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.

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

Modelling and simulation techniques for membrane biology.

PubMed

Burrage, Kevin; Hancock, John; Leier, André; Nicolau, Dan V

2007-07-01

One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.

A new theoretical approach to analyze complex processes in cytoskeleton proteins.

PubMed

Li, Xin; Kolomeisky, Anatoly B

2014-03-20

Cytoskeleton proteins are filament structures that support a large number of important biological processes. These dynamic biopolymers exist in nonequilibrium conditions stimulated by hydrolysis chemical reactions in their monomers. Current theoretical methods provide a comprehensive picture of biochemical and biophysical processes in cytoskeleton proteins. However, the description is only qualitative under biologically relevant conditions because utilized theoretical mean-field models neglect correlations. We develop a new theoretical method to describe dynamic processes in cytoskeleton proteins that takes into account spatial correlations in the chemical composition of these biopolymers. Our approach is based on analysis of probabilities of different clusters of subunits. It allows us to obtain exact analytical expressions for a variety of dynamic properties of cytoskeleton filaments. By comparing theoretical predictions with Monte Carlo computer simulations, it is shown that our method provides a fully quantitative description of complex dynamic phenomena in cytoskeleton proteins under all conditions.

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.

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.

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.

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.

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.

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.

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

Dimension reduction for stochastic dynamical systems forced onto a manifold by large drift: a constructive approach with examples from theoretical biology

NASA Astrophysics Data System (ADS)

Parsons, Todd L.; Rogers, Tim

2017-10-01

Systems composed of large numbers of interacting agents often admit an effective coarse-grained description in terms of a multidimensional stochastic dynamical system, driven by small-amplitude intrinsic noise. In applications to biological, ecological, chemical and social dynamics it is common for these models to posses quantities that are approximately conserved on short timescales, in which case system trajectories are observed to remain close to some lower-dimensