal and offshore waters and the horizontal mixing, were also important drivers of phytoplankton dynamics promoting diversity maintenance by i) advecting species from offshore and ii) diluting the resident coastal community so as to dampen resource stripping by dominant species and thereby increase the numerical importance of rarer species. Our observations highlight the resilience of coastal communities, which may favour their persistence over time and the prevalence of successional events over small time and space scales. Although coastal systems may act differently from one another, our findings provide a conceptual framework to address physical-biological interactions occurring in coastal basins, which can be generalised to other areas.

Atomic force microscopy contact, tapping, and jumping modes for imaging biological samples in liquids

NASA Astrophysics Data System (ADS)

Moreno-Herrero, F.; Colchero, J.; Gómez-Herrero, J.; Baró, A. M.

2004-03-01

The capabilities of the atomic force microscope for imaging biomolecules under physiological conditions has been systematically investigated. Contact, dynamic, and jumping modes have been applied to four different biological systems: DNA, purple membrane, Alzheimer paired helical filaments, and the bacteriophage φ29. These samples have been selected to cover a wide variety of biological systems in terms of sizes and substrate contact area, which make them very appropriate for the type of comparative studies carried out in the present work. Although dynamic mode atomic force microscopy is clearly the best choice for imaging soft samples in air, in liquids there is not a leading technique. In liquids, the most appropriate imaging mode depends on the sample characteristics and preparation methods. Contact or dynamic modes are the best choices for imaging molecular assemblies arranged as crystals such as the purple membrane. In this case, the advantage of image acquisition speed predominates over the disadvantage of high lateral or normal force. For imaging individual macromolecules, which are weakly bonded to the substrate, lateral and normal forces are the relevant factors, and hence the jumping mode, an imaging mode which minimizes lateral and normal forces, is preferable to other imaging modes.

Direct observation of a single nanoparticle-ubiquitin corona formation

NASA Astrophysics Data System (ADS)

Ding, Feng; Radic, Slaven; Chen, Ran; Chen, Pengyu; Geitner, Nicholas K.; Brown, Jared M.; Ke, Pu Chun

2013-09-01

The advancement of nanomedicine and the increasing applications of nanoparticles in consumer products have led to administered biological exposure and unintentional environmental accumulation of nanoparticles, causing concerns over the biocompatibility and sustainability of nanotechnology. Upon entering physiological environments, nanoparticles readily assume the form of a nanoparticle-protein corona that dictates their biological identity. Consequently, understanding the structure and dynamics of a nanoparticle-protein corona is essential for predicting the fate, transport, and toxicity of nanomaterials in living systems and for enabling the vast applications of nanomedicine. Here we combined multiscale molecular dynamics simulations and complementary experiments to characterize the silver nanoparticle-ubiquitin corona formation. Notably, ubiquitins competed with citrates for the nanoparticle surface, governed by specific electrostatic interactions. Under a high protein/nanoparticle stoichiometry, ubiquitins formed a multi-layer corona on the particle surface. The binding exhibited an unusual stretched-exponential behavior, suggesting a rich binding kinetics. Furthermore, the binding destabilized the α-helices while increasing the β-sheet content of the proteins. This study revealed the atomic and molecular details of the structural and dynamic characteristics of nanoparticle-protein corona formation.The advancement of nanomedicine and the increasing applications of nanoparticles in consumer products have led to administered biological exposure and unintentional environmental accumulation of nanoparticles, causing concerns over the biocompatibility and sustainability of nanotechnology. Upon entering physiological environments, nanoparticles readily assume the form of a nanoparticle-protein corona that dictates their biological identity. Consequently, understanding the structure and dynamics of a nanoparticle-protein corona is essential for predicting the fate, transport, and toxicity of nanomaterials in living systems and for enabling the vast applications of nanomedicine. Here we combined multiscale molecular dynamics simulations and complementary experiments to characterize the silver nanoparticle-ubiquitin corona formation. Notably, ubiquitins competed with citrates for the nanoparticle surface, governed by specific electrostatic interactions. Under a high protein/nanoparticle stoichiometry, ubiquitins formed a multi-layer corona on the particle surface. The binding exhibited an unusual stretched-exponential behavior, suggesting a rich binding kinetics. Furthermore, the binding destabilized the α-helices while increasing the β-sheet content of the proteins. This study revealed the atomic and molecular details of the structural and dynamic characteristics of nanoparticle-protein corona formation. Electronic supplementary information (ESI) available: Experimental and computational methods as well as supporting figures. See DOI: 10.1039/c3nr02147e

Extending and expanding the Darwinian synthesis: the role of complex systems dynamics.

PubMed

Weber, Bruce H

2011-03-01

Darwinism is defined here as an evolving research tradition based upon the concepts of natural selection acting upon heritable variation articulated via background assumptions about systems dynamics. Darwin's theory of evolution was developed within a context of the background assumptions of Newtonian systems dynamics. The Modern Evolutionary Synthesis, or neo-Darwinism, successfully joined Darwinian selection and Mendelian genetics by developing population genetics informed by background assumptions of Boltzmannian systems dynamics. Currently the Darwinian Research Tradition is changing as it incorporates new information and ideas from molecular biology, paleontology, developmental biology, and systems ecology. This putative expanded and extended synthesis is most perspicuously deployed using background assumptions from complex systems dynamics. Such attempts seek to not only broaden the range of phenomena encompassed by the Darwinian Research Tradition, such as neutral molecular evolution, punctuated equilibrium, as well as developmental biology, and systems ecology more generally, but to also address issues of the emergence of evolutionary novelties as well as of life itself. Copyright © 2010 Elsevier Ltd. All rights reserved.

Tracking the Dynamic Folding and Unfolding of RNA G-Quadruplexes in Live Cells.

PubMed

Chen, Xiu-Cai; Chen, Shuo-Bin; Dai, Jing; Yuan, Jia-Hao; Ou, Tian-Miao; Huang, Zhi-Shu; Tan, Jia-Heng

2018-04-16

Because of the absence of methods for tracking RNA G-quadruplex dynamics, especially the folding and unfolding of this attractive structure in live cells, understanding of the biological roles of RNA G-quadruplexes is so far limited. Herein, we report a new red-emitting fluorescent probe, QUMA-1, for the selective, continuous, and real-time visualization of RNA G-quadruplexes in live cells. The applications of QUMA-1 in several previously intractable applications, including live-cell imaging of the dynamic folding, unfolding, and movement of RNA G-quadruplexes and the visualization of the unwinding of RNA G-quadruplexes by RNA helicase have been demonstrated. Notably, our real-time results revealed the complexity of the dynamics of RNA G-quadruplexes in live cells. We anticipate that the further application of QUMA-1 in combination with appropriate biological and imaging methods to explore the dynamics of RNA G-quadruplexes will uncover more information about the biological roles of RNA G-quadruplexes. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Direct Prediction of EPR Spectra from Lipid Bilayers: Understanding Structure and Dynamics in Biological Membranes.

PubMed

Catte, Andrea; White, Gaye F; Wilson, Mark R; Oganesyan, Vasily S

2018-06-02

Of the many biophysical techniques now being brought to bear on studies of membranes, electron paramagnetic resonance (EPR) of nitroxide spin probes was the first to provide information about both mobility and ordering in lipid membranes. Here, we report the first prediction of variable temperature EPR spectra of model lipid bilayers in the presence and absence of cholesterol from the results of large scale fully atomistic molecular dynamics (MD) simulations. Three types of structurally different spin probes were employed in order to study different parts of the bilayer. Our results demonstrate very good agreement with experiment and thus confirm the accuracy of the latest lipid force fields. The atomic resolution of the simulations allows the interpretation of the molecular motions and interactions in terms of their impact on the sensitive EPR line shapes. Direct versus indirect effects of cholesterol on the dynamics of spin probes are analysed. Given the complexity of structural organisation in lipid bilayers, the advantage of using a combined MD-EPR simulation approach is two-fold. Firstly, prediction of EPR line shapes directly from MD trajectories of actual phospholipid structures allows unambiguous interpretation of EPR spectra of biological membranes in terms of complex motions. Secondly, such an approach provides an ultimate test bed for the up-to-date MD simulation models employed in the studies of biological membranes, an area that currently attracts great attention. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Residual perception of biological motion in cortical blindness.

PubMed

Ruffieux, Nicolas; Ramon, Meike; Lao, Junpeng; Colombo, Françoise; Stacchi, Lisa; Borruat, François-Xavier; Accolla, Ettore; Annoni, Jean-Marie; Caldara, Roberto

2016-12-01

From birth, the human visual system shows a remarkable sensitivity for perceiving biological motion. This visual ability relies on a distributed network of brain regions and can be preserved even after damage of high-level ventral visual areas. However, it remains unknown whether this critical biological skill can withstand the loss of vision following bilateral striate damage. To address this question, we tested the categorization of human and animal biological motion in BC, a rare case of cortical blindness after anoxia-induced bilateral striate damage. The severity of his impairment, encompassing various aspects of vision (i.e., color, shape, face, and object recognition) and causing blind-like behavior, contrasts with a residual ability to process motion. We presented BC with static or dynamic point-light displays (PLDs) of human or animal walkers. These stimuli were presented either individually, or in pairs in two alternative forced choice (2AFC) tasks. When confronted with individual PLDs, the patient was unable to categorize the stimuli, irrespective of whether they were static or dynamic. In the 2AFC task, BC exhibited appropriate eye movements towards diagnostic information, but performed at chance level with static PLDs, in stark contrast to his ability to efficiently categorize dynamic biological agents. This striking ability to categorize biological motion provided top-down information is important for at least two reasons. Firstly, it emphasizes the importance of assessing patients' (visual) abilities across a range of task constraints, which can reveal potential residual abilities that may in turn represent a key feature for patient rehabilitation. Finally, our findings reinforce the view that the neural network processing biological motion can efficiently operate despite severely impaired low-level vision, positing our natural predisposition for processing dynamicity in biological agents as a robust feature of human vision. Copyright © 2016 Elsevier Ltd. All rights reserved.

A low cost, modular, and physiologically inspired electronic neuron

NASA Astrophysics Data System (ADS)

Sitt, J. D.; Campetella, F.; Aliaga, J.

2010-12-01

We describe a low cost design of an electronic neuron, which is designed to represent the dynamical properties of the membrane potential of biological neurons by modeling the states of the membrane channels. This electronic neuron can be used to study the nonlinear properties of the membrane voltage dynamics and to develop and analyze small neuronal circuits using electronic neurons as building blocks.

Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity

PubMed Central

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand “complex behavior” and complexity theory, and from which important biological insight can be gained. PMID:24999297

Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity.

PubMed

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand "complex behavior" and complexity theory, and from which important biological insight can be gained.

THE INFLUENCE OF CATCHMENT LAND USE ON HYDROGRAPH DYNAMICS AND IMPLICATIONS FOR STREAM BIOLOGICAL ASSEMBLAGES

EPA Science Inventory

Catchment land use impacts the rise and fall dynamic of hydrographs, and may also help explain variation in biological assemblages known to be sensitive to flow regime. We collected continuous stream depth records for the 2002 water year (5 min. intervals) from eight streams dra...

Philosophical Basis and Some Historical Aspects of Systems Biology: From Hegel to Noble - Applications for Bioenergetic Research

PubMed Central

Saks, Valdur; Monge, Claire; Guzun, Rita

2009-01-01

We live in times of paradigmatic changes for the biological sciences. Reductionism, that for the last six decades has been the philosophical basis of biochemistry and molecular biology, is being displaced by Systems Biology, which favors the study of integrated systems. Historically, Systems Biology - defined as the higher level analysis of complex biological systems - was pioneered by Claude Bernard in physiology, Norbert Wiener with the development of cybernetics, and Erwin Schrödinger in his thermodynamic approach to the living. Systems Biology applies methods inspired by cybernetics, network analysis, and non-equilibrium dynamics of open systems. These developments follow very precisely the dialectical principles of development from thesis to antithesis to synthesis discovered by Hegel. Systems Biology opens new perspectives for studies of the integrated processes of energy metabolism in different cells. These integrated systems acquire new, system-level properties due to interaction of cellular components, such as metabolic compartmentation, channeling and functional coupling mechanisms, which are central for regulation of the energy fluxes. State of the art of these studies in the new area of Molecular System Bioenergetics is analyzed. PMID:19399243

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Impaired associative learning in schizophrenia: behavioral and computational studies

PubMed Central

Diwadkar, Vaibhav A.; Flaugher, Brad; Jones, Trevor; Zalányi, László; Ujfalussy, Balázs; Keshavan, Matcheri S.

2008-01-01

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia. PMID:19003486

Systems cell biology.

PubMed

Mast, Fred D; Ratushny, Alexander V; Aitchison, John D

2014-09-15

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology. © 2014 Mast et al.

Biological Oceanography

NASA Astrophysics Data System (ADS)

Dyhrman, Sonya

2004-10-01

The ocean is arguably the largest habitat on the planet, and it houses an astounding array of life, from microbes to whales. As a testament to this diversity and its importance, the discipline of biological oceanography spans studies of all levels of biological organization, from that of single genes, to organisms, to their population dynamics. Biological oceanography also includes studies on how organisms interact with, and contribute to, essential global processes. Students of biological oceanography are often as comfortable looking at satellite images as they are electron micrographs. This diversity of perspective begins the textbook Biological Oceanography, with cover graphics including a Coastal Zone Color Scanner image representing chlorophyll concentration, an electron micrograph of a dinoflagellate, and a photograph of a copepod. These images instantly capture the reader's attention and illustrate some of the different scales on which budding oceanographers are required to think. Having taught a core graduate course in biological oceanography for many years, Charlie Miller has used his lecture notes as the genesis for this book. The text covers the subject of biological oceanography in a manner that is targeted to introductory graduate students, but it would also be appropriate for advanced undergraduates.

Förster resonance energy transfer as a tool to study photoreceptor biology

PubMed Central

Hovan, Stephanie C.; Howell, Scott; Park, Paul S.-H.

2010-01-01

Vision is initiated in photoreceptor cells of the retina by a set of biochemical events called phototransduction. These events occur via coordinated dynamic processes that include changes in secondary messenger concentrations, conformational changes and post-translational modifications of signaling proteins, and protein-protein interactions between signaling partners. A complete description of the orchestration of these dynamic processes is still unavailable. Described in this work is the first step in the development of tools combining fluorescent protein technology, Förster resonance energy transfer (FRET), and transgenic animals that have the potential to reveal important molecular insights about the dynamic processes occurring in photoreceptor cells. We characterize the fluorescent proteins SCFP3A and SYFP2 for use as a donor-acceptor pair in FRET assays, which will facilitate the visualization of dynamic processes in living cells. We also demonstrate the targeted expression of these fluorescent proteins to the rod photoreceptor cells of Xenopus laevis, and describe a general method for detecting FRET in these cells. The general approaches described here can address numerous types of questions related to phototransduction and photoreceptor biology by providing a platform to visualize dynamic processes in molecular detail within a native context. PMID:21198205

The Role of Protein Loops and Linkers in Conformational Dynamics and Allostery.

PubMed

Papaleo, Elena; Saladino, Giorgio; Lambrughi, Matteo; Lindorff-Larsen, Kresten; Gervasio, Francesco Luigi; Nussinov, Ruth

2016-06-08

Proteins are dynamic entities that undergo a plethora of conformational changes that may take place on a wide range of time scales. These changes can be as small as the rotation of one or a few side-chain dihedral angles or involve concerted motions in larger portions of the three-dimensional structure; both kinds of motions can be important for biological function and allostery. It is becoming increasingly evident that "connector regions" are important components of the dynamic personality of protein structures. These regions may be either disordered loops, i.e., poorly structured regions connecting secondary structural elements, or linkers that connect entire protein domains. Experimental and computational studies have, however, revealed that these regions are not mere connectors, and their role in allostery and conformational changes has been emerging in the last few decades. Here we provide a detailed overview of the structural properties and classification of loops and linkers, as well as a discussion of the main computational methods employed to investigate their function and dynamical properties. We also describe their importance for protein dynamics and allostery using as examples key proteins in cellular biology and human diseases such as kinases, ubiquitinating enzymes, and transcription factors.

Structure theorems and the dynamics of nitrogen catabolite repression in yeast

PubMed Central

Boczko, Erik M.; Cooper, Terrance G.; Gedeon, Tomas; Mischaikow, Konstantin; Murdock, Deborah G.; Pratap, Siddharth; Wells, K. Sam

2005-01-01

By using current biological understanding, a conceptually simple, but mathematically complex, model is proposed for the dynamics of the gene circuit responsible for regulating nitrogen catabolite repression (NCR) in yeast. A variety of mathematical “structure” theorems are described that allow one to determine the asymptotic dynamics of complicated systems under very weak hypotheses. It is shown that these theorems apply to several subcircuits of the full NCR circuit, most importantly to the URE2–GLN3 subcircuit that is independent of the other constituents but governs the switching behavior of the full NCR circuit under changes in nitrogen source. Under hypotheses that are fully consistent with biological data, it is proven that the dynamics of this subcircuit is simple periodic behavior in synchrony with the cell cycle. Although the current mathematical structure theorems do not apply to the full NCR circuit, extensive simulations suggest that the dynamics is constrained in much the same way as that of the URE2–GLN3 subcircuit. This finding leads to the proposal that mathematicians study genetic circuits to find new geometries for which structure theorems may exist. PMID:15814615

Parasite transmission among relatives halts Red Queen dynamics.

PubMed

Greenspoon, Philip B; Mideo, Nicole

2017-03-01

The theory that coevolving hosts and parasites create a fluctuating selective environment for one another (i.e., produce Red Queen dynamics) has deep roots in evolutionary biology; yet empirical evidence for Red Queen dynamics remains scarce. Fluctuating coevolutionary dynamics underpin the Red Queen hypothesis for the evolution of sex, as well as hypotheses explaining the persistence of genetic variation under sexual selection, local parasite adaptation, the evolution of mutation rate, and the evolution of nonrandom mating. Coevolutionary models that exhibit Red Queen dynamics typically assume that hosts and parasites encounter one another randomly. However, if related individuals aggregate into family groups or are clustered spatially, related hosts will be more likely to encounter parasites transmitted by genetically similar individuals. Using a model that incorporates familial parasite transmission, we show that a slight degree of familial parasite transmission is sufficient to halt coevolutionary fluctuations. Our results predict that evidence for Red Queen dynamics, and its evolutionary consequences, are most likely to be found in biological systems in which hosts and parasites mix mainly at random, and are less likely to be found in systems with familial aggregation. This presents a challenge to the Red Queen hypothesis and other hypotheses that depend on coevolutionary cycling. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm.

PubMed

Nakajima, Kohei; Hauser, Helmut; Kang, Rongjie; Guglielmino, Emanuele; Caldwell, Darwin G; Pfeifer, Rolf

2013-01-01

The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of "soft robotics". Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed.

A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

PubMed Central

Nakajima, Kohei; Hauser, Helmut; Kang, Rongjie; Guglielmino, Emanuele; Caldwell, Darwin G.; Pfeifer, Rolf

2013-01-01

The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of “soft robotics”. Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed. PMID:23847526

Informing biological design by integration of systems and synthetic biology.

PubMed

Smolke, Christina D; Silver, Pamela A

2011-03-18

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. Copyright © 2011 Elsevier Inc. All rights reserved.

Dynamics of problem setting and framing in citizen discussions on synthetic biology.

PubMed

Betten, Afke Wieke; Broerse, Jacqueline E W; Kupper, Frank

2018-04-01

Synthetic biology is an emerging scientific field where engineers and biologists design and build biological systems for various applications. Developing synthetic biology responsibly in the public interest necessitates a meaningful societal dialogue. In this article, we argue that facilitating such a dialogue requires an understanding of how people make sense of synthetic biology. We performed qualitative research to unravel the underlying dynamics of problem setting and framing in citizen discussions on synthetic biology. We found that most people are not inherently for or against synthetic biology as a technology or development in itself, but that their perspectives are framed by core values about our relationships with science and technology and that sensemaking is much dependent on the context and general feelings of (dis)content. Given that there are many assumptions focused on a more binary idea of the public's view, we emphasize the need for frame awareness and understanding in a meaningful dialogue.

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

PubMed

Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A

2012-07-02

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

Complementary uses of small angle X-ray scattering and X-ray crystallography.

PubMed

Pillon, Monica C; Guarné, Alba

2017-11-01

Most proteins function within networks and, therefore, protein interactions are central to protein function. Although stable macromolecular machines have been extensively studied, dynamic protein interactions remain poorly understood. Small-angle X-ray scattering probes the size, shape and dynamics of proteins in solution at low resolution and can be used to study samples in a large range of molecular weights. Therefore, it has emerged as a powerful technique to study the structure and dynamics of biomolecular systems and bridge fragmented information obtained using high-resolution techniques. Here we review how small-angle X-ray scattering can be combined with other structural biology techniques to study protein dynamics. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

PubMed Central

2011-01-01

Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics. PMID:21774817

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

PubMed Central

Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G.

2016-01-01

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512

Dynamics of active sites in biological macromolecules using a Green-function approach: An application to heme vibrational dynamics in myoglobin

NASA Astrophysics Data System (ADS)

Rai, Brajesh; Prohofsky, Earl

2003-03-01

Dynamics of functionally active regions of biological macromolecules can be studied using a Green-function technique. This approach uses the fact that in most cases one has a good set of force constants for active sites, and rather poorly defined force field parameters for other regions of the macromolecule. The Green-function method is applied to study the iron vibrational modes of the heme active site in myoglobin. In this approach, the heme active site is viewed as a system interacting with surrounding globin, which acts as an excitation bath. The normal modes of heme and globin are separately calculated using the best available force fields for the two entities. The iron vibrational spectrum of myoglobin is then obtained using the solutions of the heme and globin, and by considering physically meaningful interactions between the two units. The refinement of the Green-function calculations to the experimental data from an x-ray synchrotron-based Nuclear Resonance Vibrational Spectroscopy provides important insights into the character of iron normal modes of myoglobin.

Sialyldisaccharide conformations: a molecular dynamics perspective

NASA Astrophysics Data System (ADS)

Selvin, Jeyasigamani F. A.; Priyadarzini, Thanu R. K.; Veluraja, Kasinadar

2012-04-01

Sialyldisaccharides are significant terminal components of glycoconjugates and their negative charge and conformation are extensively utilized in molecular recognition processes. The conformation and flexibility of four biologically important sialyldisaccharides [Neu5Acα(2-3)Gal, Neu5Acα(2-6)Gal, Neu5Acα(2-8)Neu5Ac and Neu5Acα(2-9)Neu5Ac] are studied using Molecular Dynamics simulations of 20 ns duration to deduce the conformational preferences of the sialyldisaccharides and the interactions which stabilize the conformations. This study clearly describes the possible conformational models of sialyldisaccharides deduced from 20 ns Molecular Dynamics simulations and our results confirm the role of water in the structural stabilization of sialyldisaccharides. An extensive analysis on the sialyldisaccharide structures available in PDB also confirms the conformational regions found by experiments are detected in MD simulations of 20 ns duration. The three dimensional structural coordinates for all the MD derived sialyldisaccharide conformations are deposited in the 3DSDSCAR database and these conformational models will be useful for glycobiologists and biotechnologists to understand the biological functions of sialic acid containing glycoconjugates.

Population Dynamics of Genetic Regulatory Networks

NASA Astrophysics Data System (ADS)

Braun, Erez

2005-03-01

Unlike common objects in physics, a biological cell processes information. The cell interprets its genome and transforms the genomic information content, through the action of genetic regulatory networks, into proteins which in turn dictate its metabolism, functionality and morphology. Understanding the dynamics of a population of biological cells presents a unique challenge. It requires to link the intracellular dynamics of gene regulation, through the mechanism of cell division, to the level of the population. We present experiments studying adaptive dynamics of populations of genetically homogeneous microorganisms (yeast), grown for long durations under steady conditions. We focus on population dynamics that do not involve random genetic mutations. Our experiments follow the long-term dynamics of the population distributions and allow to quantify the correlations among generations. We focus on three interconnected issues: adaptation of genetically homogeneous populations following environmental changes, selection processes on the population and population variability and expression distributions. We show that while the population exhibits specific short-term responses to environmental inputs, it eventually adapts to a robust steady-state, largely independent of external conditions. Cycles of medium-switch show that the adapted state is imprinted in the population and that this memory is maintained for many generations. To further study population adaptation, we utilize the process of gene recruitment whereby a gene naturally regulated by a specific promoter is placed under a different regulatory system. This naturally occurring process has been recognized as a major driving force in evolution. We have recruited an essential gene to a foreign regulatory network and followed the population long-term dynamics. Rewiring of the regulatory network allows us to expose their complex dynamics and phase space structure.

Optical Antenna-Based Fluorescence Correlation Spectroscopy to Probe the Nanoscale Dynamics of Biological Membranes.

PubMed

Winkler, Pamina M; Regmi, Raju; Flauraud, Valentin; Brugger, Jürgen; Rigneault, Hervé; Wenger, Jérôme; García-Parajo, María F

2018-01-04

The plasma membrane of living cells is compartmentalized at multiple spatial scales ranging from the nano- to the mesoscale. This nonrandom organization is crucial for a large number of cellular functions. At the nanoscale, cell membranes organize into dynamic nanoassemblies enriched by cholesterol, sphingolipids, and certain types of proteins. Investigating these nanoassemblies known as lipid rafts is of paramount interest in fundamental cell biology. However, this goal requires simultaneous nanometer spatial precision and microsecond temporal resolution, which is beyond the reach of common microscopes. Optical antennas based on metallic nanostructures efficiently enhance and confine light into nanometer dimensions, breaching the diffraction limit of light. In this Perspective, we discuss recent progress combining optical antennas with fluorescence correlation spectroscopy (FCS) to monitor microsecond dynamics at nanoscale spatial dimensions. These new developments offer numerous opportunities to investigate lipid and protein dynamics in both mimetic and native biological membranes.

Quasielastic neutron scattering in biology: Theory and applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vural, Derya; Univ. of Tennessee, Knoxville, TN; Hu, Xiaohu

Neutrons scatter quasielastically from stochastic, diffusive processes, such as overdamped vibrations, localized diffusion and transitions between energy minima. In biological systems, such as proteins and membranes, these relaxation processes are of considerable physical interest. We review here recent methodological advances and applications of quasielastic neutron scattering (QENS) in biology, concentrating on the role of molecular dynamics simulation in generating data with which neutron profiles can be unambiguously interpreted. We examine the use of massively-parallel computers in calculating scattering functions, and the application of Markov state modeling. The decomposition of MD-derived neutron dynamic susceptibilities is described, and the use of thismore » in combination with NMR spectroscopy. We discuss dynamics at very long times, including approximations to the infinite time mean-square displacement and nonequilibrium aspects of single-protein dynamics. Lastly, we examine how neutron scattering and MD can be combined to provide information on lipid nanodomains.« less

Quasielastic neutron scattering in biology: Theory and applications

DOE PAGES

Vural, Derya; Univ. of Tennessee, Knoxville, TN; Hu, Xiaohu; ...

2016-06-15

Neutrons scatter quasielastically from stochastic, diffusive processes, such as overdamped vibrations, localized diffusion and transitions between energy minima. In biological systems, such as proteins and membranes, these relaxation processes are of considerable physical interest. We review here recent methodological advances and applications of quasielastic neutron scattering (QENS) in biology, concentrating on the role of molecular dynamics simulation in generating data with which neutron profiles can be unambiguously interpreted. We examine the use of massively-parallel computers in calculating scattering functions, and the application of Markov state modeling. The decomposition of MD-derived neutron dynamic susceptibilities is described, and the use of thismore » in combination with NMR spectroscopy. We discuss dynamics at very long times, including approximations to the infinite time mean-square displacement and nonequilibrium aspects of single-protein dynamics. Lastly, we examine how neutron scattering and MD can be combined to provide information on lipid nanodomains.« less

E-Index for Differentiating Complex Dynamic Traits

PubMed Central

Qi, Jiandong; Sun, Jianfeng; Wang, Jianxin

2016-01-01

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns. PMID:27064292

Unusual dynamics of extinction in a simple ecological model.

PubMed Central

Sinha, S; Parthasarathy, S

1996-01-01

Studies on natural populations and harvesting biological resources have led to the view, commonly held, that (i) populations exhibiting chaotic oscillations run a high risk of extinction; and (ii) a decrease in emigration/exploitation may reduce the risk of extinction. Here we describe a simple ecological model with emigration/depletion that shows behavior in contrast to this. This model displays unusual dynamics of extinction and survival, where populations growing beyond a critical rate can persist within a band of high depletion rates, whereas extinction occurs for lower depletion rates. Though prior to extinction at lower depletion rates the population exhibits chaotic dynamics with large amplitudes of variation and very low minima, at higher depletion rates the population persists at chaos but with reduced variation and increased minima. For still higher values, within the band of persistence, the dynamics show period reversal leading to stability. These results illustrate that chaos does not necessarily lead to population extinction. In addition, the persistence of populations at high depletion rates has important implications in the considerations of strategies for the management of biological resources. PMID:8643661

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms.

PubMed

Mori, Takaharu; Miyashita, Naoyuki; Im, Wonpil; Feig, Michael; Sugita, Yuji

2016-07-01

This paper reviews various enhanced conformational sampling methods and explicit/implicit solvent/membrane models, as well as their recent applications to the exploration of the structure and dynamics of membranes and membrane proteins. Molecular dynamics simulations have become an essential tool to investigate biological problems, and their success relies on proper molecular models together with efficient conformational sampling methods. The implicit representation of solvent/membrane environments is reasonable approximation to the explicit all-atom models, considering the balance between computational cost and simulation accuracy. Implicit models can be easily combined with replica-exchange molecular dynamics methods to explore a wider conformational space of a protein. Other molecular models and enhanced conformational sampling methods are also briefly discussed. As application examples, we introduce recent simulation studies of glycophorin A, phospholamban, amyloid precursor protein, and mixed lipid bilayers and discuss the accuracy and efficiency of each simulation model and method. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Infections on the move: how transient phases of host movement influence disease spread

PubMed Central

Fenton, A.; Dell, A. I.

2017-01-01

Animal movement impacts the spread of human and wildlife diseases, and there is significant interest in understanding the role of migrations, biological invasions and other wildlife movements in spatial infection dynamics. However, the influence of processes acting on infections during transient phases of host movement is poorly understood. We propose a conceptual framework that explicitly considers infection dynamics during transient phases of host movement to better predict infection spread through spatial host networks. Accounting for host transient movement captures key processes that occur while hosts move between locations, which together determine the rate at which hosts spread infections through networks. We review theoretical and empirical studies of host movement and infection spread, highlighting the multiple factors that impact the infection status of hosts. We then outline characteristics of hosts, parasites and the environment that influence these dynamics. Recent technological advances provide disease ecologists unprecedented ability to track the fine-scale movement of organisms. These, in conjunction with experimental testing of the factors driving infection dynamics during host movement, can inform models of infection spread based on constituent biological processes. PMID:29263283

Hot Spots in a Network of Functional Sites

PubMed Central

Ozbek, Pemra; Soner, Seren; Haliloglu, Turkan

2013-01-01

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation. PMID:24023934

Supramolecular delivery of photoactivatable fluorophores in developing embryos

NASA Astrophysics Data System (ADS)

Zhang, Yang; Tang, Sicheng; Sansalone, Lorenzo; Thapaliya, Ek Raj; Baker, James D.; Raymo, Françisco M.

2017-02-01

The identification of noninvasive strategies to monitor dynamics within living organisms in real time is essential to elucidate the fundamental factors governing a diversity of biological processes. This study demonstrates that the supramolecular delivery of photoactivatable fluorophores in Drosophila melanogaster embryos allows the real-time tracking of translocating molecules. The designed photoactivatable fluorophores switch from an emissive reactant to an emissive product with spectrally-resolved fluorescence, under moderate blue-light irradiation conditions. These hydrophobic fluorescent probes can be encapsulated within supramolecular hosts and delivered to the cellular blastoderm of the embryos. Thus, the combination of supramolecular delivery and fluorescence photoactivation translates into a noninvasive method to monitor dynamics in vivo and can evolve into a general chemical tool to track motion in biological specimens.

Robust and irreversible development in cell society as a general consequence of intra-inter dynamics

NASA Astrophysics Data System (ADS)

Kaneko, Kunihiko; Furusawa, Chikara

2000-05-01

A dynamical systems scenario for developmental cell biology is proposed, based on numerical studies of a system with interacting units with internal dynamics and reproduction. Diversification, formation of discrete and recursive types, and rules for differentiation are found as a natural consequence of such a system. “Stem cells” that either proliferate or differentiate to different types stochastically are found to appear when intra-cellular dynamics are chaotic. Robustness of the developmental process against microscopic and macroscopic perturbations is shown to be a natural consequence of such intra-inter dynamics, while irreversibility in developmental process is discussed in terms of the gain of stability, loss of diversity and chaotic instability.

Spectrum of Slow and Super-Slow (Picosecond to Nanosecond) Water Dynamics around Organic and Biological Solutes.

PubMed

Ramakrishnan, Gopakumar; González-Jiménez, Mario; Lapthorn, Adrian J; Wynne, Klaas

2017-07-06

Water dynamics in the solvation shell of solutes plays a very important role in the interaction of biomolecules and in chemical reaction dynamics. However, a selective spectroscopic study of the solvation shell is difficult because of the interference of the solute dynamics. Here we report on the observation of heavily slowed down water dynamics in the solvation shell of different solutes by measuring the low-frequency spectrum of solvation water, free from the contribution of the solute. A slowdown factor of ∼50 is observed even for relatively low concentrations of the solute. We go on to show that the effect can be generalized to different solutes including proteins.

Joining Forces: Integrating Proteomics and Cross-linking with the Mass Spectrometry of Intact Complexes*

PubMed Central

Stengel, Florian; Aebersold, Ruedi; Robinson, Carol V.

2012-01-01

Protein assemblies are critical for cellular function and understanding their physical organization is the key aim of structural biology. However, applying conventional structural biology approaches is challenging for transient, dynamic, or polydisperse assemblies. There is therefore a growing demand for hybrid technologies that are able to complement classical structural biology methods and thereby broaden our arsenal for the study of these important complexes. Exciting new developments in the field of mass spectrometry and proteomics have added a new dimension to the study of protein-protein interactions and protein complex architecture. In this review, we focus on how complementary mass spectrometry-based techniques can greatly facilitate structural understanding of protein assemblies. PMID:22180098

Systems biology, adverse outcome pathways, and ecotoxicology in the 21st century

EPA Science Inventory

While many definitions of systems biology exist, the majority of these contain most (if not all) of the following elements: global measurements of biological molecules to the extent technically feasible, dynamic measurements of key biological molecules to establish quantitative r...

A spatial-dynamic value transfer model of economic losses from a biological invasion

Treesearch

Thomas P. Holmes; Andrew M. Liebhold; Kent F. Kovacs; Betsy Von Holle

2010-01-01

Rigorous assessments of the economic impacts of introduced species at broad spatial scales are required to provide credible information to policy makers. We propose that economic models of aggregate damages induced by biological invasions need to link microeconomic analyses of site-specific economic damages with spatial-dynamic models of value change associated with...

Assessing Students' Ability to Trace Matter in Dynamic Systems in Cell Biology

ERIC Educational Resources Information Center

Wilson, Christopher D.; Anderson, Charles W.; Heidemann, Merle; Merrill, John E.; Merritt, Brett W.; Richmond, Gail; Sibley, Duncan F.; Parker, Joyce M.

2006-01-01

College-level biology courses contain many complex processes that are often taught and learned as detailed narratives. These processes can be better understood by perceiving them as dynamic systems that are governed by common fundamental principles. Conservation of matter is such a principle, and thus tracing matter is an essential step in…

No question about exciting questions in cell biology.

PubMed

Pollard, Thomas D

2013-12-01

Although we have a good grasp of many important processes in cell biology, including knowledge of many molecules involved and how they interact with each other, we still do not understand most of the dynamical features that are the essence of living systems. Fortunately, we now have the ability to dissect biological systems in enough detail to understand their dynamics, including the use of mathematical models to account for past observations and predict future experiments. This deep level of mechanistic understanding should be our goal—not simply to satisfy our scientific curiosity, but also to understand the causes of disease well enough to predict risks, make early diagnoses, and treat effectively. Many big questions remain to be answered before we reach this goal of understanding cellular dynamics.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

C. elegans network biology: a beginning.

PubMed Central

Piano, Fabio; Gunsalus, Kristin C; Hill, David E; Vidal, Marc

2006-01-01

The architecture and dynamics of molecular networks can provide an understanding of complex biological processes complementary to that obtained from the in-depth study of single genes and proteins. With a completely sequenced and well-annotated genome, a fully characterized cell lineage, and powerful tools available to dissect development, Caenorhabditis elegans, among metazoans, provides an optimal system to bridge cellular and organismal biology with the global properties of macromolecular networks. This chapter considers omic technologies available for C. elegans to describe molecular networks--encompassing transcriptional and phenotypic profiling as well as physical interaction mapping--and discusses how their individual and integrated applications are paving the way for a network-level understanding of C. elegans biology. PMID:18050437

Micro/nanofabricated environments for synthetic biology.

PubMed

Collier, C Patrick; Simpson, Michael L

2011-08-01

A better understanding of how confinement, crowding and reduced dimensionality modulate reactivity and reaction dynamics will aid in the rational and systematic discovery of functionality in complex biological systems. Artificial microfabricated and nanofabricated structures have helped elucidate the effects of nanoscale spatial confinement and segregation on biological behavior, particularly when integrated with microfluidics, through precise control in both space and time of diffusible signals and binding interactions. Examples of nanostructured interfaces for synthetic biology include the development of cell-like compartments for encapsulating biochemical reactions, nanostructured environments for fundamental studies of diffusion, molecular transport and biochemical reaction kinetics, and regulation of biomolecular interactions as functions of microfabricated and nanofabricated topological constraints. Copyright © 2011 Elsevier Ltd. All rights reserved.

Molecular binding of black tea theaflavins to biological membranes: relationship to bioactivities

USDA-ARS?s Scientific Manuscript database

Molecular dynamics simulations were used to study the interactions of three theaflavin compounds with lipid bilayers. Experimental studies have linked theaflavins to beneficial health effects, some of which are related to interactions with the cell membrane. The molecular interaction of theaflavin...

Coming Out in Class: Challenges and Benefits of Active Learning in a Biology Classroom for LGBTQIA Students

PubMed Central

Cooper, Katelyn M.; Brownell, Sara E.

2016-01-01

As we transition our undergraduate biology classrooms from traditional lectures to active learning, the dynamics among students become more important. These dynamics can be influenced by student social identities. One social identity that has been unexamined in the context of undergraduate biology is the spectrum of lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) identities. In this exploratory interview study, we probed the experiences and perceptions of seven students who identify as part of the LGBTQIA community. We found that students do not always experience the undergraduate biology classroom to be a welcoming or accepting place for their identities. In contrast to traditional lectures, active-learning classes increase the relevance of their LGBTQIA identities due to the increased interactions among students during group work. Finally, working with other students in active-learning classrooms can present challenges and opportunities for students considering their LGBTQIA identity. These findings indicate that these students’ LGBTQIA identities are affecting their experience in the classroom and that there may be specific instructional practices that can mitigate some of the possible obstacles. We hope that this work can stimulate discussions about how to broadly make our active-learning biology classes more inclusive of this specific population of students. PMID:27543636

Underlying Principles of Natural Selection in Network Evolution: Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Wu, Wei-Sheng

2007-01-01

Systems biology is a rapidly expanding field that integrates diverse areas of science such as physics, engineering, computer science, mathematics, and biology toward the goal of elucidating the underlying principles of hierarchical metabolic and regulatory systems in the cell, and ultimately leading to predictive understanding of cellular response to perturbations. Because post-genomics research is taking place throughout the tree of life, comparative approaches offer a way for combining data from many organisms to shed light on the evolution and function of biological networks from the gene to the organismal level. Therefore, systems biology can build on decades of theoretical work in evolutionary biology, and at the same time evolutionary biology can use the systems biology approach to go in new uncharted directions. In this study, we present a review of how the post-genomics era is adopting comparative approaches and dynamic system methods to understand the underlying design principles of network evolution and to shape the nascent field of evolutionary systems biology. Finally, the application of evolutionary systems biology to robust biological network designs is also discussed from the synthetic biology perspective. PMID:19468310

Biological evaluation, docking and molecular dynamic simulation of some novel diaryl urea derivatives bearing quinoxalindione moiety

PubMed Central

Sadeghian-Rizi, Sedighe; Khodarahmi, Ghadamali Ali; Sakhteman, Amirhossein; Jahanian-Najafabadi, Ali; Rostami, Mahboubeh; Mirzaei, Mahmoud; Hassanzadeh, Farshid

2017-01-01

In this study a series of diarylurea derivatives containing quinoxalindione group were biologically evaluated for their cytotoxic activities using MTT assay against MCF-7 and HepG2 cell lines. Antibacterial activities of these compounds were also evaluated by Microplate Alamar Blue Assay (MABA) against three Gram-negative (Escherichia coli, Pseudomonas aeruginosa and Salmonella typhi), three Gram-positive (Staphylococcus aureus, Bacillus subtilis and Listeria monocitogenes) and one yeast-like fungus (Candida albicans) strain. Furthermore, molecular docking was carried out to study the binding pattern of the compounds to the active site of B-RAF kinase (PDB code: 1UWH). Molecular dynamics simulation was performed on the best ligand (16e) to investigate the ligand binding dynamics in the physiological environment. Cytotoxic evaluation revealed the most prominent cytotoxicity for 6 compounds with IC50 values of 10-18 μM against two mentioned cell lines. None of the synthesized compounds showed significant antimicrobial activity. The obtained results of the molecular docking study showed that all compounds fitted in the binding site of enzyme with binding energy range of -11.22 to -12.69 kcal/mol vs sorafenib binding energy -11.74 kcal/mol as the lead compound. Molecular dynamic simulation indicated that the binding of ligand (16e) was stable in the active site of B-RAF during the simulation. PMID:29204178

Coupling between the Dynamics of Water and Surfactants in Lyotropic Liquid Crystals.

PubMed

McDaniel, Jesse G; Yethiraj, Arun

2017-05-18

Bilayers composed of lipid or surfactant molecules are central to biological membranes and lamellar lyotropic liquid crystalline (LLC) phases. Common to these systems are phases that exhibit either ordered or disordered packing of the hydrophobic tails. In this work, we study the impact of surfactant ordering, i.e., disordered L α and ordered L β LLC phases, on the dynamics of water and sodium ions in the lamellar phases of dicarboxylate gemini surfactants. We study the different phases at identical hydration levels by changing the length of the hydrophobic tails; surfactants with shorter tails form L α phases and those with longer tails form L β phases. We find that the L α phases exhibit lower density and greater compressibility than the L β phases, with a hydration-dependent headgroup surface area. These structural differences significantly affect the relative dynamic properties of the phases, primarily the mobility of the surfactant molecules tangential to the bilayer surface, as well as the rates of water and ion diffusion. We find ∼20-50% faster water diffusion in the L α phases compared to the L β phases, with the differences most pronounced at low hydration. This coupling between water dynamics and surfactant mobility is verified using additional simulations in which the surfactant tails are frozen. Our study indicates that gemini surfactant LLCs provide an important prototypical system for characterizing properties shared with more complex biological lipid membranes.

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

Ligand diffusion in proteins via enhanced sampling in molecular dynamics.

PubMed

Rydzewski, J; Nowak, W

2017-12-01

Computational simulations in biophysics describe the dynamics and functions of biological macromolecules at the atomic level. Among motions particularly important for life are the transport processes in heterogeneous media. The process of ligand diffusion inside proteins is an example of a complex rare event that can be modeled using molecular dynamics simulations. The study of physical interactions between a ligand and its biological target is of paramount importance for the design of novel drugs and enzymes. Unfortunately, the process of ligand diffusion is difficult to study experimentally. The need for identifying the ligand egress pathways and understanding how ligands migrate through protein tunnels has spurred the development of several methodological approaches to this problem. The complex topology of protein channels and the transient nature of the ligand passage pose difficulties in the modeling of the ligand entry/escape pathways by canonical molecular dynamics simulations. In this review, we report a methodology involving a reconstruction of the ligand diffusion reaction coordinates and the free-energy profiles along these reaction coordinates using enhanced sampling of conformational space. We illustrate the above methods on several ligand-protein systems, including cytochromes and G-protein-coupled receptors. The methods are general and may be adopted to other transport processes in living matter. Copyright © 2017 Elsevier B.V. All rights reserved.

How far in-silico computing meets real experiments. A study on the structure and dynamics of spin labeled vinculin tail protein by molecular dynamics simulations and EPR spectroscopy

PubMed Central

2013-01-01

Background Investigation of conformational changes in a protein is a prerequisite to understand its biological function. To explore these conformational changes in proteins we developed a strategy with the combination of molecular dynamics (MD) simulations and electron paramagnetic resonance (EPR) spectroscopy. The major goal of this work is to investigate how far computer simulations can meet the experiments. Methods Vinculin tail protein is chosen as a model system as conformational changes within the vinculin protein are believed to be important for its biological function at the sites of cell adhesion. MD simulations were performed on vinculin tail protein both in water and in vacuo environments. EPR experimental data is compared with those of the simulated data for corresponding spin label positions. Results The calculated EPR spectra from MD simulations trajectories of selected spin labelled positions are comparable to experimental EPR spectra. The results show that the information contained in the spin label mobility provides a powerful means of mapping protein folds and their conformational changes. Conclusions The results suggest the localization of dynamic and flexible regions of the vinculin tail protein. This study shows MD simulations can be used as a complementary tool to interpret experimental EPR data. PMID:23445506

Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation

PubMed Central

Moffitt, Richard A.; Merrill, Alfred H.; Wang, May D.

2011-01-01

Abstract Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb. PMID:21314456

Molecular Dynamics Study of the Opening Mechanism for DNA Polymerase I

PubMed Central

Miller, Bill R.; Parish, Carol A.; Wu, Eugene Y.

2014-01-01

During DNA replication, DNA polymerases follow an induced fit mechanism in order to rapidly distinguish between correct and incorrect dNTP substrates. The dynamics of this process are crucial to the overall effectiveness of catalysis. Although X-ray crystal structures of DNA polymerase I with substrate dNTPs have revealed key structural states along the catalytic pathway, solution fluorescence studies indicate that those key states are populated in the absence of substrate. Herein, we report the first atomistic simulations showing the conformational changes between the closed, open, and ajar conformations of DNA polymerase I in the binary (enzyme∶DNA) state to better understand its dynamics. We have applied long time-scale, unbiased molecular dynamics to investigate the opening process of the fingers domain in the absence of substrate for B. stearothermophilis DNA polymerase in silico. These simulations are biologically and/or physiologically relevant as they shed light on the transitions between states in this important enzyme. All closed and ajar simulations successfully transitioned into the fully open conformation, which is known to be the dominant binary enzyme-DNA conformation from solution and crystallographic studies. Furthermore, we have detailed the key stages in the opening process starting from the open and ajar crystal structures, including the observation of a previously unknown key intermediate structure. Four backbone dihedrals were identified as important during the opening process, and their movements provide insight into the recognition of dNTP substrate molecules by the polymerase binary state. In addition to revealing the opening mechanism, this study also demonstrates our ability to study biological events of DNA polymerase using current computational methods without biasing the dynamics. PMID:25474643

Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions

NASA Astrophysics Data System (ADS)

Karlsson, Fredrik; Hörnquist, Michael

2007-10-01

We explore the connection between order/chaos in Boolean networks and the naturally occurring fraction of canalizing functions in such systems. This fraction turns out to give a very clear indication of whether the system possesses ordered or chaotic dynamics, as measured by Derrida plots, and also the degree of order when we compare different networks with the same number of vertices and edges. By studying also a wide distribution of indegrees in a network, we show that the mean probability of canalizing functions is a more reliable indicator of the type of dynamics for a finite network than the classical result on stability relating the bias to the mean indegree. Finally, we compare by direct simulations two biologically derived networks with networks of similar sizes but with power-law and Poisson distributions of indegrees, respectively. The biologically motivated networks are not more ordered than the latter, and in one case the biological network is even chaotic while the others are not.

Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.

PubMed

Ocone, Andrea; Millar, Andrew J; Sanguinetti, Guido

2013-04-01

Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem. Here, we develop a general statistical inference framework for stochastic transcription-translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions. http://homepages.inf.ed.ac.uk/gsanguin/software.html. Supplementary data are available at Bioinformatics online.

Self-assembly of polyelectrolyte surfactant complexes using large scale MD simulation

NASA Astrophysics Data System (ADS)

Goswami, Monojoy; Sumpter, Bobby

2014-03-01

Polyelectrolytes (PE) and surfactants are known to form interesting structures with varied properties in aqueous solutions. The morphological details of the PE-surfactant complexes depend on a combination of polymer backbone, electrostatic interactions and hydrophobic interactions. We study the self-assembly of cationic PE and anionic surfactants complexes in dilute condition. The importance of such complexes of PE with oppositely charged surfactants can be found in biological systems, such as immobilization of enzymes in polyelectrolyte complexes or nonspecific association of DNA with protein. Many useful properties of PE surfactant complexes come from the highly ordered structures of surfactant self-assembly inside the PE aggregate which has applications in industry. We do large scale molecular dynamics simulation using LAMMPS to understand the structure and dynamics of PE-surfactant systems. Our investigation shows highly ordered pearl-necklace structures that have been observed experimentally in biological systems. We investigate many different properties of PE-surfactant complexation for different parameter ranges that are useful for pharmaceutical, engineering and biological applications.

Model reduction of multiscale chemical langevin equations: a numerical case study.

PubMed

Sotiropoulos, Vassilios; Contou-Carrere, Marie-Nathalie; Daoutidis, Prodromos; Kaznessis, Yiannis N

2009-01-01

Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

Mathematical modelling in developmental biology.

PubMed

Vasieva, Olga; Rasolonjanahary, Manan'Iarivo; Vasiev, Bakhtier

2013-06-01

In recent decades, molecular and cellular biology has benefited from numerous fascinating developments in experimental technique, generating an overwhelming amount of data on various biological objects and processes. This, in turn, has led biologists to look for appropriate tools to facilitate systematic analysis of data. Thus, the need for mathematical techniques, which can be used to aid the classification and understanding of this ever-growing body of experimental data, is more profound now than ever before. Mathematical modelling is becoming increasingly integrated into biological studies in general and into developmental biology particularly. This review outlines some achievements of mathematics as applied to developmental biology and demonstrates the mathematical formulation of basic principles driving morphogenesis. We begin by describing a mathematical formalism used to analyse the formation and scaling of morphogen gradients. Then we address a problem of interplay between the dynamics of morphogen gradients and movement of cells, referring to mathematical models of gastrulation in the chick embryo. In the last section, we give an overview of various mathematical models used in the study of the developmental cycle of Dictyostelium discoideum, which is probably the best example of successful mathematical modelling in developmental biology.

Dynamic Clamp in Cardiac and Neuronal Systems Using RTXI

PubMed Central

Ortega, Francis A.; Butera, Robert J.; Christini, David J.; White, John A.; Dorval, Alan D.

2016-01-01

The injection of computer-simulated conductances through the dynamic clamp technique has allowed researchers to probe the intercellular and intracellular dynamics of cardiac and neuronal systems with great precision. By coupling computational models to biological systems, dynamic clamp has become a proven tool in electrophysiology with many applications, such as generating hybrid networks in neurons or simulating channelopathies in cardiomyocytes. While its applications are broad, the approach is straightforward: synthesizing traditional patch clamp, computational modeling, and closed-loop feedback control to simulate a cellular conductance. Here, we present two example applications: artificial blocking of the inward rectifier potassium current in a cardiomyocyte and coupling of a biological neuron to a virtual neuron through a virtual synapse. The design and implementation of the necessary software to administer these dynamic clamp experiments can be difficult. In this chapter, we provide an overview of designing and implementing a dynamic clamp experiment using the Real-Time eXperiment Interface (RTXI), an open- source software system tailored for real-time biological experiments. We present two ways to achieve this using RTXI’s modular format, through the creation of a custom user-made module and through existing modules found in RTXI’s online library. PMID:25023319

Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time.

PubMed

Jack, Rachael E; Garrod, Oliver G B; Schyns, Philippe G

2014-01-20

Designed by biological and social evolutionary pressures, facial expressions of emotion comprise specific facial movements to support a near-optimal system of signaling and decoding. Although highly dynamical, little is known about the form and function of facial expression temporal dynamics. Do facial expressions transmit diagnostic signals simultaneously to optimize categorization of the six classic emotions, or sequentially to support a more complex communication system of successive categorizations over time? Our data support the latter. Using a combination of perceptual expectation modeling, information theory, and Bayesian classifiers, we show that dynamic facial expressions of emotion transmit an evolving hierarchy of "biologically basic to socially specific" information over time. Early in the signaling dynamics, facial expressions systematically transmit few, biologically rooted face signals supporting the categorization of fewer elementary categories (e.g., approach/avoidance). Later transmissions comprise more complex signals that support categorization of a larger number of socially specific categories (i.e., the six classic emotions). Here, we show that dynamic facial expressions of emotion provide a sophisticated signaling system, questioning the widely accepted notion that emotion communication is comprised of six basic (i.e., psychologically irreducible) categories, and instead suggesting four. Copyright © 2014 Elsevier Ltd. All rights reserved.

On Mechanical Transitions in Biologically Motivated Soft Matter Systems

NASA Astrophysics Data System (ADS)

Fogle, Craig

The notion of phase transitions as a characterization of a change in physical properties pervades modern physics. Such abrupt and fundamental changes in the behavior of physical systems are evident in condensed matter system and also occur in nuclear and subatomic settings. While this concept is less prevalent in the field of biology, recent advances have pointed to its relevance in a number of settings. Recent studies have modeled both the cell cycle and cancer as phase transition in physical systems. In this dissertation we construct simplified models for two biological systems. As described by those models, both systems exhibit phase transitions. The first model is inspired by the shape transition in the nuclei of neutrophils during differentiation. During differentiation the nucleus transitions from spherical to a shape often described as "beads on a string." As a simplified model of this system, we investigate the spherical-to-wrinkled transition in an elastic core bounded to a fluid shell system. We find that this model exhibits a first-order phase transition, and the shape that minimizes the energy of the system scales as (micror3/kappa). . The second system studied is motivated by the dynamics of globular proteins. These proteins may undergoes conformational changes with large displacements relative to their size. Transitions between conformational states are not possible if the dynamics are governed strictly by linear elasticity. We construct a model consisting of an predominantly elastic region near the energetic minimum of the system and a non-linear softening of the system at a critical displacement. We find that this simple model displays very rich dynamics include a sharp dynamical phase transition and driving-force-dependent symmetry breaking.

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

PubMed

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

2014-09-01

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

Normal and Anomalous Diffusion: An Analytical Study Based on Quantum Collision Dynamics and Boltzmann Transport Theory.

PubMed

Mahakrishnan, Sathiya; Chakraborty, Subrata; Vijay, Amrendra

2016-09-15

Diffusion, an emergent nonequilibrium transport phenomenon, is a nontrivial manifestation of the correlation between the microscopic dynamics of individual molecules and their statistical behavior observed in experiments. We present a thorough investigation of this viewpoint using the mathematical tools of quantum scattering, within the framework of Boltzmann transport theory. In particular, we ask: (a) How and when does a normal diffusive transport become anomalous? (b) What physical attribute of the system is conceptually useful to faithfully rationalize large variations in the coefficient of normal diffusion, observed particularly within the dynamical environment of biological cells? To characterize the diffusive transport, we introduce, analogous to continuous phase transitions, the curvature of the mean square displacement as an order parameter and use the notion of quantum scattering length, which measures the effective interactions between the diffusing molecules and the surrounding, to define a tuning variable, η. We show that the curvature signature conveniently differentiates the normal diffusion regime from the superdiffusion and subdiffusion regimes and the critical point, η = ηc, unambiguously determines the coefficient of normal diffusion. To solve the Boltzmann equation analytically, we use a quantum mechanical expression for the scattering amplitude in the Boltzmann collision term and obtain a general expression for the effective linear collision operator, useful for a variety of transport studies. We also demonstrate that the scattering length is a useful dynamical characteristic to rationalize experimental observations on diffusive transport in complex systems. We assess the numerical accuracy of the present work with representative experimental results on diffusion processes in biological systems. Furthermore, we advance the idea of temperature-dependent effective voltage (of the order of 1 μV or less in a biological environment, for example) as a dynamical cause of the perpetual molecular movement, which eventually manifests as an ordered motion, called the diffusion.

A social ecology approach to understanding urban ecosystems and landscapes

Treesearch

J. Morgan Grove; Karen E. Hinson; Robert J. Northrop

2003-01-01

The shape and dynamics of cities are the result of physical, biological, and social forces. We include the term dynamic to emphasize that cities change over time and are the result of both idiosyncratic events and dominant trends. To begin to understand the patterns and processes of cities, we approach the idiosyncratic and dominant - whether it is physical, biological...

Biological Response to the Dynamic Spectral-Polarized Underwater Light Field

DTIC Science & Technology

2011-09-30

www.bio.utexas.edu/research/cummingslab/ LONG-TERM GOALS Camouflage in marine environments requires matching all of the background optical ...polarized light field in near-shore and near-surface environments (2) Characterize the biological camouflage response of organisms to these dynamic optical ...field will be measured by the simultaneous deployment of a comprehensive optical suite including underwater video-polarimetry (Cummings), inherent

The ''self-stirred'' genome: Bulk and surface dynamics of the chromatin globule

NASA Astrophysics Data System (ADS)

Zidovska, Alexandra

Chromatin structure and dynamics control all aspects of DNA biology yet are poorly understood. In interphase, time between two cell divisions, chromatin fills the cell nucleus in its minimally condensed polymeric state. Chromatin serves as substrate to a number of biological processes, e.g. gene expression and DNA replication, which require it to become locally restructured. These are energy-consuming processes giving rise to non-equilibrium dynamics. Chromatin dynamics has been traditionally studied by imaging of fluorescently labeled nuclear proteins and single DNA-sites, thus focusing only on a small number of tracer particles. Recently, we developed an approach, displacement correlation spectroscopy (DCS) based on time-resolved image correlation analysis, to map chromatin dynamics simultaneously across the whole nucleus in cultured human cells. DCS revealed that chromatin movement was coherent across large regions (4-5 μm) for several seconds. Regions of coherent motion extended beyond the boundaries of single-chromosome territories, suggesting elastic coupling of motion over length scales much larger than those of genes. These large-scale, coupled motions were ATP-dependent and unidirectional for several seconds. Following these observations, we developed a hydrodynamic theory of active chromatin dynamics, using the two-fluid model and describing the content of cell nucleus as a chromatin solution, which is subject to both passive thermal fluctuations and active (ATP-consuming) scalar and vector events. In this work we continue in our efforts to elucidate the mechanism and function of the chromatin dynamics in interphase. We investigate the chromatin interactions with the nuclear envelope and compare the surface dynamics of the chromatin globule with its bulk dynamics.

Counting motifs in dynamic networks.

PubMed

Mukherjee, Kingshuk; Hasan, Md Mahmudul; Boucher, Christina; Kahveci, Tamer

2018-04-11

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Discrete virus infection model of hepatitis B virus.

PubMed

Zhang, Pengfei; Min, Lequan; Pian, Jianwei

2015-01-01

In 1996 Nowak and his colleagues proposed a differential equation virus infection model, which has been widely applied in the study for the dynamics of hepatitis B virus (HBV) infection. Biological dynamics may be described more practically by discrete events rather than continuous ones. Using discrete systems to describe biological dynamics should be reasonable. Based on one revised Nowak et al's virus infection model, this study introduces a discrete virus infection model (DVIM). Two equilibriums of this model, E1 and E2, represents infection free and infection persistent, respectively. Similar to the case of the basic virus infection model, this study deduces a basic virus reproductive number R0 independing on the number of total cells of an infected target organ. A proposed theorem proves that if the basic virus reproductive number R0<1 then the virus free equilibrium E1 is locally stable. The DVIM is more reasonable than an abstract discrete susceptible-infected-recovered model (SIRS) whose basic virus reproductive number R0 is relevant to the number of total cells of the infected target organ. As an application, this study models the clinic HBV DNA data of a patient who was accepted via anti-HBV infection therapy with drug lamivudine. The results show that the numerical simulation is good in agreement with the clinic data.

Self-Organization Processes at the Intracellular Level

NASA Astrophysics Data System (ADS)

Ponce Dawson, Silvina

2003-03-01

In spite of their relatively small sizes, cells are incredibly complex objects in which various sorts of self-organizing processes occur. Cell division is an example of a process that nearly all cells undergo in which a concerted sequence of events takes place. What are the signals that tell the cell to move along this sequence? Clearly, this is a self-organized process. Microtubules (long polymers that are part of the cytoskeleton) and calcium signals play a major role during cell division. In this course we will focus on some features of microtubule dynamics and calcium signals that are amenable to modeling. In both of these biological systems, behaviors at a single molecule level are key determinants of the self-organized dynamics that is observed at larger scales. Thus, the modeling of these systems presents interesting challenges which require novel strategies. Their study may not only provide answers for biologically motivated questions, but is also a natural setting in which the transition between particle-like and mean-field models can be explored. In this course we will first give a brief biological introduction on the structure of eukaryotic cells, microtubule dynamics and intracellular calcium signals. We will then describe some of the models that have been presented in the literature and discuss the spatio-temporal dynamics that they predict, comparing them with observed behaviors in vitro or in vivo. We will end with a discussion on the virtues and limitations of the various modeling strategies described.

In vivo study of lipid synthesis and lipolysis dynamics by stimulated Raman scattering microscopy

NASA Astrophysics Data System (ADS)

Li, Xuesong; Li, Yan; He, Sicong; Chen, Congping; Qin, Zhongya; Mak, Ho Yi; Qu, Jianan Y.

2018-02-01

To understand the mechanisms of important lipid-related biological processes and diseases, it is highly demanded to study the dynamics of lipids in living biological system with high spatiotemporal resolution. However, in vivo quantitative analysis of lipid synthesis and lipolysis has been technically difficult to achieve by conventional lipid extraction and fluorescent staining methods. Recently, SRS microscopy has emerged as a powerful tool to probe small molecules with alkyne (C≡C) or deuterium (C-D) labeling in cell-silent region. The Raman tags have been used for the quantitative study of lipids in cells. In this study, we investigated metabolic dynamics of lipid droplets (LDs) by tracing the alkyne-tagged fatty acid 17-ODYA and deuterium-labeled saturated and unsaturated fatty acids PA-D31 & OA-D34 in living C. elegans. Specifically, we developed a hyperspectral SRS microscope system for LDs characterization. The system can sequentially excite and probe the stimulated Raman scattering-induced CH2 stretching of endogenous lipids information (2863 cm-1), C≡C stretching from 17-ODYA (2125 cm-1) and C-D stretching from deuterium-labeled fatty acids (2117 cm-1). We first examined the concentration levels of fatty acids in E. coli OP50. Two major lipid metabolic processes, namely uptake and turnover, were further studied in adult C. elegans. We imaged alkyne-tagged and deuterated fatty acids using SRS and traced their uptake, transportation, incorporation and turnover over time. Additionally, several other treatments including starvation were also conducted to study their effects on metabolic dynamics of pulse labeled 17-ODYA, PA-D31 and OA-D34.

Quantum Dynamics in Biological Systems

NASA Astrophysics Data System (ADS)

Shim, Sangwoo

In the first part of this dissertation, recent efforts to understand quantum mechanical effects in biological systems are discussed. Especially, long-lived quantum coherences observed during the electronic energy transfer process in the Fenna-Matthews-Olson complex at physiological condition are studied extensively using theories of open quantum systems. In addition to the usual master equation based approaches, the effect of the protein structure is investigated in atomistic detail through the combined application of quantum chemistry and molecular dynamics simulations. To evaluate the thermalized reduced density matrix, a path-integral Monte Carlo method with a novel importance sampling approach is developed for excitons coupled to an arbitrary phonon bath at a finite temperature. In the second part of the thesis, simulations of molecular systems and applications to vibrational spectra are discussed. First, the quantum dynamics of a molecule is simulated by combining semiclassical initial value representation and density funcitonal theory with analytic derivatives. A computationally-tractable approximation to the sum-of-states formalism of Raman spectra is subsequently discussed.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

[Interdependence of plankton spatial distribution and plancton biomass temporal oscillations: mathematical simulation].

PubMed

Medvedinskiĭ, A B; Tikhonova, I A; Li, B L; Malchow, H

2003-01-01

The dynamics of aquatic biological communities in a patchy environment is of great interest in respect to interrelations between phenomena at various spatial and time scales. To study the complex plankton dynamics in relation to variations of such a biologically essential parameter as the fish predation rate, we use a simple reaction-diffusion model of trophic interactions between phytoplankton, zooplankton, and fish. We suggest that plankton is distributed between two habitats one of which is fish-free due to hydrological inhomogeneity, while the other is fish-populated. We show that temporal variations in the fish predation rate do not violate the strong correspondence between the character of spatial distribution of plankton and changes of plankton biomass in time: regular temporal oscillations of plankton biomass correspond to large-scale plankton patches, while chaotic oscillations correspond to small-scale plankton patterns. As in the case of the constant fish predation rate, the chaotic plankton dynamics is characterized by coexistence of the chaotic attractor and limit cycle.

Fluid models and simulations of biological cell phenomena

NASA Technical Reports Server (NTRS)

Greenspan, H. P.

1982-01-01

The dynamics of coated droplets are examined within the context of biofluids. Of specific interest is the manner in which the shape of a droplet, the motion within it as well as that of aggregates of droplets can be controlled by the modulation of surface properties and the extent to which such fluid phenomena are an intrinsic part of cellular processes. From the standpoint of biology, an objective is to elucidate some of the general dynamical features that affect the disposition of an entire cell, cell colonies and tissues. Conventionally averaged field variables of continuum mechanics are used to describe the overall global effects which result from the myriad of small scale molecular interactions. An attempt is made to establish cause and effect relationships from correct dynamical laws of motion rather than by what may have been unnecessary invocation of metabolic or life processes. Several topics are discussed where there are strong analogies droplets and cells including: encapsulated droplets/cell membranes; droplet shape/cell shape; adhesion and spread of a droplet/cell motility and adhesion; and oams and multiphase flows/cell aggregates and tissues. Evidence is presented to show that certain concepts of continuum theory such as suface tension, surface free energy, contact angle, bending moments, etc. are relevant and applicable to the study of cell biology.

Emergent properties of interacting populations of spiking neurons.

PubMed

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems.

PubMed

Adams, Alyssa; Zenil, Hector; Davies, Paul C W; Walker, Sara Imari

2017-04-20

Open-ended evolution (OEE) is relevant to a variety of biological, artificial and technological systems, but has been challenging to reproduce in silico. Most theoretical efforts focus on key aspects of open-ended evolution as it appears in biology. We recast the problem as a more general one in dynamical systems theory, providing simple criteria for open-ended evolution based on two hallmark features: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we implement novel variants of cellular automata (CA) where the update rules are allowed to vary with time in three alternative ways. Each is capable of generating conditions for open-ended evolution, but vary in their ability to do so. We find that state-dependent dynamics, regarded as a hallmark of life, statistically out-performs other candidate mechanisms, and is the only mechanism to produce open-ended evolution in a scalable manner, essential to the notion of ongoing evolution. This analysis suggests a new framework for unifying mechanisms for generating OEE with features distinctive to life and its artifacts, with broad applicability to biological and artificial systems.

Effects of noise on a computational model for disease states of mood disorders

NASA Astrophysics Data System (ADS)

Tobias Huber, Martin; Krieg, Jürgen-Christian; Braun, Hans Albert; Moss, Frank

2000-03-01

Nonlinear dynamics are currently proposed to explain the progressive course of recurrent mood disorders starting with isolated episodes and ending with accelerated irregular (``chaotic") mood fluctuations. Such a low-dimensional disease model is attractive because of its principal accordance with biological disease models, i.e. the kindling and biological rhythms model. However, most natural systems are nonlinear and noisy and several studies in the neuro- and physical sciences have demonstrated interesting cooperative behaviors arising from interacting random and deterministic dynamics. Here, we consider the effects of noise on a recent neurodynamical model for the timecourse of affective disorders (Huber et al.: Biological Psychiatry 1999;46:256-262). We describe noise effects on temporal patterns and mean episode frequencies of various in computo disease states. Our simulations demonstrate that noise can cause unstructured randomness or can maximize periodic order. The frequency of episode occurence can increase with noise but it can also remain unaffected or even can decrease. We show further that noise can make visible bifurcations before they would normally occur under deterministic conditions and we quantify this behavior with a recently developed statistical method. All these effects depend critically on both, the dynamic state and the noise intensity. Implications for neurobiology and course of mood disorders are discussed.

Emergent Properties of Interacting Populations of Spiking Neurons

PubMed Central

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations. PMID:22207844

Dynamics of problem setting and framing in citizen discussions on synthetic biology

PubMed Central

Betten, Afke Wieke; Broerse, Jacqueline E.W.; Kupper, Frank

2017-01-01

Synthetic biology is an emerging scientific field where engineers and biologists design and build biological systems for various applications. Developing synthetic biology responsibly in the public interest necessitates a meaningful societal dialogue. In this article, we argue that facilitating such a dialogue requires an understanding of how people make sense of synthetic biology. We performed qualitative research to unravel the underlying dynamics of problem setting and framing in citizen discussions on synthetic biology. We found that most people are not inherently for or against synthetic biology as a technology or development in itself, but that their perspectives are framed by core values about our relationships with science and technology and that sensemaking is much dependent on the context and general feelings of (dis)content. Given that there are many assumptions focused on a more binary idea of the public’s view, we emphasize the need for frame awareness and understanding in a meaningful dialogue. PMID:28597721

From immunology to MRI data anlysis: Problems in mathematical biology

NASA Astrophysics Data System (ADS)

Waters, Ryan Samuel

This thesis represents a collection of four distinct biological projects rising from immunology and metabolomics that required unique and creative mathematical approaches. One project focuses on understanding the role IL-2 plays in immune response regulation and exploring how these effects can be altered. We developed several dynamic models of the receptor signaling network which we analyze analytically and numerically. In a second project focused also on MS, we sought to create a system for grading magnetic resonance images (MRI) with good correlation with disability. The goal is for these MRI scores to provide a better standard for large-scale clinical drug trials, which limits the bias associated with differences in available MRI technology and general grader/participant variability. The third project involves the study of the CRISPR adaptive immune system in bacteria. Bacterial cells recognize and acquire snippets of exogenous genetic material, which they incorporate into their DNA. In this project we explore the optimal design for the CRISPR system given a viral distribution to maximize its probability of survival. The final project involves the study of the benefits for colocalization of coupled enzymes in metabolic pathways. The hypothesized kinetic advantage, known as `channeling', of putting coupled enzymes closer together has been used as justification for the colocalization of coupled enzymes in biological systems. We developed and analyzed a simple partial differential equation of the diffusion of the intermediate substrate between coupled enzymes to explore the phenomena of channeling. The four projects of my thesis represent very distinct biological problems that required a variety of techniques from diverse areas of mathematics ranging from dynamical modeling to statistics, Fourier series and calculus of variations. In each case, quantitative techniques were used to address biological questions from a mathematical perspective ultimately providing insight back to the biological problems which motivated them.

High-Frequency Electron Paramagnetic Resonance Spectroscopy of Nitroxide-Functionalized Nanodiamonds in Aqueous Solution.

PubMed

Akiel, R D; Stepanov, V; Takahashi, S

2017-06-01

Nanodiamond (ND) is an attractive class of nanomaterial for fluorescent labeling, magnetic sensing of biological molecules, and targeted drug delivery. Many of those applications require tethering of target biological molecules on the ND surface. Even though many approaches have been developed to attach macromolecules to the ND surface, it remains challenging to characterize dynamics of tethered molecule. Here, we show high-frequency electron paramagnetic resonance (HF EPR) spectroscopy of nitroxide-functionalized NDs. Nitroxide radical is a commonly used spin label to investigate dynamics of biological molecules. In the investigation, we developed a sample holder to overcome water absorption of HF microwave. Then, we demonstrated HF EPR spectroscopy of nitroxide-functionalized NDs in aqueous solution and showed clear spectral distinction of ND and nitroxide EPR signals. Moreover, through EPR spectral analysis, we investigate dynamics of nitroxide radicals on the ND surface. The demonstration sheds light on the use of HF EPR spectroscopy to investigate biological molecule-functionalized nanoparticles.

Chemical and Biological Sensor Standards Study

DTIC Science & Technology

2005-01-01

that is utilized in lieu of Bacillus anthracis in testing biological agent sensors; both are gram positive, spore forming bacteria that have similar...for a given agent dosage is as follows: C = D r 3 f B Tη4π 3 ρ See the table for the variable designation. Using Bacillus anthracis as an example...e.g., genetic similarity, aerosol dynamics, size, shape, etc.) of the agent of interest. For example, Bacillus globigii is a widely used bacterium

Self-spreading of the wetting ridge during stick-slip on a viscoelastic surface

DOE PAGES

Park, S. J.; Bostwick, J. B.; De Andrade, V.; ...

2017-10-23

Dynamic wetting behaviors on soft solids are important to interpret complex biological processes from cell–substrate interactions. Despite intensive research studies over the past half-century, the underlying mechanisms of spreading behaviors are not clearly understood. The most interesting feature of wetting on soft matter is the formation of a “wetting ridge”, a surface deformation by a competition between elasticity and capillarity. Dynamics of the wetting ridge formed at the three-phase contact line underlies the dynamic wetting behaviors, but remains largely unexplored mostly due to limitations in indirect observation. Here, we directly visualize wetting ridge dynamics during continuous- and stick-slip motions onmore » a viscoelastic surface using X-ray microscopy. Strikingly, we discover that the ridge spreads spontaneously during stick and triggers contact line depinning (stick-to-slip transition) by changing the ridge geometry which weakens the contact line pinning. Finally, we clarify ‘viscoelastic-braking’, ‘stick-slipping’, and ‘stick-breaking’ spreading behaviors through the ridge dynamics. In stick-breaking, no ridge-spreading occurs and contact line pinning (hysteresis) is enhanced by cusp-bending while preserving a microscopic equilibrium at the ridge tip. We have furthered the understanding of spreading behaviors on soft solids and demonstrated the value of X-ray microscopy in elucidating various dynamic wetting behaviors on soft solids as well as puzzling biological issues.« less

Reconstitution of a nanomachine driving the assembly of proteins into bacterial outer membranes

NASA Astrophysics Data System (ADS)

Shen, Hsin-Hui; Leyton, Denisse L.; Shiota, Takuya; Belousoff, Matthew J.; Noinaj, Nicholas; Lu, Jingxiong; Holt, Stephen A.; Tan, Khershing; Selkrig, Joel; Webb, Chaille T.; Buchanan, Susan K.; Martin, Lisandra L.; Lithgow, Trevor

2014-10-01

In biological membranes, various protein secretion devices function as nanomachines, and measuring the internal movements of their component parts is a major technological challenge. The translocation and assembly module (TAM) is a nanomachine required for virulence of bacterial pathogens. We have reconstituted a membrane containing the TAM onto a gold surface for characterization by quartz crystal microbalance with dissipation (QCM-D) and magnetic contrast neutron reflectrometry (MCNR). The MCNR studies provided structural resolution down to 1 Å, enabling accurate measurement of protein domains projecting from the membrane layer. Here we show that dynamic movements within the TamA component of the TAM are initiated in the presence of a substrate protein, Ag43, and that these movements recapitulate an initial stage in membrane protein assembly. The reconstituted system provides a powerful new means to study molecular movements in biological membranes, and the technology is widely applicable to studying the dynamics of diverse cellular nanomachines.

Postnatal brain development: Structural imaging of dynamic neurodevelopmental processes

PubMed Central

Jernigan, Terry L.; Baaré, William F. C.; Stiles, Joan; Madsen, Kathrine Skak

2013-01-01

After birth, there is striking biological and functional development of the brain’s fiber tracts as well as remodeling of cortical and subcortical structures. Behavioral development in children involves a complex and dynamic set of genetically guided processes by which neural structures interact constantly with the environment. This is a protracted process, beginning in the third week of gestation and continuing into early adulthood. Reviewed here are studies using structural imaging techniques, with a special focus on diffusion weighted imaging, describing age-related brain maturational changes in children and adolescents, as well as studies that link these changes to behavioral differences. Finally, we discuss evidence for effects on the brain of several factors that may play a role in mediating these brain–behavior associations in children, including genetic variation, behavioral interventions, and hormonal variation associated with puberty. At present longitudinal studies are few, and we do not yet know how variability in individual trajectories of biological development in specific neural systems map onto similar variability in behavioral trajectories. PMID:21489384

Ocean dynamics, not dust, have controlled equatorial Pacific productivity over the past 500,000 years

PubMed Central

Winckler, Gisela; Anderson, Robert F.; Jaccard, Samuel L.; Marcantonio, Franco

2016-01-01

Biological productivity in the equatorial Pacific is relatively high compared with other low-latitude regimes, especially east of the dateline, where divergence driven by the trade winds brings nutrient-rich waters of the Equatorial Undercurrent to the surface. The equatorial Pacific is one of the three principal high-nutrient low-chlorophyll ocean regimes where biological utilization of nitrate and phosphate is limited, in part, by the availability of iron. Throughout most of the equatorial Pacific, upwelling of water from the Equatorial Undercurrent supplies far more dissolved iron than is delivered by dust, by as much as two orders of magnitude. Nevertheless, recent studies have inferred that the greater supply of dust during ice ages stimulated greater utilization of nutrients within the region of upwelling on the equator, thereby contributing to the sequestration of carbon in the ocean interior. Here we present proxy records for dust and for biological productivity over the past 500 ky at three sites spanning the breadth of the equatorial Pacific Ocean to test the dust fertilization hypothesis. Dust supply peaked under glacial conditions, consistent with previous studies, whereas proxies of export production exhibit maxima during ice age terminations. Temporal decoupling between dust supply and biological productivity indicates that other factors, likely involving ocean dynamics, played a greater role than dust in regulating equatorial Pacific productivity. PMID:27185933

Detailed transient heme structures of Mb-CO in solution after CO dissociation: an X-ray transient absorption spectroscopic study.

PubMed

Stickrath, Andrew B; Mara, Michael W; Lockard, Jenny V; Harpham, Michael R; Huang, Jier; Zhang, Xiaoyi; Attenkofer, Klaus; Chen, Lin X

2013-04-25

Although understanding the structural dynamics associated with ligand photodissociation is necessary in order to correlate structure and function in biological systems, few techniques are capable of measuring the ultrafast dynamics of these systems in solution-phase at room temperature. We present here a detailed X-ray transient absorption (XTA) study of the photodissociation of CO-bound myoglobin (Fe(II)CO-Mb) in room-temperature aqueous buffer solution with a time resolution of 80 ps, along with a general procedure for handling biological samples under the harsh experimental conditions that transient X-ray experiments entail. The XTA spectra of (Fe(II)CO-Mb) exhibit significant XANES and XAFS alterations following 527 nm excitation, which remain unchanged for >47 μs. These spectral changes indicate loss of the CO ligand, resulting in a five-coordinate, domed heme, and significant energetic reorganization of the 3d orbitals of the Fe center. With the current experimental setup, each X-ray pulse in the pulse train, separated by ~153 ns, can be separately discriminated, yielding snapshots of the myoglobin evolution over time. These methods can be easily applied to other biological systems, allowing for simultaneous structural and electronic measurements of any biological system with both ultrafast and slow time resolutions, effectively mapping out all of the samples' relevant physiological processes.

How causal analysis can reveal autonomy in models of biological systems

NASA Astrophysics Data System (ADS)

Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa

2017-11-01

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Ocean dynamics, not dust, have controlled equatorial Pacific productivity over the past 500,000 years

NASA Astrophysics Data System (ADS)

Winckler, Gisela; Anderson, Robert F.; Jaccard, Samuel L.; Marcantonio, Franco

2016-05-01

Biological productivity in the equatorial Pacific is relatively high compared with other low-latitude regimes, especially east of the dateline, where divergence driven by the trade winds brings nutrient-rich waters of the Equatorial Undercurrent to the surface. The equatorial Pacific is one of the three principal high-nutrient low-chlorophyll ocean regimes where biological utilization of nitrate and phosphate is limited, in part, by the availability of iron. Throughout most of the equatorial Pacific, upwelling of water from the Equatorial Undercurrent supplies far more dissolved iron than is delivered by dust, by as much as two orders of magnitude. Nevertheless, recent studies have inferred that the greater supply of dust during ice ages stimulated greater utilization of nutrients within the region of upwelling on the equator, thereby contributing to the sequestration of carbon in the ocean interior. Here we present proxy records for dust and for biological productivity over the past 500 ky at three sites spanning the breadth of the equatorial Pacific Ocean to test the dust fertilization hypothesis. Dust supply peaked under glacial conditions, consistent with previous studies, whereas proxies of export production exhibit maxima during ice age terminations. Temporal decoupling between dust supply and biological productivity indicates that other factors, likely involving ocean dynamics, played a greater role than dust in regulating equatorial Pacific productivity.

Evolutionary Dynamics of Biological Auctions

PubMed Central

Chatterjee, Krishnendu; Reiter, Johannes G.; Nowak, Martin A.

2011-01-01

Many scenarios in the living world, where individual organisms compete for winning positions (or resources), have properties of auctions. Here we study the evolution of bids in biological auctions. For each auction n individuals are drawn at random from a population of size N. Each individual makes a bid which entails a cost. The winner obtains a benefit of a certain value. Costs and benefits are translated into reproductive success (fitness). Therefore, successful bidding strategies spread in the population. We compare two types of auctions. In “biological all-pay auctions” the costs are the bid for every participating individual. In “biological second price all-pay auctions” the cost for everyone other than the winner is the bid, but the cost for the winner is the second highest bid. Second price all-pay auctions are generalizations of the “war of attrition” introduced by Maynard Smith. We study evolutionary dynamics in both types of auctions. We calculate pairwise invasion plots and evolutionarily stable distributions over the continuous strategy space. We find that the average bid in second price all-pay auctions is higher than in all-pay auctions, but the average cost for the winner is similar in both auctions. In both cases the average bid is a declining function of the number of participants, n. The more individuals participate in an auction the smaller is the chance of winning, and thus expensive bids must be avoided. PMID:22120126

Ocean dynamics, not dust, have controlled equatorial Pacific productivity over the past 500,000 years.

PubMed

Winckler, Gisela; Anderson, Robert F; Jaccard, Samuel L; Marcantonio, Franco

2016-05-31

Biological productivity in the equatorial Pacific is relatively high compared with other low-latitude regimes, especially east of the dateline, where divergence driven by the trade winds brings nutrient-rich waters of the Equatorial Undercurrent to the surface. The equatorial Pacific is one of the three principal high-nutrient low-chlorophyll ocean regimes where biological utilization of nitrate and phosphate is limited, in part, by the availability of iron. Throughout most of the equatorial Pacific, upwelling of water from the Equatorial Undercurrent supplies far more dissolved iron than is delivered by dust, by as much as two orders of magnitude. Nevertheless, recent studies have inferred that the greater supply of dust during ice ages stimulated greater utilization of nutrients within the region of upwelling on the equator, thereby contributing to the sequestration of carbon in the ocean interior. Here we present proxy records for dust and for biological productivity over the past 500 ky at three sites spanning the breadth of the equatorial Pacific Ocean to test the dust fertilization hypothesis. Dust supply peaked under glacial conditions, consistent with previous studies, whereas proxies of export production exhibit maxima during ice age terminations. Temporal decoupling between dust supply and biological productivity indicates that other factors, likely involving ocean dynamics, played a greater role than dust in regulating equatorial Pacific productivity.

Multiscale Hy3S: hybrid stochastic simulation for supercomputers.

PubMed

Salis, Howard; Sotiropoulos, Vassilios; Kaznessis, Yiannis N

2006-02-24

Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems.

Nano-scale measurement of biomolecules by optical microscopy and semiconductor nanoparticles

PubMed Central

Ichimura, Taro; Jin, Takashi; Fujita, Hideaki; Higuchi, Hideo; Watanabe, Tomonobu M.

2014-01-01

Over the past decade, great developments in optical microscopy have made this technology increasingly compatible with biological studies. Fluorescence microscopy has especially contributed to investigating the dynamic behaviors of live specimens and can now resolve objects with nanometer precision and resolution due to super-resolution imaging. Additionally, single particle tracking provides information on the dynamics of individual proteins at the nanometer scale both in vitro and in cells. Complementing advances in microscopy technologies has been the development of fluorescent probes. The quantum dot, a semi-conductor fluorescent nanoparticle, is particularly suitable for single particle tracking and super-resolution imaging. This article overviews the principles of single particle tracking and super resolution along with describing their application to the nanometer measurement/observation of biological systems when combined with quantum dot technologies. PMID:25120488

Mathematical and Computational Challenges in Population Biology and Ecosystems Science

NASA Technical Reports Server (NTRS)

Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.

1997-01-01

Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.

Shape Mode Analysis Exposes Movement Patterns in Biology: Flagella and Flatworms as Case Studies

PubMed Central

Werner, Steffen; Rink, Jochen C.; Riedel-Kruse, Ingmar H.; Friedrich, Benjamin M.

2014-01-01

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way. PMID:25426857

Ecology as the Theme of a High School Advanced Biology Course

ERIC Educational Resources Information Center

Phillips, Lida

1977-01-01

This article describes an ecology course based upon the concepts of diversity and dynamic interaction. Most of the class time is spent doing field or laboratory activities; students assume most of the preparation and participation responsibilities. Activities include aquatic studies, museum visits, and terrestrial studies. (MA)

Growth-rate-dependent dynamics of a bacterial genetic oscillator

NASA Astrophysics Data System (ADS)

Osella, Matteo; Lagomarsino, Marco Cosentino

2013-01-01

Gene networks exhibiting oscillatory dynamics are widespread in biology. The minimal regulatory designs giving rise to oscillations have been implemented synthetically and studied by mathematical modeling. However, most of the available analyses generally neglect the coupling of regulatory circuits with the cellular “chassis” in which the circuits are embedded. For example, the intracellular macromolecular composition of fast-growing bacteria changes with growth rate. As a consequence, important parameters of gene expression, such as ribosome concentration or cell volume, are growth-rate dependent, ultimately coupling the dynamics of genetic circuits with cell physiology. This work addresses the effects of growth rate on the dynamics of a paradigmatic example of genetic oscillator, the repressilator. Making use of empirical growth-rate dependencies of parameters in bacteria, we show that the repressilator dynamics can switch between oscillations and convergence to a fixed point depending on the cellular state of growth, and thus on the nutrients it is fed. The physical support of the circuit (type of plasmid or gene positions on the chromosome) also plays an important role in determining the oscillation stability and the growth-rate dependence of period and amplitude. This analysis has potential application in the field of synthetic biology, and suggests that the coupling between endogenous genetic oscillators and cell physiology can have substantial consequences for their functionality.

Physiological and pathophysiological reactive oxygen species as probed by EPR spectroscopy: the underutilized research window on muscle ageing

PubMed Central

A. Abdel‐Rahman, Engy; Mahmoud, Ali M.; Khalifa, Abdulrahman M.

2016-01-01

Abstract Reactive oxygen and nitrogen species (ROS and RNS) play crucial roles in triggering, mediating and regulating physiological and pathophysiological signal transduction pathways within the cell. Within the cell, ROS efflux is firmly controlled both spatially and temporally, making the study of ROS dynamics a challenging task. Different approaches have been developed for ROS assessment; however, many of these assays are not capable of direct identification or determination of subcellular localization of different ROS. Here we highlight electron paramagnetic resonance (EPR) spectroscopy as a powerful technique that is uniquely capable of addressing questions on ROS dynamics in different biological specimens and cellular compartments. Due to their critical importance in muscle functions and dysfunction, we discuss in some detail spin trapping of various ROS and focus on EPR detection of nitric oxide before highlighting how EPR can be utilized to probe biophysical characteristics of the environment surrounding a given stable radical. Despite the demonstrated ability of EPR spectroscopy to provide unique information on the identity, quantity, dynamics and environment of radical species, its applications in the field of muscle physiology, fatiguing and ageing are disproportionately infrequent. While reviewing the limited examples of successful EPR applications in muscle biology we conclude that the field would greatly benefit from more studies exploring ROS sources and kinetics by spin trapping, protein dynamics by site‐directed spin labelling, and membrane dynamics and global redox changes by spin probing EPR approaches. PMID:26801204

Protein-style dynamical transition in a non-biological polymer and a non-aqueous solvent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mamontov, E.; Sharma, V. K.; Borreguero, J. M.

Using neutron scattering and molecular dynamics simulation, techniques most often associated with protein dynamical transition studies, we have investigated the microscopic dynamics of one of the most common polymers, polystyrene, which was exposed to toluene vapor, mimicking the process of protein hydration from water vapor. Polystyrene with adsorbed toluene is an example of a solvent-solute system, which, unlike biopolymers, is anhydrous and lacks hydrogen bonding. Nevertheless, it exhibits the essential traits of the dynamical transition in biomolecules, such as a specific dependence of the microscopic dynamics of both solvent and host on the temperature and the amount of solvent adsorbed.more » Ultimately, we conclude that the protein dynamical transition is a manifestation of a universal solvent-solute dynamical relationship, which is not specific to either biomolecules as solute, or aqueous media as solvent, or even a particular type of interactions between solvent and solute.« less

Protein-style dynamical transition in a non-biological polymer and a non-aqueous solvent

DOE PAGES

Mamontov, E.; Sharma, V. K.; Borreguero, J. M.; ...

2016-03-15

Using neutron scattering and molecular dynamics simulation, techniques most often associated with protein dynamical transition studies, we have investigated the microscopic dynamics of one of the most common polymers, polystyrene, which was exposed to toluene vapor, mimicking the process of protein hydration from water vapor. Polystyrene with adsorbed toluene is an example of a solvent-solute system, which, unlike biopolymers, is anhydrous and lacks hydrogen bonding. Nevertheless, it exhibits the essential traits of the dynamical transition in biomolecules, such as a specific dependence of the microscopic dynamics of both solvent and host on the temperature and the amount of solvent adsorbed.more » Ultimately, we conclude that the protein dynamical transition is a manifestation of a universal solvent-solute dynamical relationship, which is not specific to either biomolecules as solute, or aqueous media as solvent, or even a particular type of interactions between solvent and solute.« less

Cellular automata simulation of topological effects on the dynamics of feed-forward motifs

PubMed Central

Apte, Advait A; Cain, John W; Bonchev, Danail G; Fong, Stephen S

2008-01-01

Background Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops. The search for the most efficient network architecture could be of particular interest for regulatory or signaling pathways in biology, as well as in computational and communication systems. Results Feed-forward motif dynamics were studied using cellular automata and compared with differential equation modeling. The number of cellular automata iterations needed for a 100% conversion of a substrate into a target product was used as an inverse measure of the transformation rate. Several basic topological patterns were identified that order the specific feed-forward constructions according to the rate of dynamics they enable. At the same number of network nodes and constant other parameters, the bi-parallel and tri-parallel motifs provide higher network efficacy than single feed-forward motifs. Additionally, a topological property of isodynamicity was identified for feed-forward motifs where different network architectures resulted in the same overall rate of the target production. Conclusion It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. These fundamental results can be used as a basis for simulating larger networks as combinations of smaller network modules with implications on studying synthetic gene circuits, small regulatory systems, and eventually dynamic whole-cell models. PMID:18304325

Temperature-altered predator-prey dynamics in freshwater ponds in Arctic Greenland

NASA Astrophysics Data System (ADS)

Culler, L. E.; Ayres, M.

2011-12-01

Temperature sets the pace of many biological processes including species interactions. Describing the response of terrestrial and aquatic habitats to climate warming therefore requires studies of cross-trophic level dynamics. I use freshwater pond ecosystems in Arctic Greenland to study how the thermal environment shapes interactions between predators and their prey. This system is of interest because warming trends are notable, freshwaters are responding rapidly and dynamically to changes in temperature, and the biology of freshwaters is intimately linked to the terrestrial environment. My focal species are the Arctic mosquito (Diptera: Culicidae, Aedes nigripes) and its invertebrate predator, a predaceous diving beetle (Coleoptera: Dytiscidae, Colymbetes dolabratus). Both species develop as larvae in snow-melt ponds in May and June. I used experimental and observational studies to test effects of temperature on larval mosquito growth rates and predation rates by C. dolabratus. Results indicate strong effects of temperature on growth rate and development time but weak effects of temperature on consumption of mosquitoes by their predators. Incorporation of measured temperature response functions into a mosquito demographic model will elucidate how mosquito population dynamics in Arctic Greenland may change with temperature. For example, warming increases growth rate and decreases development time of mosquito larvae, which shortens the time larvae are exposed to predation. Additionally, decreased development time leads to an earlier mosquito emergence, with potential consequences for the health of wildlife. Evaluation of this model will reveal the importance of considering cross-trophic level dynamics when predicting mosquito population response to warming. Future studies will address interesting properties emerging from modeling, such as how shorter development time affects adult size and fitness, and connecting results to terrestrial systems in Arctic Greenland.

A natural and readily available crowding agent: NMR studies of proteins in hen egg white.

PubMed

Martorell, Gabriel; Adrover, Miquel; Kelly, Geoff; Temussi, Piero Andrea; Pastore, Annalisa

2011-05-01

In vitro studies of biological macromolecules are usually performed in dilute, buffered solutions containing one or just a few different biological macromolecules. Under these conditions, the interactions among molecules are diffusion limited. On the contrary, in living systems, macromolecules of a given type are surrounded by many others, at very high total concentrations. In the last few years, there has been an increasing effort to study biological macromolecules directly in natural crowded environments, as in intact bacterial cells or by mimicking natural crowding by adding proteins, polysaccharides, or even synthetic polymers. Here, we propose the use of hen egg white (HEW) as a simple natural medium, with all features of the media of crowded cells, that could be used by any researcher without difficulty and inexpensively. We present a study of the stability and dynamics behavior of model proteins in HEW, chosen as a prototypical, readily accessible natural medium that can mimic cytosol. We show that two typical globular proteins, dissolved in HEW, give NMR spectra very similar to those obtained in dilute buffers, although dynamic parameters are clearly affected by the crowded medium. The thermal stability of one of these proteins, measured in a range comprising both heat and cold denaturation, is also similar to that in buffer. Our data open new possibilities to the study of proteins in natural crowded media. Copyright © 2010 Wiley-Liss, Inc.

Biomorphodynamics: Physical-biological feedbacks that shape landscapes

USGS Publications Warehouse

Murray, A.B.; Knaapen, M.A.F.; Tal, M.; Kirwan, M.L.

2008-01-01

Plants and animals affect morphological evolution in many environments. The term "ecogeomorphology" describes studies that address such effects. In this opinion article we use the term "biomorphodynamics" to characterize a subset of ecogeomorphologic studies: those that investigate not only the effects of organisms on physical processes and morphology but also how the biological processes depend on morphology and physical forcing. The two-way coupling precipitates feedbacks, leading to interesting modes of behavior, much like the coupling between flow/sediment transport and morphology leads to rich morphodynamic behaviors. Select examples illustrate how even the basic aspects of some systems cannot be understood without considering biomorphodynamic coupling. Prominent examples include the dynamic interactions between vegetation and flow/sediment transport that can determine river channel patterns and the multifaceted biomorphodynamic feedbacks shaping tidal marshes and channel networks. These examples suggest that the effects of morphology and physical processes on biology tend to operate over the timescale of the evolution of the morphological pattern. Thus, in field studies, which represent a snapshot in the pattern evolution, these effects are often not as obvious as the effects of biology on physical processes. However, numerical modeling indicates that the influences on biology from physical processes can play a key role in shaping landscapes and that even local and temporary vegetation disturbances can steer large-scale, long-term landscape evolution. The prevalence of biomorphodynamic research is burgeoning in recent years, driven by societal need and a confluence of complex systems-inspired modeling approaches in ecology and geomorphology. To make fundamental progress in understanding the dynamics of many landscapes, our community needs to increasingly learn to look for two-way, biomorphodynamic feedbacks and to collect new types of data to support the modeling of such emergent interactions. Copyright 2008 by the American Geophysical Union.

Amplification without instability: applying fluid dynamical insights in chemistry and biology

NASA Astrophysics Data System (ADS)

McCoy, Jonathan H.

2013-11-01

While amplification of small perturbations often arises from instability, transient amplification is possible locally even in asymptotically stable systems. That is, knowledge of a system's stability properties can mislead one's intuition for its transient behaviors. This insight, which has an interesting history in fluid dynamics, has more recently been rediscovered in ecology. Surprisingly, many nonlinear fluid dynamical and ecological systems share linear features associated with transient amplification of noise. This paper aims to establish that these features are widespread in many other disciplines concerned with noisy systems, especially chemistry, cell biology and molecular biology. Here, using classic nonlinear systems and the graphical language of network science, we explore how the noise amplification problem can be reframed in terms of activatory and inhibitory interactions between dynamical variables. The interaction patterns considered here are found in a great variety of systems, ranging from autocatalytic reactions and activator-inhibitor systems to influential models of nerve conduction, glycolysis, cell signaling and circadian rhythms.

Influence of changes in initial conditions for the simulation of dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotyrba, Martin

2015-03-10

Chaos theory is a field of study in mathematics, with applications in several disciplines including meteorology, sociology, physics, engineering, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions—a paradigm popularly referred to as the butterfly effect. Small differences in initial conditions field widely diverging outcomes for such dynamical systems, rendering long-term prediction impossible in general. This happens even though these systems are deterministic, meaning that their future behavior is fully determined by their initial conditions, with no random elements involved. In this paperinfluence of changes in initial conditions will bemore » presented for the simulation of Lorenz system.« less

Cameras on the NEPTUNE Canada seafloor observatory: Towards monitoring hydrothermal vent ecosystem dynamics

NASA Astrophysics Data System (ADS)

Robert, K.; Matabos, M.; Sarrazin, J.; Sarradin, P.; Lee, R. W.; Juniper, K.

2010-12-01

Hydrothermal vent environments are among the most dynamic benthic habitats in the ocean. The relative roles of physical and biological factors in shaping vent community structure remain unclear. Undersea cabled observatories offer the power and bandwidth required for high-resolution, time-series study of the dynamics of vent communities and the physico-chemical forces that influence them. The NEPTUNE Canada cabled instrument array at the Endeavour hydrothermal vents provides a unique laboratory for researchers to conduct long-term, integrated studies of hydrothermal vent ecosystem dynamics in relation to environmental variability. Beginning in September-October 2010, NEPTUNE Canada (NC) will be deploying a multi-disciplinary suite of instruments on the Endeavour Segment of the Juan de Fuca Ridge. Two camera and sensor systems will be used to study ecosystem dynamics in relation to hydrothermal discharge. These studies will make use of new experimental protocols for time-series observations that we have been developing since 2008 at other observatory sites connected to the VENUS and NC networks. These protocols include sampling design, camera calibration (i.e. structure, position, light, settings) and image analysis methodologies (see communication by Aron et al.). The camera systems to be deployed in the Main Endeavour vent field include a Sidus high definition video camera (2010) and the TEMPO-mini system (2011), designed by IFREMER (France). Real-time data from three sensors (O2, dissolved Fe, temperature) integrated with the TEMPO-mini system will enhance interpretation of imagery. For the first year of observations, a suite of internally recording temperature probes will be strategically placed in the field of view of the Sidus camera. These installations aim at monitoring variations in vent community structure and dynamics (species composition and abundances, interactions within and among species) in response to changes in environmental conditions at different temporal scales. High-resolution time-series studies also provide a mean of studying population dynamics, biological rhythms, organism growth and faunal succession. In addition to programmed time-series monitoring, the NC infrastructure will also permit manual and automated modification of observational protocols in response to natural events. This will enhance our ability to document potentially critical but short-lived environmental forces affecting vent communities.

On the origin of biological construction, with a focus on multicellularity.

PubMed

van Gestel, Jordi; Tarnita, Corina E

2017-10-17

Biology is marked by a hierarchical organization: all life consists of cells; in some cases, these cells assemble into groups, such as endosymbionts or multicellular organisms; in turn, multicellular organisms sometimes assemble into yet other groups, such as primate societies or ant colonies. The construction of new organizational layers results from hierarchical evolutionary transitions, in which biological units (e.g., cells) form groups that evolve into new units of biological organization (e.g., multicellular organisms). Despite considerable advances, there is no bottom-up, dynamical account of how, starting from the solitary ancestor, the first groups originate and subsequently evolve the organizing principles that qualify them as new units. Guided by six central questions, we propose an integrative bottom-up approach for studying the dynamics underlying hierarchical evolutionary transitions, which builds on and synthesizes existing knowledge. This approach highlights the crucial role of the ecology and development of the solitary ancestor in the emergence and subsequent evolution of groups, and it stresses the paramount importance of the life cycle: only by evaluating groups in the context of their life cycle can we unravel the evolutionary trajectory of hierarchical transitions. These insights also provide a starting point for understanding the types of subsequent organizational complexity. The central research questions outlined here naturally link existing research programs on biological construction (e.g., on cooperation, multilevel selection, self-organization, and development) and thereby help integrate knowledge stemming from diverse fields of biology.

Equivalent formulations of “the equation of life”

NASA Astrophysics Data System (ADS)

Ao, Ping

2014-07-01

Motivated by progress in theoretical biology a recent proposal on a general and quantitative dynamical framework for nonequilibrium processes and dynamics of complex systems is briefly reviewed. It is nothing but the evolutionary process discovered by Charles Darwin and Alfred Wallace. Such general and structured dynamics may be tentatively named “the equation of life”. Three equivalent formulations are discussed, and it is also pointed out that such a quantitative dynamical framework leads naturally to the powerful Boltzmann-Gibbs distribution and the second law in physics. In this way, the equation of life provides a logically consistent foundation for thermodynamics. This view clarifies a particular outstanding problem and further suggests a unifying principle for physics and biology.

High-speed atomic force microscopy for observing protein molecules in dynamic action

NASA Astrophysics Data System (ADS)

Ando, T.

2017-02-01

Directly observing protein molecules in dynamic action at high spatiotemporal resolution has long been a holy grail for biological science. To materialize this long quested dream, I have been developing high-speed atomic force microscopy (HS-AFM) since 1993. Tremendous strides were recently accomplished in its high-speed and low-invasive performances. Consequently, various dynamic molecular actions, including bipedal walking of myosin V and rotary propagation of structural changes in F1-ATPase, were successfully captured on video. The visualized dynamic images not only provided irrefutable evidence for speculated actions of the protein molecules but also brought new discoveries inaccessible with other approaches, thus giving great mechanistic insights into how the molecules function. HS-AFM is now transforming "static" structural biology into dynamic structural bioscience.

Biophysical EPR Studies Applied to Membrane Proteins

PubMed Central

Sahu, Indra D; Lorigan, Gary A

2015-01-01

Membrane proteins are very important in controlling bioenergetics, functional activity, and initializing signal pathways in a wide variety of complicated biological systems. They also represent approximately 50% of the potential drug targets. EPR spectroscopy is a very popular and powerful biophysical tool that is used to study the structural and dynamic properties of membrane proteins. In this article, a basic overview of the most commonly used EPR techniques and examples of recent applications to answer pertinent structural and dynamic related questions on membrane protein systems will be presented. PMID:26855825

Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles

PubMed Central

Kononova, Olga; Snijder, Joost; Kholodov, Yaroslav; Marx, Kenneth A.; Wuite, Gijs J. L.; Roos, Wouter H.; Barsegov, Valeri

2016-01-01

The mechanical properties of virus capsids correlate with local conformational dynamics in the capsid structure. They also reflect the required stability needed to withstand high internal pressures generated upon genome loading and contribute to the success of important events in viral infectivity, such as capsid maturation, genome uncoating and receptor binding. The mechanical properties of biological nanoparticles are often determined from monitoring their dynamic deformations in Atomic Force Microscopy nanoindentation experiments; but a comprehensive theory describing the full range of observed deformation behaviors has not previously been described. We present a new theory for modeling dynamic deformations of biological nanoparticles, which considers the non-linear Hertzian deformation, resulting from an indenter-particle physical contact, and the bending of curved elements (beams) modeling the particle structure. The beams’ deformation beyond the critical point triggers a dynamic transition of the particle to the collapsed state. This extreme event is accompanied by a catastrophic force drop as observed in the experimental or simulated force (F)-deformation (X) spectra. The theory interprets fine features of the spectra, including the nonlinear components of the FX-curves, in terms of the Young’s moduli for Hertzian and bending deformations, and the structural damage dependent beams’ survival probability, in terms of the maximum strength and the cooperativity parameter. The theory is exemplified by successfully describing the deformation dynamics of natural nanoparticles through comparing theoretical curves with experimental force-deformation spectra for several virus particles. This approach provides a comprehensive description of the dynamic structural transitions in biological and artificial nanoparticles, which is essential for their optimal use in nanotechnology and nanomedicine applications. PMID:26821264

Transient Resetting: A Novel Mechanism for Synchrony and Its Biological Examples

PubMed Central

Li, Chunguang; Chen, Luonan; Aihara, Kazuyuki

2006-01-01

The study of synchronization in biological systems is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. In this paper, by using simple dynamical systems theory, we present a novel mechanism, named transient resetting, for the synchronization of uncoupled biological oscillators with stimuli. This mechanism not only can unify and extend many existing results on (deterministic and stochastic) stimulus-induced synchrony, but also may actually play an important role in biological rhythms. We argue that transient resetting is a possible mechanism for the synchronization in many biological organisms, which might also be further used in the medical therapy of rhythmic disorders. Examples of the synchronization of neural and circadian oscillators as well as a chaotic neuron model are presented to verify our hypothesis. PMID:16933980

Systematic methods for defining coarse-grained maps in large biomolecules.

PubMed

Zhang, Zhiyong

2015-01-01

Large biomolecules are involved in many important biological processes. It would be difficult to use large-scale atomistic molecular dynamics (MD) simulations to study the functional motions of these systems because of the computational expense. Therefore various coarse-grained (CG) approaches have attracted rapidly growing interest, which enable simulations of large biomolecules over longer effective timescales than all-atom MD simulations. The first issue in CG modeling is to construct CG maps from atomic structures. In this chapter, we review the recent development of a novel and systematic method for constructing CG representations of arbitrarily complex biomolecules, in order to preserve large-scale and functionally relevant essential dynamics (ED) at the CG level. In this ED-CG scheme, the essential dynamics can be characterized by principal component analysis (PCA) on a structural ensemble, or elastic network model (ENM) of a single atomic structure. Validation and applications of the method cover various biological systems, such as multi-domain proteins, protein complexes, and even biomolecular machines. The results demonstrate that the ED-CG method may serve as a very useful tool for identifying functional dynamics of large biomolecules at the CG level.

An optimal strategy for functional mapping of dynamic trait loci.

PubMed

Jin, Tianbo; Li, Jiahan; Guo, Ying; Zhou, Xiaojing; Yang, Runqing; Wu, Rongling

2010-02-01

As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

Nonlinear fluid dynamics of nanoscale hydration water layer

NASA Astrophysics Data System (ADS)

Jhe, Wonho; Kim, Bongsu; Kim, Qhwan; An, Sangmin

In nature, the hydration water layer (HWL) ubiquitously exists in ambient conditions or aqueous solutions, where water molecules are tightly bound to ions or hydrophilic surfaces. It plays an important role in various mechanisms such as biological processes, abiotic materials, colloidal interaction, and friction. The HWL, for example, can be easily formed between biomaterials since most biomaterials are covered by hydrophilic molecules such as lipid bilayers, and this HWL is expected to be significant to biological and physiological functions. Here (1) we present the general stress tensor of the hydration water layer. The hydration stress tensor provided the platform form for holistic understanding of the dynamic behaviors of the confined HWL including tapping and shear dynamics which are until now individually studied. And, (2) through fast shear velocity ( 1mm/s) experiments, the elastic turbulence caused by elastic property of the HWL is indirectly observed. Our results may contribute to a deeper study of systems where the HWL plays an important role such as biomolecules, colloidal particles, and the MEMS. This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korea government(MSIP) (2016R1A3B1908660).

Bioattractors: dynamical systems theory and the evolution of regulatory processes

PubMed Central

Jaeger, Johannes; Monk, Nick

2014-01-01

In this paper, we illustrate how dynamical systems theory can provide a unifying conceptual framework for evolution of biological regulatory systems. Our argument is that the genotype–phenotype map can be characterized by the phase portrait of the underlying regulatory process. The features of this portrait – such as attractors with associated basins and their bifurcations – define the regulatory and evolutionary potential of a system. We show how the geometric analysis of phase space connects Waddington's epigenetic landscape to recent computational approaches for the study of robustness and evolvability in network evolution. We discuss how the geometry of phase space determines the probability of possible phenotypic transitions. Finally, we demonstrate how the active, self-organizing role of the environment in phenotypic evolution can be understood in terms of dynamical systems concepts. This approach yields mechanistic explanations that go beyond insights based on the simulation of evolving regulatory networks alone. Its predictions can now be tested by studying specific, experimentally tractable regulatory systems using the tools of modern systems biology. A systematic exploration of such systems will enable us to understand better the nature and origin of the phenotypic variability, which provides the substrate for evolution by natural selection. PMID:24882812

Membrane Lipid Oscillation: An Emerging System of Molecular Dynamics in the Plant Membrane.

PubMed

Nakamura, Yuki

2018-03-01

Biological rhythm represents a major biological process of living organisms. However, rhythmic oscillation of membrane lipid content is poorly described in plants. The development of lipidomic technology has led to the illustration of precise molecular profiles of membrane lipids under various growth conditions. Compared with conventional lipid signaling, which produces unpredictable lipid changes in response to ever-changing environmental conditions, lipid oscillation generates a fairly predictable lipid profile, adding a new layer of biological function to the membrane system and possible cross-talk with the other chronobiological processes. This mini review covers recent studies elucidating membrane lipid oscillation in plants.

Using Network Dynamical Influence to Drive Consensus

NASA Astrophysics Data System (ADS)

Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.

2016-05-01

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Evolutionary dynamics of group interactions on structured populations: a review

PubMed Central

Perc, Matjaž; Gómez-Gardeñes, Jesús; Szolnoki, Attila; Floría, Luis M.; Moreno, Yamir

2013-01-01

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and non-living matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proved valuable for studying pattern formation, equilibrium selection and self-organization in evolutionary games. Here, we review recent advances in the study of evolutionary dynamics of group interactions on top of structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory. PMID:23303223

Separating intrinsic from extrinsic fluctuations in dynamic biological systems

PubMed Central

Paulsson, Johan

2011-01-01

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems. PMID:21730172

Separating intrinsic from extrinsic fluctuations in dynamic biological systems.

PubMed

Hilfinger, Andreas; Paulsson, Johan

2011-07-19

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems.

Coupling between the Dynamics of Water and Surfactants in Lyotropic Liquid Crystals

DOE PAGES

McDaniel, Jesse G.; Yethiraj, Arun

2017-04-26

Bilayers composed of lipid or surfactant molecules are central to biological membranes and lamellar lyotropic liquid crystalline (LLC) phases. Common to these systems are phases that exhibit either ordered or disordered packing of the hydrophobic tails. In this work, we study the impact of surfactant ordering, i.e., disordered L α and ordered L β LLC phases, on the dynamics of water and sodium ions in the lamellar phases of dicarboxylate gemini surfactants. We study the different phases at identical hydration levels by changing the length of the hydrophobic tails; surfactants with shorter tails form L α phases and those withmore » longer tails form L β phases. We find that the L α phases exhibit lower density and greater compressibility than the L β phases, with a hydration-dependent headgroup surface area. These structural differences significantly affect the relative dynamic properties of the phases, primarily the mobility of the surfactant molecules tangential to the bilayer surface, as well as the rates of water and ion diffusion. We find ~20–50% faster water diffusion in the L α phases compared to the L β phases, with the differences most pronounced at low hydration. This coupling between water dynamics and surfactant mobility is verified using additional simulations in which the surfactant tails are frozen. Our study indicates that gemini surfactant LLCs provide an important prototypical system for characterizing properties shared with more complex biological lipid membranes.« less

Dynamics of Active Layer Depth across Alaskan Tundra Ecosystems

NASA Astrophysics Data System (ADS)

Ma, C.; Zhang, X.; Song, X.; Xu, X.

2016-12-01

The thickness of the active layer, near-surface layer of Earth material above permafrost undergoing seasonal freezing and thawing, is of considerable importance in high-latitude environments because most physical, chemical, and biological processes in the permafrost region take place within it. The dynamics of active layer thickness (ALT) result from a combination of various factors including heat transfer, soil water content, soil texture, root density, stem density, moss layer thickness, organic layer thickness, etc. However, the magnitude and controls of ALT in the permafrost region remain uncertain. The purpose of this study is to improve our understanding of the dynamics of ALT across Alaskan tundra ecosystems and their controls at multiple scales, ranging from plots to entire Alaska. This study compiled a comprehensive dataset of ALT at site and regional scales across the Alaskan tundra ecosystems, and further analyzed ALT dynamics and their hierarchical controls. We found that air temperature played a predominant role on the seasonality of ALT, regulated by other physical and chemical factors including soil texture, moisture, and root density. The structural equation modeling (SEM) analysis confirmed the predominant role of physical controls (dominated by heat and soil properties), followed by chemical and biological factors. Then a simple empirical model was developed to reconstruct the ALT across the Alaska. The comparisons against field observational data show that the method used in this study is robust; the reconstructed time-series ALT across Alaska provides a valuable dataset source for understanding ALT and validating large-scale ecosystem models.

In Vitro Experimental Model for the Long-Term Analysis of Cellular Dynamics During Bronchial Tree Development from Lung Epithelial Cells

PubMed Central

Maruta, Naomichi; Marumoto, Moegi

2017-01-01

Lung branching morphogenesis has been studied for decades, but the underlying developmental mechanisms are still not fully understood. Cellular movements dynamically change during the branching process, but it is difficult to observe long-term cellular dynamics by in vivo or tissue culture experiments. Therefore, developing an in vitro experimental model of bronchial tree would provide an essential tool for developmental biology, pathology, and systems biology. In this study, we succeeded in reconstructing a bronchial tree in vitro by using primary human bronchial epithelial cells. A high concentration gradient of bronchial epithelial cells was required for branching initiation, whereas homogeneously distributed endothelial cells induced the formation of successive branches. Subsequently, the branches grew in size to the order of millimeter. The developed model contains only two types of cells and it facilitates the analysis of lung branching morphogenesis. By taking advantage of our experimental model, we carried out long-term time-lapse observations, which revealed self-assembly, collective migration with leader cells, rotational motion, and spiral motion of epithelial cells in each developmental event. Mathematical simulation was also carried out to analyze the self-assembly process and it revealed simple rules that govern cellular dynamics. Our experimental model has provided many new insights into lung development and it has the potential to accelerate the study of developmental mechanisms, pattern formation, left–right asymmetry, and disease pathogenesis of the human lung. PMID:28471293

The Integrin Receptor in Biologically Relevant Bilayers: Insights from Molecular Dynamics Simulations.

PubMed

Kalli, Antreas C; Rog, Tomasz; Vattulainen, Ilpo; Campbell, Iain D; Sansom, Mark S P

2017-08-01

Integrins are heterodimeric (αβ) cell surface receptors that are potential therapeutic targets for a number of diseases. Despite the existence of structural data for all parts of integrins, the structure of the complete integrin receptor is still not available. We have used available structural data to construct a model of the complete integrin receptor in complex with talin F2-F3 domain. It has been shown that the interactions of integrins with their lipid environment are crucial for their function but details of the integrin/lipid interactions remain elusive. In this study an integrin/talin complex was inserted in biologically relevant bilayers that resemble the cell plasma membrane containing zwitterionic and charged phospholipids, cholesterol and sphingolipids to study the dynamics of the integrin receptor and its effect on bilayer structure and dynamics. The results of this study demonstrate the dynamic nature of the integrin receptor and suggest that the presence of the integrin receptor alters the lipid organization between the two leaflets of the bilayer. In particular, our results suggest elevated density of cholesterol and of phosphatidylserine lipids around the integrin/talin complex and a slowing down of lipids in an annulus of ~30 Å around the protein due to interactions between the lipids and the integrin/talin F2-F3 complex. This may in part regulate the interactions of integrins with other related proteins or integrin clustering thus facilitating signal transduction across cell membranes.

Method and apparatus to image biological interactions in plants

DOEpatents

Weisenberger, Andrew; Bonito, Gregory M.; Reid, Chantal D.; Smith, Mark Frederick

2015-12-22

A method to dynamically image the actual translocation of molecular compounds of interest in a plant root, root system, and rhizosphere without disturbing the root or the soil. The technique makes use of radioactive isotopes as tracers to label molecules of interest and to image their distribution in the plant and/or soil. The method allows for the study and imaging of various biological and biochemical interactions in the rhizosphere of a plant, including, but not limited to, mycorrhizal associations in such regions.

The Ultrasensitivity of Living Polymers

NASA Astrophysics Data System (ADS)

O'Shaughnessy, Ben; Vavylonis, Dimitrios

2003-03-01

Synthetic and biological living polymers are self-assembling chains whose chain length distributions (CLDs) are dynamic. We show these dynamics are ultrasensitive: Even a small perturbation (e.g., temperature jump) nonlinearly distorts the CLD, eliminating or massively augmenting short chains. The origin is fast relaxation of mass variables (mean chain length, monomer concentration) which perturbs CLD shape variables before these can relax via slow chain growth rate fluctuations. Viscosity relaxation predictions agree with experiments on the best-studied synthetic system, α-methylstyrene.

Application of Non-Kolmogorovian Probability and Quantum Adaptive Dynamics to Unconscious Inference in Visual Perception Process

NASA Astrophysics Data System (ADS)

Accardi, Luigi; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-07-01

Recently a novel quantum information formalism — quantum adaptive dynamics — was developed and applied to modelling of information processing by bio-systems including cognitive phenomena: from molecular biology (glucose-lactose metabolism for E.coli bacteria, epigenetic evolution) to cognition, psychology. From the foundational point of view quantum adaptive dynamics describes mutual adapting of the information states of two interacting systems (physical or biological) as well as adapting of co-observations performed by the systems. In this paper we apply this formalism to model unconscious inference: the process of transition from sensation to perception. The paper combines theory and experiment. Statistical data collected in an experimental study on recognition of a particular ambiguous figure, the Schröder stairs, support the viability of the quantum(-like) model of unconscious inference including modelling of biases generated by rotation-contexts. From the probabilistic point of view, we study (for concrete experimental data) the problem of contextuality of probability, its dependence on experimental contexts. Mathematically contextuality leads to non-Komogorovness: probability distributions generated by various rotation contexts cannot be treated in the Kolmogorovian framework. At the same time they can be embedded in a “big Kolmogorov space” as conditional probabilities. However, such a Kolmogorov space has too complex structure and the operational quantum formalism in the form of quantum adaptive dynamics simplifies the modelling essentially.

Photoinduced nanobubble-driven superfast diffusion of nanoparticles imaged by 4D electron microscopy

PubMed Central

Fu, Xuewen; Chen, Bin; Tang, Jau; Zewail, Ahmed H.

2017-01-01

Dynamics of active or propulsive Brownian particles in nonequilibrium status have recently attracted great interest in many fields including artificial micro/nanoscopic motors and biological entities. Understanding of their dynamics can provide insight into the statistical properties of physical and biological systems far from equilibrium. We report the translational dynamics of photon-activated gold nanoparticles (NPs) in water imaged by liquid-cell four-dimensional electron microscopy (4D-EM) with high spatiotemporal resolution. Under excitation of femtosecond laser pulses, we observed that those NPs exhibit superfast diffusive translation with a diffusion constant four to five orders of magnitude greater than that in the absence of laser excitation. The measured diffusion constant follows a power-law dependence on the laser fluence and a linear increase with the laser repetition rate, respectively. This superfast diffusion of the NPs is induced by a strong random driving force arising from the photoinduced steam nanobubbles (NBs) near the NP surface. In contrast, the NPs exhibit a superfast ballistic translation at a short time scale down to nanoseconds. Combining with a physical model simulation, this study reveals a photoinduced NB propulsion mechanism for propulsive motion, providing physical insights into better design of light-activated artificial micro/nanomotors. The liquid-cell 4D-EM also provides the potential of studying other numerical dynamical behaviors in their native environments. PMID:28875170

Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry.

PubMed

Pandini, Alessandro; Fraccalvieri, Domenico; Bonati, Laura

2013-01-01

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry.

Bayesian parameter estimation for nonlinear modelling of biological pathways.

PubMed

Ghasemi, Omid; Lindsey, Merry L; Yang, Tianyi; Nguyen, Nguyen; Huang, Yufei; Jin, Yu-Fang

2011-01-01

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.

Microsecond protein dynamics observed at the single-molecule level

NASA Astrophysics Data System (ADS)

Otosu, Takuhiro; Ishii, Kunihiko; Tahara, Tahei

2015-07-01

How polypeptide chains acquire specific conformations to realize unique biological functions is a central problem of protein science. Single-molecule spectroscopy, combined with fluorescence resonance energy transfer, is utilized to study the conformational heterogeneity and the state-to-state transition dynamics of proteins on the submillisecond to second timescales. However, observation of the dynamics on the microsecond timescale is still very challenging. This timescale is important because the elementary processes of protein dynamics take place and direct comparison between experiment and simulation is possible. Here we report a new single-molecule technique to reveal the microsecond structural dynamics of proteins through correlation of the fluorescence lifetime. This method, two-dimensional fluorescence lifetime correlation spectroscopy, is applied to clarify the conformational dynamics of cytochrome c. Three conformational ensembles and the microsecond transitions in each ensemble are indicated from the correlation signal, demonstrating the importance of quantifying microsecond dynamics of proteins on the folding free energy landscape.

Microsecond protein dynamics observed at the single-molecule level

PubMed Central

Otosu, Takuhiro; Ishii, Kunihiko; Tahara, Tahei

2015-01-01

How polypeptide chains acquire specific conformations to realize unique biological functions is a central problem of protein science. Single-molecule spectroscopy, combined with fluorescence resonance energy transfer, is utilized to study the conformational heterogeneity and the state-to-state transition dynamics of proteins on the submillisecond to second timescales. However, observation of the dynamics on the microsecond timescale is still very challenging. This timescale is important because the elementary processes of protein dynamics take place and direct comparison between experiment and simulation is possible. Here we report a new single-molecule technique to reveal the microsecond structural dynamics of proteins through correlation of the fluorescence lifetime. This method, two-dimensional fluorescence lifetime correlation spectroscopy, is applied to clarify the conformational dynamics of cytochrome c. Three conformational ensembles and the microsecond transitions in each ensemble are indicated from the correlation signal, demonstrating the importance of quantifying microsecond dynamics of proteins on the folding free energy landscape. PMID:26151767

Comparative Investigation of Normal Modes and Molecular Dynamics of Hepatitis C NS5B Protein

NASA Astrophysics Data System (ADS)

Asafi, M. S.; Yildirim, A.; Tekpinar, M.

2016-04-01

Understanding dynamics of proteins has many practical implications in terms of finding a cure for many protein related diseases. Normal mode analysis and molecular dynamics methods are widely used physics-based computational methods for investigating dynamics of proteins. In this work, we studied dynamics of Hepatitis C NS5B protein with molecular dynamics and normal mode analysis. Principal components obtained from a 100 nanoseconds molecular dynamics simulation show good overlaps with normal modes calculated with a coarse-grained elastic network model. Coarse-grained normal mode analysis takes at least an order of magnitude shorter time. Encouraged by this good overlaps and short computation times, we analyzed further low frequency normal modes of Hepatitis C NS5B. Motion directions and average spatial fluctuations have been analyzed in detail. Finally, biological implications of these motions in drug design efforts against Hepatitis C infections have been elaborated.

Evolutionary optimization with data collocation for reverse engineering of biological networks.

PubMed

Tsai, Kuan-Yao; Wang, Feng-Sheng

2005-04-01

Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

Biology-inspired AMO physics

NASA Astrophysics Data System (ADS)

Mathur, Deepak

2015-01-01

This Topical Review presents an overview of increasingly robust interconnects that are being established between atomic, molecular and optical (AMO) physics and the life sciences. AMO physics, outgrowing its historical role as a facilitator—a provider of optical methodologies, for instance—now seeks to partner biology in its quest to link systems-level descriptions of biological entities to insights based on molecular processes. Of course, perspectives differ when AMO physicists and biologists consider various processes. For instance, while AMO physicists link molecular properties and dynamics to potential energy surfaces, these have to give way to energy landscapes in considerations of protein dynamics. But there are similarities also: tunnelling and non-adiabatic transitions occur both in protein dynamics and in molecular dynamics. We bring to the fore some such differences and similarities; we consider imaging techniques based on AMO concepts, like 4D fluorescence microscopy which allows access to the dynamics of cellular processes, multiphoton microscopy which offers a built-in confocality, and microscopy with femtosecond laser beams to saturate the suppression of fluorescence in spatially controlled fashion so as to circumvent the diffraction limit. Beyond imaging, AMO physics contributes with optical traps that probe the mechanical and dynamical properties of single ‘live’ cells, highlighting differences between healthy and diseased cells. Trap methodologies have also begun to probe the dynamics governing of neural stem cells adhering to each other to form neurospheres and, with squeezed light to probe sub-diffusive motion of yeast cells. Strong field science contributes not only by providing a source of energetic electrons and γ-rays via laser-plasma accelerations schemes, but also via filamentation and supercontinuum generation, enabling mainstream collision physics into play in diverse processes like DNA damage induced by low-energy collisions to invoking dissociative attachment in quantification of stress levels in humans. The prognosis is extremely good for more intense interaction of AMO physics and biology; by way of future predictions attention is drawn to only two of very many opportunities for such interactions: application of attosecond techniques and tunnelling experiments to biological problems.

Systems biology of cellular membranes: a convergence with biophysics.

PubMed

Chabanon, Morgan; Stachowiak, Jeanne C; Rangamani, Padmini

2017-09-01

Systems biology and systems medicine have played an important role in the last two decades in shaping our understanding of biological processes. While systems biology is synonymous with network maps and '-omics' approaches, it is not often associated with mechanical processes. Here, we make the case for considering the mechanical and geometrical aspects of biological membranes as a key step in pushing the frontiers of systems biology of cellular membranes forward. We begin by introducing the basic components of cellular membranes, and highlight their dynamical aspects. We then survey the functions of the plasma membrane and the endomembrane system in signaling, and discuss the role and origin of membrane curvature in these diverse cellular processes. We further give an overview of the experimental and modeling approaches to study membrane phenomena. We close with a perspective on the converging futures of systems biology and membrane biophysics, invoking the need to include physical variables such as location and geometry in the study of cellular membranes. WIREs Syst Biol Med 2017, 9:e1386. doi: 10.1002/wsbm.1386 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

Frequency modulation atomic force microscopy: a dynamic measurement technique for biological systems

NASA Astrophysics Data System (ADS)

Higgins, Michael J.; Riener, Christian K.; Uchihashi, Takayuki; Sader, John E.; McKendry, Rachel; Jarvis, Suzanne P.

2005-03-01

Frequency modulation atomic force microscopy (FM-AFM) has been modified to operate in a liquid environment within an atomic force microscope specifically designed for investigating biological samples. We demonstrate the applicability of FM-AFM to biological samples using the spectroscopy mode to measure the unbinding forces of a single receptor-ligand (biotin-avidin) interaction. We show that quantitative adhesion force measurements can only be obtained provided certain modifications are made to the existing theory, which is used to convert the detected frequency shifts to an interaction force. Quantitative force measurements revealed that the unbinding forces for the biotin-avidin interaction were greater than those reported in previous studies. This finding was due to the use of high average tip velocities, which were calculated to be two orders of magnitude greater than those typically used in unbinding receptor-ligand experiments. This study therefore highlights the potential use of FM-AFM to study a range of biological systems, including living cells and/or single biomolecule interactions.

Generation of radicals in hard biological tissues under the action of laser radiation

NASA Astrophysics Data System (ADS)

Sviridov, Alexander P.; Bagratashvili, Victor N.; Sobol, Emil N.; Omelchenko, Alexander I.; Lunina, Elena V.; Zhitnev, Yurii N.; Markaryan, Galina L.; Lunin, Valerii V.

2002-07-01

The formation of radicals upon UV and IR laser irradiation of some biological tissues and their components was studied by the EPR technique. The radical decay kinetics in body tissue specimens after their irradiation with UV light were described by various models. By the spin trapping technique, it was shown that radicals were not produced during IR laser irradiation of cartilaginous tissue. A change in optical absorption spectra and the dynamics of optical density of cartilaginous tissue, fish scale, and a collagen film under exposure to laser radiation in an air, oxygen, and nitrogen atmosphere was studied.

Some Physical, Chemical, and Biological Parameters of Samples of Scleractinium Coral Aquaculture Skeleton Used for Reconstruction/Engineering of the Bone Tissue.

PubMed

Popov, A A; Sergeeva, N S; Britaev, T A; Komlev, V S; Sviridova, I K; Kirsanova, V A; Akhmedova, S A; Dgebuadze, P Yu; Teterina, A Yu; Kuvshinova, E A; Schanskii, Ya D

2015-08-01

Physical and chemical (phase and chemical composition, dynamics of resorption, and strength properties), and biological (cytological compatibility and scaffold properties of the surface) properties of samples of scleractinium coral skeletons from aquacultures of three types and corresponding samples of natural coral skeletons (Pocillopora verrucosa, Acropora formosa, and Acropora nobilis) were studied. Samples of scleractinium coral aquaculture skeleton of A. nobilis, A. formosa, and P. verrucosa met the requirements (all study parameters) to materials for osteoplasty and 3D-scaffolds for engineering of bone tissue.

Linking dynamics of the inhibitory network to the input structure

PubMed Central

Komarov, Maxim

2017-01-01

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives. PMID:27650865

COULD ETHINYL ESTRADIOL AFFECT THE POPULATION BIOLOGY OF CUNNER, TAUTOGOLABRUS ADSPERSUS

EPA Science Inventory

Endocrine disrupting chemicals in the environment may disturb the population dynamics of wildlife by affecting reproductive output and embryonic development of organisms. This study used a population model to evaluate whether ethinyl estradiol (EE2 could affect cunner Tautogolabr...

Dynamical properties of water in living cells

NASA Astrophysics Data System (ADS)

Piazza, Irina; Cupane, Antonio; Barbier, Emmanuel L.; Rome, Claire; Collomb, Nora; Ollivier, Jacques; Gonzalez, Miguel A.; Natali, Francesca

2018-02-01

With the aim of studying the effect of water dynamics on the properties of biological systems, in this paper, we present a quasi-elastic neutron scattering study on three different types of living cells, differing both in their morphological and tumor properties. The measured scattering signal, which essentially originates from hydrogen atoms present in the investigated systems, has been analyzed using a global fitting strategy using an optimized theoretical model that considers various classes of hydrogen atoms and allows disentangling diffusive and rotational motions. The approach has been carefully validated by checking the reliability of the calculation of parameters and their 99% confidence intervals. We demonstrate that quasi-elastic neutron scattering is a suitable experimental technique to characterize the dynamics of intracellular water in the angstrom/picosecond space/time scale and to investigate the effect of water dynamics on cellular biodiversity.

Transforming the Social Practices of Learning with Representations: A Study of Disciplinary Discourse

ERIC Educational Resources Information Center

Nichols, Kim; Hanan, Jim; Ranasinghe, Muditha

2013-01-01

This study used an interactive dynamic simulation of action potential to explore social practices of learning among first year undergraduate biology students. It aimed to create a learning environment that fosters knowledge building discourse through working with multiple concept-specific representations. Three hundred and eighty-nine students and…

A STUDY OF SMALL GROUP DYNAMICS AND PRODUCTIVITY IN THE BSCS LABORATORY BLOCK PROGRAM.

ERIC Educational Resources Information Center

HURD, PAUL DEHART; ROWE, MARY BUDD

THE RELATIONSHIP BETWEEN SMALL GROUP COMPATIBILITY AND ACHIEVEMENT IN THE BIOLOGICAL SCIENCE CURRICULUM STUDY LABORATORY BLOCK PROGRAM WAS TESTED. STUDENTS IN 14 CLASSES FROM FOUR HIGH SCHOOLS WERE ASSIGNED TO FOUR-MEMBER LABORATORY GROUPS CLASSIFIED AS COMPATIBLE OR INCOMPATIBLE. GROUP CLASSIFICATION WAS VALIDATED BY OBSERVERS WHO WERE NOT AWARE…

Knowledge discovery and system biology in molecular medicine: an application on neurodegenerative diseases.

PubMed

Fattore, Matteo; Arrigo, Patrizio

2005-01-01

The possibility to study an organism in terms of system theory has been proposed in the past, but only the advancement of molecular biology techniques allow us to investigate the dynamical properties of a biological system in a more quantitative and rational way than before . These new techniques can gave only the basic level view of an organisms functionality. The comprehension of its dynamical behaviour depends on the possibility to perform a multiple level analysis. Functional genomics has stimulated the interest in the investigation the dynamical behaviour of an organism as a whole. These activities are commonly known as System Biology, and its interests ranges from molecules to organs. One of the more promising applications is the 'disease modeling'. The use of experimental models is a common procedure in pharmacological and clinical researches; today this approach is supported by 'in silico' predictive methods. This investigation can be improved by a combination of experimental and computational tools. The Machine Learning (ML) tools are able to process different heterogeneous data sources, taking into account this peculiarity, they could be fruitfully applied to support a multilevel data processing (molecular, cellular and morphological) that is the prerequisite for the formal model design; these techniques can allow us to extract the knowledge for mathematical model development. The aim of our work is the development and implementation of a system that combines ML and dynamical models simulations. The program is addressed to the virtual analysis of the pathways involved in neurodegenerative diseases. These pathologies are multifactorial diseases and the relevance of the different factors has not yet been well elucidated. This is a very complex task; in order to test the integrative approach our program has been limited to the analysis of the effects of a specific protein, the Cyclin dependent kinase 5 (CDK5) which relies on the induction of neuronal apoptosis. The system has a modular structure centred on a textual knowledge discovery approach. The text mining is the only way to enhance the capability to extract ,from multiple data sources, the information required for the dynamical simulator. The user may access the publically available modules through the following site: http://biocomp.ge.ismac.cnr.it.

Biological measurement beyond the quantum limit

NASA Astrophysics Data System (ADS)

Taylor, Michael; Janousek, Jiri; Daria, Vincent; Knittel, Joachim; Hage, Boris; Bachor, Hans; Bowen, Warwick

2013-05-01

Biology is an important frontier for quantum metrology, with quantum enhanced sensitivity allowing optical intensities to be lowered, and a consequent reduction in specimen damage and photochemical intrusion upon biological processes. Here we demonstrate the first biological measurement with precision surpassing the quantum noise limit. Naturally occurring lipid granules within living yeast cells were tracked in real time with sensitivity surpassing the quantum noise limit by 42% as they diffuse through the cytoplasm and interact with embedded polymer networks. This allowed dynamic mechanical properties of the cytoplasm to be determined with a 64% higher measurement rate than possible classically. To enable this, a new microscopy system was developed which is compatible with squeezed light, and which utilized a novel optical lock-in technique to allow quantum enhancement down to 10 Hz. This method is widely applicable, extending the reach of quantum enhanced measurement to many dynamic biological processes.

Dynamic features of carboxy cytoglobin distal mutants investigated by molecular dynamics simulations.

PubMed

Zhao, Cong; Du, Weihong

2016-04-01

Cytoglobin (Cgb) is a member of hemoprotein family with roles in NO metabolism, fibrosis, and tumourigenesis. Similar to other hemoproteins, Cgb structure and functions are markedly influenced by distal key residues. The sixth ligand His(81) (E7) is crucial to exogenous ligand binding, heme pocket conformation, and physiological roles of this protein. However, the effects of other key residues on heme pocket and protein biological functions are not well known. In this work, a molecular dynamics (MD) simulation study of two single mutants in CO-ligated Cgb (L46FCgbCO and L46VCgbCO) and two double mutants (L46FH81QCgbCO and L46VH81QCgbCO) was conducted to explore the effects of the key distal residues Leu(46)(B10) and His(81)(E7) on Cgb structure and functions. Results indicated that the distal mutation of B10 and E7 affected CgbCO dynamic properties on loop region fluctuation, internal cavity rearrangement, and heme motion. The distal conformation change was reflected by the distal key residues Gln(62) (CD3) and Arg(84)(E10). The hydrogen bond between heme propionates with CD3 or E10 residues were evidently influenced by B10/E7 mutation. Furthermore, heme pocket rearrangement was also observed based on the distal pocket volume and occurrence rate of inner cavities. The mutual effects of B10 and E7 residues on protein conformational rearrangement and other dynamic features were expressed in current MD studies of CgbCO and its distal mutants, suggesting their crucial role in heme pocket stabilization, ligand binding, and Cgb biological functions. The mutation of distal B10 and E7 residues affects the dynamic features of carboxy cytoglobin.

Dynamic Modularity of Host Protein Interaction Networks in Salmonella Typhi Infection

PubMed Central

Dhal, Paltu Kumar; Barman, Ranjan Kumar; Saha, Sudipto; Das, Santasabuj

2014-01-01

Background Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood. Methods We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified. Results We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever. Conclusions Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi. PMID:25144185

Inherit the policy: A sociocultural approach to understanding evolutionary biology policy in South Carolina

NASA Astrophysics Data System (ADS)

Moore, Gregory D.

South Carolina biology Indicator 5.6 calls for students to "Summarize ways that scientists use data from a variety of sources to investigate and critically analyze aspects of evolutionary theory" (South Carolina Department of Education, 2006). Levinson and Sutton (2001) offered a sociocultural approach to policy that considers cultural and historical influences at all levels of the policy process. Lipsky (1980/2010) and others have identified teachers as de facto policy makers, exercising broad discretion in the execution of their work. This study looks to Ajzen's Theory of Planned Behavior as an initial framework to inform how evolutionary biology policy in South Carolina is conceptualized and understood at different levels of the policy process. The results of this study indicate that actors in the state's evolutionary biology policy process draw upon a myriad of Discourses (Gee, 1999/2005). These Discourses shape cultural dynamics and the agency of the policy actors as they navigate conflicting messages between testing mandates and evolutionary biology policy. There indeed exist gaps between how evolutionary biology policy in South Carolina is conceptualized and understood at the different levels of the policy process. Evidence from this study suggests that appropriation-level policy actors must be brought into the Discourse related to the critical analysis of evolutionary biology and academic freedom legislation must be enacted if South Carolina biology Indicator 5.6 is to realize practical significance in educational policy.

Analog Computer Laboratory with Biological Examples.

ERIC Educational Resources Information Center

Strebel, Donald E.

1979-01-01

The use of biological examples in teaching applications of the analog computer is discussed and several examples from mathematical ecology, enzyme kinetics, and tracer dynamics are described. (Author/GA)

Geometric effects in microfluidics on heterogeneous cell stress using an Eulerian-Lagrangian approach.

PubMed

Warren, K M; Mpagazehe, J N; LeDuc, P R; Higgs, C F

2016-02-07

The response of individual cells at the micro-scale in cell mechanics is important in understanding how they are affected by changing environments. To control cell stresses, microfluidics can be implemented since there is tremendous control over the geometry of the devices. Designing microfluidic devices to induce and manipulate stress levels on biological cells can be aided by computational modeling approaches. Such approaches serve as an efficient precursor to fabricating various microfluidic geometries that induce predictable levels of stress on biological cells, based on their mechanical properties. Here, a three-dimensional, multiphase computational fluid dynamics (CFD) modeling approach was implemented for soft biological materials. The computational model incorporates the physics of the particle dynamics, fluid dynamics and solid mechanics, which allows us to study how stresses affect the cells. By using an Eulerian-Lagrangian approach to treat the fluid domain as a continuum in the microfluidics, we are conducting studies of the cells' movement and the stresses applied to the cell. As a result of our studies, we were able to determine that a channel with periodically alternating columns of obstacles was capable of stressing cells at the highest rate, and that microfluidic systems can be engineered to impose heterogenous cell stresses through geometric configuring. We found that when using controlled geometries of the microfluidics channels with staggered obstructions, we could increase the maximum cell stress by nearly 200 times over cells flowing through microfluidic channels with no obstructions. Incorporating computational modeling in the design of microfluidic configurations for controllable cell stressing could help in the design of microfludic devices for stressing cells such as cell homogenizers.

Clinical, biological, and microbiological pattern associated with ventriculostomy-related infection: a retrospective longitudinal study.

PubMed

Mounier, Roman; Lobo, David; Cook, Fabrice; Fratani, Alexandre; Attias, Arie; Martin, Mathieu; Chedevergne, Karin; Bardon, Jean; Tazi, Sanaa; Nebbad, Biba; Bloc, Sébastien; Plaud, Benoît; Dhonneur, Gilles

2015-12-01

Our aim was to describe the pattern of ventriculostomy-related infection (VRI) development using a dynamic approach. Retrospective longitudinal study. We analyzed the files of 449 neurosurgical patients who underwent placement of external ventricular drain (EVD). During the study period, CSF sampling was performed on a daily base setting. VRI was defined as a positive CSF culture resulting in antibiotic treatment. For VRI patients, we arbitrary defined day 0 (D0) as the day antibiotic treatment was started. In these patients, we compared dynamic changes in clinical and biological parameters at four pre-determined time points: (D-4, D-3, D-2, D-1) with those of D0. For all CSF-positive cultures, we compared CSF biochemical markers' evolution pattern between VRI patients and the others, considered as a control cohort. Thirty-two suffered from VRI. Peripheral white blood cell count did not differ between D-4-D0. Median body temperature, CSF cell count, median Glasgow Coma Scale, CSF protein, and glucose concentrations were significantly different between D-4, D-3, D-2, and D0. At D0, 100 % of CSF samples yielded organisms in culture. The physician caring for the patient decided to treat VRI based upon positive CSF culture in only 28 % (9/32) of cases. In the control cohort, CSF markers' profile trends to normalize, while it worsens in the VRI patients. We showed that clinical symptoms and biological abnormalities of VRI evolved over time. Our data suggest that VRI decision to treat relies upon a bundle of evidence, including dynamic changes in CSF laboratory exams combined with microbiological analysis.

Characterization of dynamic physiology of the bladder by optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Zhijia; Keng, Kerri; Pan, Rubin; Ren, Hugang; Du, Congwu; Kim, Jason; Pan, Yingtian

2012-03-01

Because of its high spatial resolution and noninvasive imaging capabilities, optical coherence tomography has been used to characterize the morphological details of various biological tissues including urinary bladder and to diagnose their alternations (e.g., cancers). In addition to static morphology, the dynamic features of tissue morphology can provide important information that can be used to diagnose the physiological and functional characteristics of biological tissues. Here, we present the imaging studies based on optical coherence tomography to characterize motion related physiology and functions of rat bladder detrusor muscles and compared the results with traditional biomechanical measurements. Our results suggest that optical coherence tomography is capable of providing quantitative evaluation of contractile functions of intact bladder (without removing bladder epithelium and connective tissue), which is potentially of more clinical relevance for future clinical diagnosis - if incorporated with cystoscopic optical coherence tomography.

Optimization of the molecular dynamics method for simulations of DNA and ion transport through biological nanopores.

PubMed

Wells, David B; Bhattacharya, Swati; Carr, Rogan; Maffeo, Christopher; Ho, Anthony; Comer, Jeffrey; Aksimentiev, Aleksei

2012-01-01

Molecular dynamics (MD) simulations have become a standard method for the rational design and interpretation of experimental studies of DNA translocation through nanopores. The MD method, however, offers a multitude of algorithms, parameters, and other protocol choices that can affect the accuracy of the resulting data as well as computational efficiency. In this chapter, we examine the most popular choices offered by the MD method, seeking an optimal set of parameters that enable the most computationally efficient and accurate simulations of DNA and ion transport through biological nanopores. In particular, we examine the influence of short-range cutoff, integration timestep and force field parameters on the temperature and concentration dependence of bulk ion conductivity, ion pairing, ion solvation energy, DNA structure, DNA-ion interactions, and the ionic current through a nanopore.

Garage Demos: Using Physical Models to Illustrate Dynamic Aspects of Microscopic Biological Processes

PubMed Central

Aguilar-Roca, Nancy

2009-01-01

Colorful PowerPoint presentations with detailed drawings, micrographs, and short animations have become the standard format for illustrating the fundamental features of cell biology in large introductory classes. In this essay, we describe a low-tech tool that can be included in a standard lecture to help students visualize, understand, and remember the dynamic aspects of microscopic cell biological processes. This approach involves use of common objects, including pipe insulation and a garden hose, to illustrate basic processes such as protein folding and cloning, hence the appellation “garage demos.” The demonstrations are short, minimizing displacement of course content, easy to make, and provide an avenue for increasing student–faculty interaction in a large lecture hall. Student feedback over the past 4 years has been overwhelmingly positive. In an anonymous postclass survey in 2007, 90% of the respondents rated garage demos as having been very or somewhat helpful for understanding course concepts. Direct measurements of learning gains on specific concepts illustrated by garage demos are the focus of an ongoing study. PMID:19487500

Using computer algebra and SMT solvers in algebraic biology

NASA Astrophysics Data System (ADS)

Pineda Osorio, Mateo

2014-05-01

Biologic processes are represented as Boolean networks, in a discrete time. The dynamics within these networks are approached with the help of SMT Solvers and the use of computer algebra. Software such as Maple and Z3 was used in this case. The number of stationary states for each network was calculated. The network studied here corresponds to the immune system under the effects of drastic mood changes. Mood is considered as a Boolean variable that affects the entire dynamics of the immune system, changing the Boolean satisfiability and the number of stationary states of the immune network. Results obtained show Z3's great potential as a SMT Solver. Some of these results were verified in Maple, even though it showed not to be as suitable for the problem approach. The solving code was constructed using Z3-Python and Z3-SMT-LiB. Results obtained are important in biology systems and are expected to help in the design of immune therapies. As a future line of research, more complex Boolean network representations of the immune system as well as the whole psychological apparatus are suggested.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

The dynamical analysis of modified two-compartment neuron model and FPGA implementation

NASA Astrophysics Data System (ADS)

Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

2017-10-01

The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Nanoparticle PEBBLE sensors in live cells and in vivo

PubMed Central

Smith, Ron

2009-01-01

Nanoparticle sensors have been developed for imaging and dynamic monitoring, in live cells and in vivo, of the molecular or ionic components, constructs, forces and dynamics, all in real time, during biological/chemical/physical processes. With their biocompatible small size and inert matrix, nanoparticle sensors have been successfully applied for non-invasive real-time measurements of analytes and fields in cells and rodents, with spatial, temporal, physical and chemical resolution. This review describes the diverse designs of nanoparticle sensors for ions and small molecules, physical fields and biological features, as well as the characterization, properties, and applications of these nanosensors to in vitro and in vivo measurements. Their floating as well as localization ability in biological media is captured by the acronym PEBBLE: photonic explorer for bioanalysis with biologically localized embedding. PMID:20098636

Using Petri Net Tools to Study Properties and Dynamics of Biological Systems

PubMed Central

Peleg, Mor; Rubin, Daniel; Altman, Russ B.

2005-01-01

Petri Nets (PNs) and their extensions are promising methods for modeling and simulating biological systems. We surveyed PN formalisms and tools and compared them based on their mathematical capabilities as well as by their appropriateness to represent typical biological processes. We measured the ability of these tools to model specific features of biological systems and answer a set of biological questions that we defined. We found that different tools are required to provide all capabilities that we assessed. We created software to translate a generic PN model into most of the formalisms and tools discussed. We have also made available three models and suggest that a library of such models would catalyze progress in qualitative modeling via PNs. Development and wide adoption of common formats would enable researchers to share models and use different tools to analyze them without the need to convert to proprietary formats. PMID:15561791

Bio-inspired nano-sensor-enhanced CNN visual computer.

PubMed

Porod, Wolfgang; Werblin, Frank; Chua, Leon O; Roska, Tamas; Rodriguez-Vazquez, Angel; Roska, Botond; Fay, Patrick; Bernstein, Gary H; Huang, Yih-Fang; Csurgay, Arpad I

2004-05-01

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.

Networking Omic Data to Envisage Systems Biological Regulation.

PubMed

Kalapanulak, Saowalak; Saithong, Treenut; Thammarongtham, Chinae

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network. In the drive to decipher such a code of lives, a spectrum of technologies has continuously been developed in the post-genomic era. With current advances, high-throughput sequencing technologies are tremendously powerful for facilitating genomics and systems biology studies in the attempt to understand system regulation inside the cells. The ability to explore relevant regulatory components which infer transcriptional and signaling regulation, driving core cellular processes, is thus enhanced. This chapter reviews high-throughput sequencing technologies, including second and third generation sequencing technologies, which support the investigation of genomics and transcriptomics data. Utilization of this high-throughput data to form the virtual network of systems regulation is explained, particularly transcriptional regulatory networks. Analysis of the resulting regulatory networks could lead to an understanding of cellular systems regulation at the mechanistic and dynamics levels. The great contribution of the biological networking approach to envisage systems regulation is finally demonstrated by a broad range of examples.

Dynamic quantitative phase images of pond life, insect wings, and in vitro cell cultures

NASA Astrophysics Data System (ADS)

Creath, Katherine

2010-08-01

This paper presents images and data of live biological samples taken with a novel Linnik interference microscope. The specially designed optical system enables instantaneous and 3D video measurements of dynamic motions within and among live cells without the need for contrast agents. This "label-free", vibration insensitive imaging system enables measurement of biological objects in reflection using harmless light levels with current magnifications of 10X (NA 0.3) and 20X (NA 0.5) and wavelengths of 660 nm and 785 nm over fields of view from several hundred microns up to a millimeter. At the core of the instrument is a phasemeasurement camera (PMC) enabling simultaneous measurement of multiple interference patterns utilizing a pixelated phase mask taking advantage of the polarization properties of light. Utilizing this technology enables the creation of phase image movies in real time at video rates so that dynamic motions and volumetric changes can be tracked. Objects are placed on a reflective surface in liquid under a coverslip. Phase values are converted to optical thickness data enabling volumetric, motion and morphological studies. Data from a number of different mud puddle organisms such as paramecium, flagellates and rotifers will be presented, as will measurements of flying ant wings and cultures of human breast cancer cells. These data highlight examples of monitoring different biological processes and motions. The live presentation features 4D phase movies of these examples.

Dynamics driving function: new insights from electron transferring flavoproteins and partner complexes.

PubMed

Toogood, Helen S; Leys, David; Scrutton, Nigel S

2007-11-01

Electron transferring flavoproteins (ETFs) are soluble heterodimeric FAD-containing proteins that function primarily as soluble electron carriers between various flavoprotein dehydrogenases. ETF is positioned at a key metabolic branch point, responsible for transferring electrons from up to 10 primary dehydrogenases to the membrane-bound respiratory chain. Clinical mutations of ETF result in the often fatal disease glutaric aciduria type II. Structural and biophysical studies of ETF in complex with partner proteins have shown that ETF partitions the functions of partner binding and electron transfer between (a) a 'recognition loop', which acts as a static anchor at the ETF-partner interface, and (b) a highly mobile redox-active FAD domain. Together, this enables the FAD domain of ETF to sample a range of conformations, some compatible with fast interprotein electron transfer. This 'conformational sampling' enables ETF to recognize structurally distinct partners, whilst also maintaining a degree of specificity. Complex formation triggers mobility of the FAD domain, an 'induced disorder' mechanism contrasting with the more generally accepted models of protein-protein interaction by induced fit mechanisms. We discuss the implications of the highly dynamic nature of ETFs in biological interprotein electron transfer. ETF complexes point to mechanisms of electron transfer in which 'dynamics drive function', a feature that is probably widespread in biology given the modular assembly and flexible nature of biological electron transfer systems.

Coming Out in Class: Challenges and Benefits of Active Learning in a Biology Classroom for LGBTQIA Students.

PubMed

Cooper, Katelyn M; Brownell, Sara E

As we transition our undergraduate biology classrooms from traditional lectures to active learning, the dynamics among students become more important. These dynamics can be influenced by student social identities. One social identity that has been unexamined in the context of undergraduate biology is the spectrum of lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) identities. In this exploratory interview study, we probed the experiences and perceptions of seven students who identify as part of the LGBTQIA community. We found that students do not always experience the undergraduate biology classroom to be a welcoming or accepting place for their identities. In contrast to traditional lectures, active-learning classes increase the relevance of their LGBTQIA identities due to the increased interactions among students during group work. Finally, working with other students in active-learning classrooms can present challenges and opportunities for students considering their LGBTQIA identity. These findings indicate that these students' LGBTQIA identities are affecting their experience in the classroom and that there may be specific instructional practices that can mitigate some of the possible obstacles. We hope that this work can stimulate discussions about how to broadly make our active-learning biology classes more inclusive of this specific population of students. © 2016 K. M. Cooper and S. E. Brownell. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Volumetric imaging of fast biological dynamics in deep tissue via wavefront engineering

NASA Astrophysics Data System (ADS)

Kong, Lingjie; Tang, Jianyong; Cui, Meng

2016-03-01

To reveal fast biological dynamics in deep tissue, we combine two wavefront engineering methods that were developed in our laboratory, namely optical phase-locked ultrasound lens (OPLUL) based volumetric imaging and iterative multiphoton adaptive compensation technique (IMPACT). OPLUL is used to generate oscillating defocusing wavefront for fast axial scanning, and IMPACT is used to compensate the wavefront distortions for deep tissue imaging. We show its promising applications in neuroscience and immunology.

The fluidity of biosocial identity and the effects of place, space, and time.

PubMed

Wiese, Daniel; Rodriguez Escobar, Jeronimo; Hsu, Yohsiang; Kulathinal, Rob J; Hayes-Conroy, Allison

2018-02-01

Public and scientific conceptions of identity are changing alongside advances in biotechnology, with important relevance to health and medicine. In particular, biological identity, once predominantly conceived as static (e.g., related to DNA, dental records, fingerprints) is now being recognized as dynamic or fluid, mirroring contemporary understandings of psychological and social identity. The dynamism of biological identity comes from the individual body's unique relationship with the world surrounding it, and therefore may best be described as biosocial. This paper reviews advances in scientific understandings of identity and presents a model that contrasts prior static approaches to biological identity from more recent dynamically-relational ones. This emerging viewpoint is of broad significance to health and medicine, particularly as medicine recognizes the significance of biography - i.e. the multiple, dense interactions imparted on a body across spatio-temporal dimensions - to phenotypic prediction, especially disease risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Polynomial algebra of discrete models in systems biology.

PubMed

Veliz-Cuba, Alan; Jarrah, Abdul Salam; Laubenbacher, Reinhard

2010-07-01

An increasing number of discrete mathematical models are being published in Systems Biology, ranging from Boolean network models to logical models and Petri nets. They are used to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation. This article provides a unified framework that can aid the mathematical analysis of Boolean network models, logical models and Petri nets. They can be represented as polynomial dynamical systems, which allows the use of a variety of mathematical tools from computer algebra for their analysis. Algorithms are presented for the translation into polynomial dynamical systems. Examples are given of how polynomial algebra can be used for the model analysis. alanavc@vt.edu Supplementary data are available at Bioinformatics online.

It's All in Your Head

ERIC Educational Resources Information Center

Ennis, Lisa A.

2007-01-01

The dynamic and rapidly expanding field of neuroscience traditionally has involved the study of the nervous system from a biological/medical standpoint. In recent years, however, the science has become multidisciplinary, attracting researchers from computer science, psychology, sociology, philosophy, and even the humanities. For public and college…

Accomplishment Summary 1968-1969. Biological Computer Laboratory.

ERIC Educational Resources Information Center

Von Foerster, Heinz; And Others

This report summarizes theoretical, applied, and experimental studies in the areas of computational principles in complex intelligent systems, cybernetics, multivalued logic, and the mechanization of cognitive processes. This work is summarized under the following topic headings: properties of complex dynamic systems; computers and the language…

Ecological and Evolutionary Effects of Dispersal on Freshwater Zooplankton

ERIC Educational Resources Information Center

Allen, Michael R.

2009-01-01

A recent focus on contemporary evolution and the connections between communities has sought to more closely integrate ecology with evolutionary biology. Studies of coevolutionary dynamics, life history evolution, and rapid local adaptation demonstrate that ecological circumstances can dictate evolutionary trajectories. Thus, variation in species…

Physiological and pathophysiological reactive oxygen species as probed by EPR spectroscopy: the underutilized research window on muscle ageing.

PubMed

A Abdel-Rahman, Engy; Mahmoud, Ali M; Khalifa, Abdulrahman M; Ali, Sameh S

2016-08-15

Reactive oxygen and nitrogen species (ROS and RNS) play crucial roles in triggering, mediating and regulating physiological and pathophysiological signal transduction pathways within the cell. Within the cell, ROS efflux is firmly controlled both spatially and temporally, making the study of ROS dynamics a challenging task. Different approaches have been developed for ROS assessment; however, many of these assays are not capable of direct identification or determination of subcellular localization of different ROS. Here we highlight electron paramagnetic resonance (EPR) spectroscopy as a powerful technique that is uniquely capable of addressing questions on ROS dynamics in different biological specimens and cellular compartments. Due to their critical importance in muscle functions and dysfunction, we discuss in some detail spin trapping of various ROS and focus on EPR detection of nitric oxide before highlighting how EPR can be utilized to probe biophysical characteristics of the environment surrounding a given stable radical. Despite the demonstrated ability of EPR spectroscopy to provide unique information on the identity, quantity, dynamics and environment of radical species, its applications in the field of muscle physiology, fatiguing and ageing are disproportionately infrequent. While reviewing the limited examples of successful EPR applications in muscle biology we conclude that the field would greatly benefit from more studies exploring ROS sources and kinetics by spin trapping, protein dynamics by site-directed spin labelling, and membrane dynamics and global redox changes by spin probing EPR approaches. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts.

PubMed

Viñuelas, José; Kaneko, Gaël; Coulon, Antoine; Vallin, Elodie; Morin, Valérie; Mejia-Pous, Camila; Kupiec, Jean-Jacques; Beslon, Guillaume; Gandrillon, Olivier

2013-02-25

A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells. For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability. In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System

PubMed Central

Barrio, Rafael A.; Romero-Arias, José Roberto; Noguez, Marco A.; Azpeitia, Eugenio; Ortiz-Gutiérrez, Elizabeth; Hernández-Hernández, Valeria; Cortes-Poza, Yuriria; Álvarez-Buylla, Elena R.

2013-01-01

A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate, yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ. In this paper we propose that, during development, cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating. This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system. We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular, genetic and cellular levels and has the key traits of multicellular stem-cell niches. We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics, both of which are known to play a role in root development. We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip. Our model recovers, as an emergent pattern, the transition from proliferative to transition and elongation domains, characteristic of stem-cell niches in multicellular organisms. In addition, we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions. Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems. PMID:23658505

Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations.

PubMed

Lin, Yao-Cheng; Boone, Morgane; Meuris, Leander; Lemmens, Irma; Van Roy, Nadine; Soete, Arne; Reumers, Joke; Moisse, Matthieu; Plaisance, Stéphane; Drmanac, Radoje; Chen, Jason; Speleman, Frank; Lambrechts, Diether; Van de Peer, Yves; Tavernier, Jan; Callewaert, Nico

2014-09-03

The HEK293 human cell lineage is widely used in cell biology and biotechnology. Here we use whole-genome resequencing of six 293 cell lines to study the dynamics of this aneuploid genome in response to the manipulations used to generate common 293 cell derivatives, such as transformation and stable clone generation (293T); suspension growth adaptation (293S); and cytotoxic lectin selection (293SG). Remarkably, we observe that copy number alteration detection could identify the genomic region that enabled cell survival under selective conditions (i.c. ricin selection). Furthermore, we present methods to detect human/vector genome breakpoints and a user-friendly visualization tool for the 293 genome data. We also establish that the genome structure composition is in steady state for most of these cell lines when standard cell culturing conditions are used. This resource enables novel and more informed studies with 293 cells, and we will distribute the sequenced cell lines to this effect.

Functionalized lipids and surfactants for specific applications.

PubMed

Kepczynski, Mariusz; Róg, Tomasz

2016-10-01

Synthetic lipids and surfactants that do not exist in biological systems have been used for the last few decades in both basic and applied science. The most notable applications for synthetic lipids and surfactants are drug delivery, gene transfection, as reporting molecules, and as support for structural lipid biology. In this review, we describe the potential of the synergistic combination of computational and experimental methodologies to study the behavior of synthetic lipids and surfactants embedded in lipid membranes and liposomes. We focused on select cases in which molecular dynamics simulations were used to complement experimental studies aiming to understand the structure and properties of new compounds at the atomistic level. We also describe cases in which molecular dynamics simulations were used to design new synthetic lipids and surfactants, as well as emerging fields for the application of these compounds. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg. Copyright © 2016 Elsevier B.V. All rights reserved.

Bacterial molecular networks: bridging the gap between functional genomics and dynamical modelling.

PubMed

van Helden, Jacques; Toussaint, Ariane; Thieffry, Denis

2012-01-01

This introductory review synthesizes the contents of the volume Bacterial Molecular Networks of the series Methods in Molecular Biology. This volume gathers 9 reviews and 16 method chapters describing computational protocols for the analysis of metabolic pathways, protein interaction networks, and regulatory networks. Each protocol is documented by concrete case studies dedicated to model bacteria or interacting populations. Altogether, the chapters provide a representative overview of state-of-the-art methods for data integration and retrieval, network visualization, graph analysis, and dynamical modelling.

Hybrid finite element and Brownian dynamics method for diffusion-controlled reactions.

PubMed

Bauler, Patricia; Huber, Gary A; McCammon, J Andrew

2012-04-28

Diffusion is often the rate determining step in many biological processes. Currently, the two main computational methods for studying diffusion are stochastic methods, such as Brownian dynamics, and continuum methods, such as the finite element method. This paper proposes a new hybrid diffusion method that couples the strengths of each of these two methods. The method is derived for a general multidimensional system, and is presented using a basic test case for 1D linear and radially symmetric diffusion systems.

Homoclinic Bifurcation in an SIQR Model for Childhood Diseases

NASA Astrophysics Data System (ADS)

Wu, Lih-Ing; Feng, Zhilan

2000-11-01

We consider a system of ODEs which describes the transmission dynamics of childhood diseases. A center manifold reduction at a bifurcation point has the normal form x‧=y, y‧=axy+bx2y+O(4), indicating a bifurcation of codimension greater than two. A three-parameter unfolding of the normal form is studied to capture possible complex dynamics of the original system which is subjected to certain constraints on the state space due to biological considerations. It is shown that the perturbed system produces homoclinic bifurcation.

Multiscale agent-based cancer modeling.

PubMed

Zhang, Le; Wang, Zhihui; Sagotsky, Jonathan A; Deisboeck, Thomas S

2009-04-01

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.

Propagation dynamics for a spatially periodic integrodifference competition model

NASA Astrophysics Data System (ADS)

Wu, Ruiwen; Zhao, Xiao-Qiang

2018-05-01

In this paper, we study the propagation dynamics for a class of integrodifference competition models in a periodic habitat. An interesting feature of such a system is that multiple spreading speeds can be observed, which biologically means different species may have different spreading speeds. We show that the model system admits a single spreading speed, and it coincides with the minimal wave speed of the spatially periodic traveling waves. A set of sufficient conditions for linear determinacy of the spreading speed is also given.

Flexive and Propulsive Dynamics of Elastica at Low Reynolds Number

NASA Astrophysics Data System (ADS)

Wiggins, Chris H.; Goldstein, Raymond E.

1998-04-01

A stiff one-armed swimmer in glycerine goes nowhere. However, if its arm is elastic, the swimmer can go on its way. Quantifying this observation, we study a hyperdiffusion equation for the shape of the elastica in a viscous fluid, find solutions for impulsive or oscillatory forcing, and elucidate relevant aspects of propulsion. These results have application in a variety of physical and biological contexts, from dynamic experiments measuring biopolymer bending moduli to instabilities of twisted elastic filaments.

Interplay of Structure and Dynamics in Biomaterials

NASA Astrophysics Data System (ADS)

Vodnala, Preeti

Study of structure and dynamic behavior is essential to understand molecular motions in biological systems. In this work, two biomaterials were studied to address membrane properties and protein diffusion. For the first project, we studied the structure of liposomes, artificial vesicles that are used for drug encapsulation and administration of pharmaceuticals or cellular nutrients. Small-angle x-ray scattering (SAXS) was used to determine the structural properties of different liposomes composed of egg-PC and cholesterol bilayer. We examined the location of cholesterol by labelling cholesterol with bromine molecule and reveal that cholesterol is located one side of the leaflet adjusting itself to the curvature of a liposome. In my second project, we studied the dynamics of concentrated suspensions of alpha crystallin, one of the most abundant proteins in the human eye lens using X-ray photon correlation spectroscopy (XPCS). An improved understanding of dynamics could point the way towards treatments presbyopia and cataract. The dynamics were measured at volume fraction close to the critical volume fraction for the glass transition, where the intermediate scattering function, ƒ(q,T) could be well fit using a double exponential decay. The measured relaxation is in reasonable agreement with published molecular dynamics simulations for the relaxation times of hard-sphere colloids.

Sport science integration: An evolutionary synthesis.

PubMed

Balagué, N; Torrents, C; Hristovski, R; Kelso, J A S

2017-02-01

The aim of the paper is to point out one way of integrating the supposedly incommensurate disciplines investigated in sports science. General, common principles can be found among apparently unrelated disciplines when the focus is put on the dynamics of sports-related phenomena. Dynamical systems approaches that have recently changed research in biological and social sciences among others, offer key concepts to create a common pluricontextual language in sport science. This common language, far from being homogenising, offers key synthesis between diverse fields, respecting and enabling the theoretical and experimental pluralism. It forms a softly integrated sports science characterised by a basic dynamic explanatory backbone as well as context-dependent theoretical flexibility. After defining the dynamic integration in living systems, unable to be captured by structural static approaches, we show the commonalities between the diversity of processes existing on different levels and time scales in biological and social entities. We justify our interpretation by drawing on some recent scientific contributions that use the same general principles and concepts, and diverse methods and techniques of data analysis, to study different types of phenomena in diverse disciplines. We show how the introduction of the dynamic framework in sport science has started to blur the boundaries between physiology, biomechanics, psychology, phenomenology and sociology. The advantages and difficulties of sport science integration and its consequences in research are also discussed.

Earth observing satellite: Understanding the Earth as a system

NASA Technical Reports Server (NTRS)

Soffen, Gerald

1990-01-01

There is now a plan for global studies which include two very large efforts. One is the International Geosphere/Biosphere Program (IGBP) sponsored by the International Council of Scientific Unions. The other initiative is Mission to Planet Earth, an unbrella program for doing three kinds of space missions. The major one is the Earth Observation Satellite (EOS). EOS is large polar orbiting satellites with heavy payloads. Two will be placed in orbit by NASA, one by the Japanese and one or two by ESA. The overall mission measurement objectives of EOS are summarized: (1) the global distribution of energy input to and energy output from the Earth; (2) the structure, state variables, composition, and dynamics of the atmosphere from the ground to the mesopause; (3) the physical and biological structure, state, composition, and dynamics of the land surface, including terrestrial and inland water ecosystems; (4) the rates, important sources and sinks, and key components and processes of the Earth's biogeochemical cycles; (5) the circulation, surface temperature, wind stress, sea state, and the biological activity of the oceans; (6) the extent, type, state, elevation, roughness, and dynamics of glaciers, ice sheets, snow and sea ice, and the liquid equivalent of snow in the global cryosphere; (7) the global rates, amounts, and distribution of precipitation; and (8) the dynamic motions of the Earth (geophysics) as a whole, including both rotational dynamics and the kinematic motions of the tectonic plates.

An Introduction to Biological NMR Spectroscopy*

PubMed Central

Marion, Dominique

2013-01-01

NMR spectroscopy is a powerful tool for biologists interested in the structure, dynamics, and interactions of biological macromolecules. This review aims at presenting in an accessible manner the requirements and limitations of this technique. As an introduction, the history of NMR will highlight how the method evolved from physics to chemistry and finally to biology over several decades. We then introduce the NMR spectral parameters used in structural biology, namely the chemical shift, the J-coupling, nuclear Overhauser effects, and residual dipolar couplings. Resonance assignment, the required step for any further NMR study, bears a resemblance to jigsaw puzzle strategy. The NMR spectral parameters are then converted into angle and distances and used as input using restrained molecular dynamics to compute a bundle of structures. When interpreting a NMR-derived structure, the biologist has to judge its quality on the basis of the statistics provided. When the 3D structure is a priori known by other means, the molecular interaction with a partner can be mapped by NMR: information on the binding interface as well as on kinetic and thermodynamic constants can be gathered. NMR is suitable to monitor, over a wide range of frequencies, protein fluctuations that play a crucial role in their biological function. In the last section of this review, intrinsically disordered proteins, which have escaped the attention of classical structural biology, are discussed in the perspective of NMR, one of the rare available techniques able to describe structural ensembles. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 16 MCP). PMID:23831612

The Impact of Collective Molecular Dynamics on Physiological and Biological Functionalities of Artificial and Biological Membranes

NASA Astrophysics Data System (ADS)

Rheinstadter, Maikel

2008-03-01

We use neutron, X-ray and light scattering techniques to determine dynamical and structural properties of artificial and biological membranes. The combination of various techniques enlarges the window to length scales from the nearest-neighbor distances of lipid molecules to more than 10-6m, covering time scales from about 0.1 ps to 1 s. The main research objective is to quantify collective molecular fluctuations in these systems and to establish relationships to physiological and biological functions of the bilayers, such as transmembrane transport. The motivation for this project is twofold: 1) By understanding fundamental properties of bilayers at the microscopic and mesoscopic level, we aim to tailor membranes with specific properties such as permeability and elasticity. 2) By relating dynamical fluctuations to physiological and biological functions, we can gain a deeper understanding of the bilayers on a molecular scale that may help optimizing the transmembrane transport of certain drugs. We show how bilayer permeability, elasticity and inter protein excitations can be determined from the experiments. M.C. Rheinstädter et al., Phys. Rev. Lett. 93, 108107 (2004); Phys. Rev. Lett. 97, 048103 (2006); Phys. Rev. E 75, 011907 (2007);J. Vac. Soc. Technol. A 24, 1191 (2006).

Joshua Vermaas | NREL

Science.gov Websites

molecular dynamics simulations to explore biological interfaces, such as those found at the cell membrane or in lignocellulosic biomass. In particular, molecular dynamics can see in molecular detail the research toward fruitful results. Areas of Expertise Molecular dynamics Compound parameterization

"Engaging in an Argumentative Discourse"'- Narratives from Biology Classrooms

ERIC Educational Resources Information Center

Saxena, Astha; Behari, Alka

2015-01-01

The present paper delves into the classroom dynamics of Biology classrooms taking into account teaching learning processes associated with some of the ethical issues in Biological Sciences. Argumentation and debate appear to be the major transactional approaches adopted by teachers for dealing with these issues. The classroom discourses emanating…

Coherent organization in gene regulation: a study on six networks

NASA Astrophysics Data System (ADS)

Aral, Neşe; Kabakçıoğlu, Alkan

2016-04-01

Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes.

Characterization of the motion of membrane proteins using high-speed atomic force microscopy

NASA Astrophysics Data System (ADS)

Casuso, Ignacio; Khao, Jonathan; Chami, Mohamed; Paul-Gilloteaux, Perrine; Husain, Mohamed; Duneau, Jean-Pierre; Stahlberg, Henning; Sturgis, James N.; Scheuring, Simon

2012-08-01

For cells to function properly, membrane proteins must be able to diffuse within biological membranes. The functions of these membrane proteins depend on their position and also on protein-protein and protein-lipid interactions. However, so far, it has not been possible to study simultaneously the structure and dynamics of biological membranes. Here, we show that the motion of unlabelled membrane proteins can be characterized using high-speed atomic force microscopy. We find that the molecules of outer membrane protein F (OmpF) are widely distributed in the membrane as a result of diffusion-limited aggregation, and while the overall protein motion scales roughly with the local density of proteins in the membrane, individual protein molecules can also diffuse freely or become trapped by protein-protein interactions. Using these measurements, and the results of molecular dynamics simulations, we determine an interaction potential map and an interaction pathway for a membrane protein, which should provide new insights into the connection between the structures of individual proteins and the structures and dynamics of supramolecular membranes.

Mathematical Modeling and Nonlinear Dynamical Analysis of Cell Growth in Response to Antibiotics

NASA Astrophysics Data System (ADS)

Jin, Suoqin; Niu, Lili; Wang, Gang; Zou, Xiufen

2015-06-01

This study is devoted to the revelation of the dynamical mechanisms of cell growth in response to antibiotics. We establish a mathematical model of ordinary differential equations for an antibiotic-resistant growth system with one positive feedback loop. We perform a dynamical analysis of the behavior of this model system. We present adequate sets of conditions that can guarantee the existence and stability of biologically-reasonable steady states. Using bifurcation analysis and numerical simulation, we show that the relative growth rate, which is defined as the ratio of the cell growth rate to the basal cell growth rate in the absence of antibiotics, can exhibit bistable behavior in an extensive range of parameters that correspond to a growth state and a nongrowth state in biology. We discover that both antibiotic and antibiotic resistance genes can cooperatively enhance bistability, whereas the cooperative coefficient of feedback can contribute to the onset of bistability. These results would contribute to a better understanding of not only the evolution of antibiotics but also the emergence of drug resistance in other diseases.

Draft De Novo Transcriptome of the Rat Kangaroo Potorous tridactylus as a Tool for Cell Biology

PubMed Central

Udy, Dylan B.; Voorhies, Mark; Chan, Patricia P.; Lowe, Todd M.; Dumont, Sophie

2015-01-01

The rat kangaroo (long-nosed potoroo, Potorous tridactylus) is a marsupial native to Australia. Cultured rat kangaroo kidney epithelial cells (PtK) are commonly used to study cell biological processes. These mammalian cells are large, adherent, and flat, and contain large and few chromosomes—and are thus ideal for imaging intra-cellular dynamics such as those of mitosis. Despite this, neither the rat kangaroo genome nor transcriptome have been sequenced, creating a challenge for probing the molecular basis of these cellular dynamics. Here, we present the sequencing, assembly and annotation of the draft rat kangaroo de novo transcriptome. We sequenced 679 million reads that mapped to 347,323 Trinity transcripts and 20,079 Unigenes. We present statistics emerging from transcriptome-wide analyses, and analyses suggesting that the transcriptome covers full-length sequences of most genes, many with multiple isoforms. We also validate our findings with a proof-of-concept gene knockdown experiment. We expect that this high quality transcriptome will make rat kangaroo cells a more tractable system for linking molecular-scale function and cellular-scale dynamics. PMID:26252667

Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics

NASA Astrophysics Data System (ADS)

Roca, Carlos P.; Cuesta, José A.; Sánchez, Angel

2009-12-01

Evolutionary game dynamics is one of the most fruitful frameworks for studying evolution in different disciplines, from Biology to Economics. Within this context, the approach of choice for many researchers is the so-called replicator equation, that describes mathematically the idea that those individuals performing better have more offspring and thus their frequency in the population grows. While very many interesting results have been obtained with this equation in the three decades elapsed since it was first proposed, it is important to realize the limits of its applicability. One particularly relevant issue in this respect is that of non-mean-field effects, that may arise from temporal fluctuations or from spatial correlations, both neglected in the replicator equation. This review discusses these temporal and spatial effects focusing on the non-trivial modifications they induce when compared to the outcome of replicator dynamics. Alongside this question, the hypothesis of linearity and its relation to the choice of the rule for strategy update is also analyzed. The discussion is presented in terms of the emergence of cooperation, as one of the current key problems in Biology and in other disciplines.

Soft inclusion in a confined fluctuating active gel

NASA Astrophysics Data System (ADS)

Singh Vishen, Amit; Rupprecht, J.-F.; Shivashankar, G. V.; Prost, J.; Rao, Madan

2018-03-01

We study stochastic dynamics of a point and extended inclusion within a one-dimensional confined active viscoelastic gel. We show that the dynamics of a point inclusion can be described by a Langevin equation with a confining potential and multiplicative noise. Using a systematic adiabatic elimination over the fast variables, we arrive at an overdamped equation with a proper definition of the multiplicative noise. To highlight various features and to appeal to different biological contexts, we treat the inclusion in turn as a rigid extended element, an elastic element, and a viscoelastic (Kelvin-Voigt) element. The dynamics for the shape and position of the extended inclusion can be described by coupled Langevin equations. Deriving exact expressions for the corresponding steady-state probability distributions, we find that the active noise induces an attraction to the edges of the confining domain. In the presence of a competing centering force, we find that the shape of the probability distribution exhibits a sharp transition upon varying the amplitude of the active noise. Our results could help understanding the positioning and deformability of biological inclusions, e.g., organelles in cells, or nucleus and cells within tissues.

Draft De Novo Transcriptome of the Rat Kangaroo Potorous tridactylus as a Tool for Cell Biology.

PubMed

Udy, Dylan B; Voorhies, Mark; Chan, Patricia P; Lowe, Todd M; Dumont, Sophie

2015-01-01

The rat kangaroo (long-nosed potoroo, Potorous tridactylus) is a marsupial native to Australia. Cultured rat kangaroo kidney epithelial cells (PtK) are commonly used to study cell biological processes. These mammalian cells are large, adherent, and flat, and contain large and few chromosomes-and are thus ideal for imaging intra-cellular dynamics such as those of mitosis. Despite this, neither the rat kangaroo genome nor transcriptome have been sequenced, creating a challenge for probing the molecular basis of these cellular dynamics. Here, we present the sequencing, assembly and annotation of the draft rat kangaroo de novo transcriptome. We sequenced 679 million reads that mapped to 347,323 Trinity transcripts and 20,079 Unigenes. We present statistics emerging from transcriptome-wide analyses, and analyses suggesting that the transcriptome covers full-length sequences of most genes, many with multiple isoforms. We also validate our findings with a proof-of-concept gene knockdown experiment. We expect that this high quality transcriptome will make rat kangaroo cells a more tractable system for linking molecular-scale function and cellular-scale dynamics.

Observation and imitation of actions performed by humans, androids, and robots: an EMG study

PubMed Central

Hofree, Galit; Urgen, Burcu A.; Winkielman, Piotr; Saygin, Ayse P.

2015-01-01

Understanding others’ actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others’ behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants’ arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations. PMID:26150782

Beyond Standard Molecular Dynamics: Investigating the Molecular Mechanisms of G Protein-Coupled Receptors with Enhanced Molecular Dynamics Methods

PubMed Central

Johnston, Jennifer M.

2014-01-01

The majority of biological processes mediated by G Protein-Coupled Receptors (GPCRs) take place on timescales that are not conveniently accessible to standard molecular dynamics (MD) approaches, notwithstanding the current availability of specialized parallel computer architectures, and efficient simulation algorithms. Enhanced MD-based methods have started to assume an important role in the study of the rugged energy landscape of GPCRs by providing mechanistic details of complex receptor processes such as ligand recognition, activation, and oligomerization. We provide here an overview of these methods in their most recent application to the field. PMID:24158803

Characterizing RNA Dynamics at Atomic Resolution Using Solution-state NMR Spectroscopy

PubMed Central

Bothe, Jameson R.; Nikolova, Evgenia N.; Eichhorn, Catherine D.; Chugh, Jeetender; Hansen, Alexandar L.; Al-Hashimi, Hashim M.

2012-01-01

Many recently discovered non-coding RNAs do not fold into a single native conformation, but rather, sample many different conformations along their free energy landscape to carry out their biological function. Unprecedented insights into the RNA dynamic structure landscape are provided by solution-state NMR techniques that measure the structural, kinetic, and thermodynamic characteristics of motions spanning picosecond to second timescales at atomic resolution. From these studies a basic description of the RNA dynamic structure landscape is emerging, bringing new insights into how RNA structures change to carry out their function as well as applications in RNA-targeted drug discovery and RNA bioengineering. PMID:22036746

Family structure and child food insecurity.

PubMed

Miller, Daniel P; Nepomnyaschy, Lenna; Ibarra, Gabriel Lara; Garasky, Steven

2014-07-01

We examined whether food insecurity was different for children in cohabiting or repartnered families versus those in single-mother or married-parent (biological) families. We compared probabilities of child food insecurity (CFI) across different family structures in 4 national data sets: the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B), the Fragile Families and Child Wellbeing Study (FFCWS), the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), and the Panel Study of Income Dynamics-Child Development Supplement (PSID-CDS). Unadjusted probabilities of CFI in cohabiting or repartnered families were generally higher than in married-biological-parent families and often statistically indistinguishable from those of single-mother families. However, after adjustment for sociodemographic factors, most differences between family types were attenuated and most were no longer statistically significant. Although children whose biological parents are cohabiting or whose biological mothers have repartnered have risks for food insecurity comparable to those in single-mother families, the probability of CFI does not differ by family structure when household income, family size, and maternal race, ethnicity, education, and age were held at mean levels.

Integrated experimental platforms to study blast injuries: a bottom-up approach

NASA Astrophysics Data System (ADS)

Bo, C.; Williams, A.; Rankin, S.; Proud, W. G.; Brown, K. A.

2014-05-01

We are developing experimental models of blast injury using data from live biological samples. An integrated research strategy is followed to study material and biological properties of cells, tissues and organs, that are subjected to dynamic and static pressures, relevant to those of battlefield blast. We have developed a confined Split Hopkinson Pressure Bar (SHPB) system, which allows cells, either in suspension or as a monolayer, to be subjected to compression waves with pressures on the order of a few MPa and durations of hundreds of microseconds. The chamber design enables recovery of biological samples for cellular and molecular analysis. The SHPB platform, coupled with Quasi-Static experiments, is used to determine stress-strain curves of soft biological tissues under compression at low, medium and high strain rates. Tissue samples are examined, using histological techniques, to study macro- and microscopic changes induced by compression waves. In addition, a shock tube enables application of single or multiple air blasts with pressures on the order of kPa and a few milliseconds duration; this platform was used for initial studies on mesenchymal stem cells responses to blast pressures.

Reaction-based small-molecule fluorescent probes for chemoselective bioimaging

PubMed Central

Chan, Jefferson; Dodani, Sheel C.; Chang, Christopher J.

2014-01-01

The dynamic chemical diversity of elements, ions and molecules that form the basis of life offers both a challenge and an opportunity for study. Small-molecule fluorescent probes can make use of selective, bioorthogonal chemistries to report on specific analytes in cells and in more complex biological specimens. These probes offer powerful reagents to interrogate the physiology and pathology of reactive chemical species in their native environments with minimal perturbation to living systems. This Review presents a survey of tools and tactics for using such probes to detect biologically important chemical analytes. We highlight design criteria for effective chemical tools for use in biological applications as well as gaps for future exploration. PMID:23174976

Systems biology approach to bioremediation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakraborty, Romy; Wu, Cindy H.; Hazen, Terry C.

2012-06-01

Bioremediation has historically been approached as a ‘black box’ in terms of our fundamental understanding. Thus it succeeds and fails, seldom without a complete understanding of why. Systems biology is an integrated research approach to study complex biological systems, by investigating interactions and networks at the molecular, cellular, community, and ecosystem level. The knowledge of these interactions within individual components is fundamental to understanding the dynamics of the ecosystem under investigation. Finally, understanding and modeling functional microbial community structure and stress responses in environments at all levels have tremendous implications for our fundamental understanding of hydrobiogeochemical processes and the potentialmore » for making bioremediation breakthroughs and illuminating the ‘black box’.« less

Size-Dependent Protein-Nanoparticle Interactions in Citrate-Stabilized Gold Nanoparticles: The Emergence of the Protein Corona.

PubMed

Piella, Jordi; Bastús, Neus G; Puntes, Víctor

2017-01-18

Surface modifications of highly monodisperse citrate-stabilized gold nanoparticles (AuNPs) with sizes ranging from 3.5 to 150 nm after their exposure to cell culture media supplemented with fetal bovine serum were studied and characterized by the combined use of UV-vis spectroscopy, dynamic light scattering, and zeta potential measurements. In all the tested AuNPs, a dynamic process of protein adsorption was observed, evolving toward the formation of an irreversible hard protein coating known as Protein Corona. Interestingly, the thickness and density of this protein coating were strongly dependent on the particle size, making it possible to identify different transition regimes as the size of the particles increased: (i) NP-protein complexes (or incomplete corona), (ii) the formation of a near-single dense protein corona layer, and (iii) the formation of a multilayer corona. In addition, the different temporal patterns in the evolution of the protein coating came about more quickly for small particles than for the larger ones, further revealing the significant role that size plays in the kinetics of this process. Since the biological identity of the NPs is ultimately determined by the protein corona and different NP-biological interactions take place at different time scales, these results are relevant to biological and toxicological studies.

GPCRs: What Can We Learn from Molecular Dynamics Simulations?

PubMed

Velgy, Naushad; Hedger, George; Biggin, Philip C

2018-01-01

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Principles for the dynamic maintenance of cortical polarity

PubMed Central

Marco, Eugenio; Wedlich-Soldner, Roland; Li, Rong; Altschuler, Steven J.; Wu, Lani F.

2007-01-01

Summary Diverse cell types require the ability to dynamically maintain polarized membrane protein distributions through balancing transport and diffusion. However, design principles underlying dynamically maintained cortical polarity are not well understood. Here we constructed a mathematical model for characterizing the morphology of dynamically polarized protein distributions. We developed analytical approaches for measuring all model parameters from single-cell experiments. We applied our methods to a well-characterized system for studying polarized membrane proteins: budding yeast cells expressing activated Cdc42. We found that balanced diffusion and colocalized transport to and from the plasma membrane were sufficient for accurately describing polarization morphologies. Surprisingly, the model predicts that polarized regions are defined with a precision that is nearly optimal for measured transport rates, and that polarity can be dynamically stabilized through positive feedback with directed transport. Our approach provides a step towards understanding how biological systems shape spatially precise, unambiguous cortical polarity domains using dynamic processes. PMID:17448998

An assay to image neuronal microtubule dynamics in mice.

PubMed

Kleele, Tatjana; Marinković, Petar; Williams, Philip R; Stern, Sina; Weigand, Emily E; Engerer, Peter; Naumann, Ronald; Hartmann, Jana; Karl, Rosa M; Bradke, Frank; Bishop, Derron; Herms, Jochen; Konnerth, Arthur; Kerschensteiner, Martin; Godinho, Leanne; Misgeld, Thomas

2014-09-12

Microtubule dynamics in neurons play critical roles in physiology, injury and disease and determine microtubule orientation, the cell biological correlate of neurite polarization. Several microtubule binding proteins, including end-binding protein 3 (EB3), specifically bind to the growing plus tip of microtubules. In the past, fluorescently tagged end-binding proteins have revealed microtubule dynamics in vitro and in non-mammalian model organisms. Here, we devise an imaging assay based on transgenic mice expressing yellow fluorescent protein-tagged EB3 to study microtubules in intact mammalian neurites. Our approach allows measurement of microtubule dynamics in vivo and ex vivo in peripheral nervous system and central nervous system neurites under physiological conditions and after exposure to microtubule-modifying drugs. We find an increase in dynamic microtubules after injury and in neurodegenerative disease states, before axons show morphological indications of degeneration or regrowth. Thus increased microtubule dynamics might serve as a general indicator of neurite remodelling in health and disease.

Integrated structural biology to unravel molecular mechanisms of protein-RNA recognition.

PubMed

Schlundt, Andreas; Tants, Jan-Niklas; Sattler, Michael

2017-04-15

Recent advances in RNA sequencing technologies have greatly expanded our knowledge of the RNA landscape in cells, often with spatiotemporal resolution. These techniques identified many new (often non-coding) RNA molecules. Large-scale studies have also discovered novel RNA binding proteins (RBPs), which exhibit single or multiple RNA binding domains (RBDs) for recognition of specific sequence or structured motifs in RNA. Starting from these large-scale approaches it is crucial to unravel the molecular principles of protein-RNA recognition in ribonucleoprotein complexes (RNPs) to understand the underlying mechanisms of gene regulation. Structural biology and biophysical studies at highest possible resolution are key to elucidate molecular mechanisms of RNA recognition by RBPs and how conformational dynamics, weak interactions and cooperative binding contribute to the formation of specific, context-dependent RNPs. While large compact RNPs can be well studied by X-ray crystallography and cryo-EM, analysis of dynamics and weak interaction necessitates the use of solution methods to capture these properties. Here, we illustrate methods to study the structure and conformational dynamics of protein-RNA complexes in solution starting from the identification of interaction partners in a given RNP. Biophysical and biochemical techniques support the characterization of a protein-RNA complex and identify regions relevant in structural analysis. Nuclear magnetic resonance (NMR) is a powerful tool to gain information on folding, stability and dynamics of RNAs and characterize RNPs in solution. It provides crucial information that is complementary to the static pictures derived from other techniques. NMR can be readily combined with other solution techniques, such as small angle X-ray and/or neutron scattering (SAXS/SANS), electron paramagnetic resonance (EPR), and Förster resonance energy transfer (FRET), which provide information about overall shapes, internal domain arrangements and dynamics. Principles of protein-RNA recognition and current approaches are reviewed and illustrated with recent studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic Self-Stiffening in Liquid Crystal Elastomers

PubMed Central

Agrawal, Aditya; Chipara, Alin C.; Shamoo, Yousif; Patra, Prabir K.; Carey, Brent J.; Ajayan, Pulickel M.; Chapman, Walter G.

2013-01-01

Biological tissues have the remarkable ability to remodel and repair in response to disease, injury, and mechanical stresses. Synthetic materials lack the complexity of biological tissues, and man-made materials which respond to external stresses through a permanent increase in stiffness are uncommon. Here, we report that polydomain nematic liquid crystal elastomers increase in stiffness by up to 90% when subjected to a low-amplitude (5%), repetitive (dynamic) compression. Elastomer stiffening is influenced by liquid crystal content, the presence of a nematic liquid crystal phase and the use of a dynamic as opposed to static deformation. Through rheological and X-ray diffraction measurements, stiffening can be attributed to a nematic director which rotates in response to dynamic compression. Stiffening under dynamic compression has not been previously observed in liquid crystal elastomers and may be useful for the development of self-healing materials or for the development of biocompatible, adaptive materials for tissue replacement. PMID:23612280

Understanding force-generating microtubule systems through in vitro reconstitution

PubMed Central

Kok, Maurits; Dogterom, Marileen

2016-01-01

ABSTRACT Microtubules switch between growing and shrinking states, a feature known as dynamic instability. The biochemical parameters underlying dynamic instability are modulated by a wide variety of microtubule-associated proteins that enable the strict control of microtubule dynamics in cells. The forces generated by controlled growth and shrinkage of microtubules drive a large range of processes, including organelle positioning, mitotic spindle assembly, and chromosome segregation. In the past decade, our understanding of microtubule dynamics and microtubule force generation has progressed significantly. Here, we review the microtubule-intrinsic process of dynamic instability, the effect of external factors on this process, and how the resulting forces act on various biological systems. Recently, reconstitution-based approaches have strongly benefited from extensive biochemical and biophysical characterization of individual components that are involved in regulating or transmitting microtubule-driven forces. We will focus on the current state of reconstituting increasingly complex biological systems and provide new directions for future developments. PMID:27715396

"Life-bearing molecules" versus "life-embodying systems": Two contrasting views on the what-is-life (WIL) problem persisting from the early days of molecular biology to the post-genomic cell- and organism-level biology.

PubMed

Sato, Naoki

2018-05-01

"What is life?" is an ultimate biological quest for the principle that makes organisms alive. This 'WIL problem' is not, however, a simple one that we have a straightforward strategy to attack. From the beginning, molecular biology tried to identify molecules that bear the essence of life: the double helical DNA represented replication, and enzymes were micro-actuators of biological activities. A dominating idea behind these mainstream biological studies relies on the identification of life-bearing molecules, which themselves are models of life. Another, prevalent idea emphasizes that life resides in the whole system of an organism, but not in some particular molecules. The behavior of a complex system may be considered to embody the essence of life. The thermodynamic view of life system in the early 20th century was remodeled as physics of complex systems and systems biology. The two views contrast with each other, but they are no longer heritage of the historical dualism in biology, such as mechanism/materialism versus vitalism, or reductionism versus holism. These two views are both materialistic and mechanistic, and act as driving forces of modern biology. In reality, molecules function in a context of systems, whereas systems presuppose functional molecules. A key notion to reconcile this conflict is that subjects of biological studies are given before we start to study them. Cell- or organism-level biology is destined to the dialectic of molecules and systems, but this antagonism can be resolved by dynamic thinking involving biological evolution. Copyright © 2018 Elsevier B.V. All rights reserved.

Microbiomic investigation of the poultry production chain: Using next generation sequencing to study general microbial and foodborne pathogen dynamics

USDA-ARS?s Scientific Manuscript database

There has been a recent effort to look at production systems as an entire “farm to fork” continuum to better understand what is occurring physically, chemically, and biologically within our food chains. These types of studies are especially applicable to human and environmental health issues, althou...

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity.

PubMed

Papaleo, Elena

2015-01-01

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Recent Dynamics of Common Loon Populations and their Implications for the Measurement of Ongoing Change

EPA Science Inventory

Common loons (Gavia immer) have been studied by evolutionary ecologists and conservation organizations for several decades, leading to a rich supply of observational data on breeding biology, behavior and abundance. Analysis of such data can provide insight into population demogr...

A Descriptive Empirical Study of Gay Male Stepfamilies.

ERIC Educational Resources Information Center

Crosbie-Burnett, Margaret; Helmbrecht, Lawrence

1993-01-01

Examined family dynamics associated with family happiness for biological fathers, stepfathers, and adolescents in 48 European-American gay stepfamilies using modified Stepfamily Adjustment Scale. For all three family members, family happiness was more highly related to stepfather inclusion in family and to positive steprelationship than to…

Stochastic Dynamics through Hierarchically Embedded Markov Chains

NASA Astrophysics Data System (ADS)

Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.

2017-02-01

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Functional dynamics of cell surface membrane proteins

NASA Astrophysics Data System (ADS)

Nishida, Noritaka; Osawa, Masanori; Takeuchi, Koh; Imai, Shunsuke; Stampoulis, Pavlos; Kofuku, Yutaka; Ueda, Takumi; Shimada, Ichio

2014-04-01

Cell surface receptors are integral membrane proteins that receive external stimuli, and transmit signals across plasma membranes. In the conventional view of receptor activation, ligand binding to the extracellular side of the receptor induces conformational changes, which convert the structure of the receptor into an active conformation. However, recent NMR studies of cell surface membrane proteins have revealed that their structures are more dynamic than previously envisioned, and they fluctuate between multiple conformations in an equilibrium on various timescales. In addition, NMR analyses, along with biochemical and cell biological experiments indicated that such dynamical properties are critical for the proper functions of the receptors. In this review, we will describe several NMR studies that revealed direct linkage between the structural dynamics and the functions of the cell surface membrane proteins, such as G-protein coupled receptors (GPCRs), ion channels, membrane transporters, and cell adhesion molecules.

Modelling and finite-time stability analysis of psoriasis pathogenesis

NASA Astrophysics Data System (ADS)

Oza, Harshal B.; Pandey, Rakesh; Roper, Daniel; Al-Nuaimi, Yusur; Spurgeon, Sarah K.; Goodfellow, Marc

2017-08-01

A new systems model of psoriasis is presented and analysed from the perspective of control theory. Cytokines are treated as actuators to the plant model that govern the cell population under the reasonable assumption that cytokine dynamics are faster than the cell population dynamics. The analysis of various equilibria is undertaken based on singular perturbation theory. Finite-time stability and stabilisation have been studied in various engineering applications where the principal paradigm uses non-Lipschitz functions of the states. A comprehensive study of the finite-time stability properties of the proposed psoriasis dynamics is carried out. It is demonstrated that the dynamics are finite-time convergent to certain equilibrium points rather than asymptotically or exponentially convergent. This feature of finite-time convergence motivates the development of a modified version of the Michaelis-Menten function, frequently used in biology. This framework is used to model cytokines as fast finite-time actuators.

Global Dynamics of Proteins: Bridging Between Structure and Function

PubMed Central

Bahar, Ivet; Lezon, Timothy R.; Yang, Lee-Wei; Eyal, Eran

2010-01-01

Biomolecular systems possess unique, structure-encoded dynamic properties that underlie their biological functions. Recent studies indicate that these dynamic properties are determined to a large extent by the topology of native contacts. In recent years, elastic network models used in conjunction with normal mode analyses have proven to be useful for elucidating the collective dynamics intrinsically accessible under native state conditions, including in particular the global modes of motions that are robustly defined by the overall architecture. With increasing availability of structural data for well-studied proteins in different forms (liganded, complexed, or free), there is increasing evidence in support of the correspondence between functional changes in structures observed in experiments and the global motions predicted by these coarse-grained analyses. These observed correlations suggest that computational methods may be advantageously employed for assessing functional changes in structure and allosteric mechanisms intrinsically favored by the native fold. PMID:20192781

Global dynamics of proteins: bridging between structure and function.

PubMed

Bahar, Ivet; Lezon, Timothy R; Yang, Lee-Wei; Eyal, Eran

2010-01-01

Biomolecular systems possess unique, structure-encoded dynamic properties that underlie their biological functions. Recent studies indicate that these dynamic properties are determined to a large extent by the topology of native contacts. In recent years, elastic network models used in conjunction with normal mode analyses have proven to be useful for elucidating the collective dynamics intrinsically accessible under native state conditions, including in particular the global modes of motions that are robustly defined by the overall architecture. With increasing availability of structural data for well-studied proteins in different forms (liganded, complexed, or free), there is increasing evidence in support of the correspondence between functional changes in structures observed in experiments and the global motions predicted by these coarse-grained analyses. These observed correlations suggest that computational methods may be advantageously employed for assessing functional changes in structure and allosteric mechanisms intrinsically favored by the native fold.

Renormalization of Collective Modes in Large-Scale Neural Dynamics

NASA Astrophysics Data System (ADS)

Moirogiannis, Dimitrios; Piro, Oreste; Magnasco, Marcelo O.

2017-05-01

The bulk of studies of coupled oscillators use, as is appropriate in Physics, a global coupling constant controlling all individual interactions. However, because as the coupling is increased, the number of relevant degrees of freedom also increases, this setting conflates the strength of the coupling with the effective dimensionality of the resulting dynamics. We propose a coupling more appropriate to neural circuitry, where synaptic strengths are under biological, activity-dependent control and where the coupling strength and the dimensionality can be controlled separately. Here we study a set of N→ ∞ strongly- and nonsymmetrically-coupled, dissipative, powered, rotational dynamical systems, and derive the equations of motion of the reduced system for dimensions 2 and 4. Our setting highlights the statistical structure of the eigenvectors of the connectivity matrix as the fundamental determinant of collective behavior, inheriting from this structure symmetries and singularities absent from the original microscopic dynamics.

Stochastic Dynamics through Hierarchically Embedded Markov Chains.

PubMed

Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M

2017-02-03

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Clinical study and numerical simulation of brain cancer dynamics under radiotherapy

NASA Astrophysics Data System (ADS)

Nawrocki, S.; Zubik-Kowal, B.

2015-05-01

We perform a clinical and numerical study of the progression of brain cancer tumor growth dynamics coupled with the effects of radiotherapy. We obtained clinical data from a sample of brain cancer patients undergoing radiotherapy and compare it to our numerical simulations to a mathematical model of brain tumor cell population growth influenced by radiation treatment. We model how the body biologically receives a physically delivered dose of radiation to the affected tumorous area in the form of a generalized LQ model, modified to account for the conversion process of sublethal lesions into lethal lesions at high radiation doses. We obtain good agreement between our clinical data and our numerical simulations of brain cancer progression given by the mathematical model, which couples tumor growth dynamics and the effect of irradiation. The correlation, spanning a wide dataset, demonstrates the potential of the mathematical model to describe the dynamics of brain tumor growth influenced by radiotherapy.

Functional dynamics of cell surface membrane proteins.

PubMed

Nishida, Noritaka; Osawa, Masanori; Takeuchi, Koh; Imai, Shunsuke; Stampoulis, Pavlos; Kofuku, Yutaka; Ueda, Takumi; Shimada, Ichio

2014-04-01

Cell surface receptors are integral membrane proteins that receive external stimuli, and transmit signals across plasma membranes. In the conventional view of receptor activation, ligand binding to the extracellular side of the receptor induces conformational changes, which convert the structure of the receptor into an active conformation. However, recent NMR studies of cell surface membrane proteins have revealed that their structures are more dynamic than previously envisioned, and they fluctuate between multiple conformations in an equilibrium on various timescales. In addition, NMR analyses, along with biochemical and cell biological experiments indicated that such dynamical properties are critical for the proper functions of the receptors. In this review, we will describe several NMR studies that revealed direct linkage between the structural dynamics and the functions of the cell surface membrane proteins, such as G-protein coupled receptors (GPCRs), ion channels, membrane transporters, and cell adhesion molecules. Copyright © 2013 Elsevier Inc. All rights reserved.

Discrete dynamic modeling of cellular signaling networks.

PubMed

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Quantitative assessment of dynamic PET imaging data in cancer imaging.

PubMed

Muzi, Mark; O'Sullivan, Finbarr; Mankoff, David A; Doot, Robert K; Pierce, Larry A; Kurland, Brenda F; Linden, Hannah M; Kinahan, Paul E

2012-11-01

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach. Copyright © 2012 Elsevier Inc. All rights reserved.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.

PubMed

Williams, Richard A; Timmis, Jon; Qwarnstrom, Eva E

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

PubMed Central

Timmis, Jon; Qwarnstrom, Eva E.

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model. PMID:27571414

Leaf water dynamics of Arabidopsis thaliana monitored in-vivo using terahertz time-domain spectroscopy

NASA Astrophysics Data System (ADS)

Castro-Camus, E.; Palomar, M.; Covarrubias, A. A.

2013-10-01

The declining water availability for agriculture is becoming problematic for many countries. Therefore the study of plants under water restriction is acquiring extraordinary importance. Botanists currently follow the dehydration of plants comparing the fresh and dry weight of excised organs, or measuring their osmotic or water potentials; these are destructive methods inappropriate for in-vivo determination of plants' hydration dynamics. Water is opaque in the terahertz band, while dehydrated biological tissues are partially transparent. We used terahertz spectroscopy to study the water dynamics of Arabidopsis thaliana by comparing the dehydration kinetics of leaves from plants under well-irrigated and water deficit conditions. We also present measurements of the effect of dark-light cycles and abscisic acid on its water dynamics. The measurements we present provide a new perspective on the water dynamics of plants under different external stimuli and confirm that terahertz can be an excellent non-contact probe of in-vivo tissue hydration.

Generalized Green's function molecular dynamics for canonical ensemble simulations

NASA Astrophysics Data System (ADS)

Coluci, V. R.; Dantas, S. O.; Tewary, V. K.

2018-05-01

The need of small integration time steps (˜1 fs) in conventional molecular dynamics simulations is an important issue that inhibits the study of physical, chemical, and biological systems in real timescales. Additionally, to simulate those systems in contact with a thermal bath, thermostating techniques are usually applied. In this work, we generalize the Green's function molecular dynamics technique to allow simulations within the canonical ensemble. By applying this technique to one-dimensional systems, we were able to correctly describe important thermodynamic properties such as the temperature fluctuations, the temperature distribution, and the velocity autocorrelation function. We show that the proposed technique also allows the use of time steps one order of magnitude larger than those typically used in conventional molecular dynamics simulations. We expect that this technique can be used in long-timescale molecular dynamics simulations.

Microfluidic Examination of the "Hard" Biomolecular Corona Formed on Engineered Particles in Different Biological Milieu.

PubMed

Weiss, Alessia C G; Kempe, Kristian; Förster, Stephan; Caruso, Frank

2018-04-18

The formation of a biomolecular corona around engineered particles determines, in large part, their biological behavior in vitro and in vivo. To gain a fundamental understanding of how particle design and the biological milieu influence the formation of the "hard" biomolecular corona, we conduct a series of in vitro studies using microfluidics. This setup allows the generation of a dynamic incubation environment with precise control over the applied flow rate, stream orientation, and channel dimensions, thus allowing accurate control of the fluid flow and the shear applied to the proteins and particles. We used mesoporous silica particles, poly(2-methacryloyloxyethylphosphorylcholine) (PMPC)-coated silica hybrid particles, and PMPC replica particles (obtained by removal of the silica particle templates), representing high-, intermediate-, and low-fouling particle systems, respectively. The protein source used in the experiments was either human serum or human full blood. The effects of flow, particle surface properties, incubation medium, and incubation time on the formation of the biomolecular corona formation are examined. Our data show that protein adhesion on particles is enhanced after incubation in human blood compared to human serum and that dynamic incubation leads to a more complex corona. By varying the incubation time from 2 s to 15 min, we demonstrate that the "hard" biomolecular corona is kinetically subdivided into two phases comprising a tightly bound layer of proteins interacting directly with the particle surface and a loosely associated protein layer. Understanding the influence of particle design parameters and biological factors on the corona composition, as well as its dynamic assembly, may facilitate more accurate prediction of corona formation and therefore assist in the design of advanced drug delivery vehicles.

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

Tellurium notebooks-An environment for reproducible dynamical modeling in systems biology.

PubMed

Medley, J Kyle; Choi, Kiri; König, Matthias; Smith, Lucian; Gu, Stanley; Hellerstein, Joseph; Sealfon, Stuart C; Sauro, Herbert M

2018-06-01

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.

Sensitivity of chemical weathering and dissolved carbon dynamics to hydrological conditions in a typical karst river

PubMed Central

Zhong, Jun; Li, Si-liang; Tao, Faxiang; Yue, Fujun; Liu, Cong-Qiang

2017-01-01

To better understand the mechanisms that hydrological conditions control chemical weathering and carbon dynamics in the large rivers, we investigated hydrochemistry and carbon isotopic compositions of dissolved inorganic carbon (DIC) based on high-frequency sampling in the Wujiang River draining the carbonate area in southwestern China. Concentrations of major dissolved solute do not strictly follow the dilution process with increasing discharge, and biogeochemical processes lead to variability in the concentration-discharge relationships. Temporal variations of dissolved solutes are closely related to weathering characteristics and hydrological conditions in the rainy seasons. The concentrations of dissolved carbon and the carbon isotopic compositions vary with discharge changes, suggesting that hydrological conditions and biogeochemical processes control dissolved carbon dynamics. Biological CO2 discharge and intense carbonate weathering by soil CO2 should be responsible for the carbon variability under various hydrological conditions during the high-flow season. The concentration of DICbio (DIC from biological sources) derived from a mixing model increases with increasing discharge, indicating that DICbio influx is the main driver of the chemostatic behaviors of riverine DIC in this typical karst river. The study highlights the sensitivity of chemical weathering and carbon dynamics to hydrological conditions in the riverine system. PMID:28220859

Biological and psychological rhythms: an integrative approach to rhythm disturbances in autistic disorder.

PubMed

Botbol, Michel; Cabon, Philippe; Kermarrec, Solenn; Tordjman, Sylvie

2013-09-01

Biological rhythms are crucial phenomena that are perfect examples of the adaptation of organisms to their environment. A considerable amount of work has described different types of biological rhythms (from circadian to ultradian), individual differences in their patterns and the complexity of their regulation. In particular, the regulation and maturation of the sleep-wake cycle have been thoroughly studied. Its desynchronization, both endogenous and exogenous, is now well understood, as are its consequences for cognitive impairments and health problems. From a completely different perspective, psychoanalysts have shown a growing interest in the rhythms of psychic life. This interest extends beyond the original focus of psychoanalysis on dreams and the sleep-wake cycle, incorporating central theoretical and practical psychoanalytic issues related to the core functioning of the psychic life: the rhythmic structures of drive dynamics, intersubjective developmental processes and psychic containment functions. Psychopathological and biological approaches to the study of infantile autism reveal the importance of specific biological and psychological rhythmic disturbances in this disorder. Considering data and hypotheses from both perspectives, this paper proposes an integrative approach to the study of these rhythmic disturbances and offers an etiopathogenic hypothesis based on this integrative approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

Thermal Change and the Dynamics of Multi-Host Parasite Life Cycles in Aquatic Ecosystems.

PubMed

Barber, Iain; Berkhout, Boris W; Ismail, Zalina

2016-10-01

Altered thermal regimes associated with climate change are impacting significantly on the physical, chemical, and biological characteristics of the Earth's natural ecosystems, with important implications for the biology of aquatic organisms. As well as impacting the biology of individual species, changing thermal regimes have the capacity to mediate ecological interactions between species, and the potential for climate change to impact host-parasite interactions in aquatic ecosystems is now well recognized. Predicting what will happen to the prevalence and intensity of infection of parasites with multiple hosts in their life cycles is especially challenging because the addition of each additional host dramatically increases the potential permutations of response. In this short review, we provide an overview of the diverse routes by which altered thermal regimes can impact the dynamics of multi-host parasite life cycles in aquatic ecosystems. In addition, we examine how experimentally amenable host-parasite systems are being used to determine the consequences of changing environmental temperatures for these different types of mechanism. Our overarching aim is to examine the potential of changing thermal regimes to alter not only the biology of hosts and parasites, but also the biology of interactions between hosts and parasites. We also hope to illustrate the complexity that is likely to be involved in making predictions about the dynamics of infection by multi-host parasites in thermally challenged aquatic ecosystems. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.

Protein-Protein Interactions of Azurin Complex by Coarse-Grained Simulations with a Gō-Like Model

NASA Astrophysics Data System (ADS)

Rusmerryani, Micke; Takasu, Masako; Kawaguchi, Kazutomo; Saito, Hiroaki; Nagao, Hidemi

Proteins usually perform their biological functions by forming a complex with other proteins. It is very important to study the protein-protein interactions since these interactions are crucial in many processes of a living organism. In this study, we develop a coarse grained model to simulate protein complex in liquid system. We carry out molecular dynamics simulations with topology-based potential interactions to simulate dynamical properties of Pseudomonas Aeruginosa azurin complex systems. Azurin is known to play an essential role as an anticancer agent and bind many important intracellular molecules. Some physical properties are monitored during simulation time to get a better understanding of the influence of protein-protein interactions to the azurin complex dynamics. These studies will provide valuable insights for further investigation on protein-protein interactions in more realistic system.

Metabolomics applied to the pancreatic islet.

PubMed

Gooding, Jessica R; Jensen, Mette V; Newgard, Christopher B

2016-01-01

Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Shedding (Incoherent) Light on Quantum Effects in Light-Induced Biological Processes.

PubMed

Brumer, Paul

2018-05-18

Light-induced processes that occur in nature, such as photosynthesis and photoisomerization in the first steps in vision, are often studied in the laboratory using coherent pulsed laser sources, which induce time-dependent coherent wavepacket molecule dynamics. Nature, however, uses stationary incoherent thermal radiation, such as sunlight, leading to a totally different molecular response, the time-independent steady state. It is vital to appreciate this difference in order to assess the role of quantum coherence effects in biological systems. Developments in this area are discussed in detail.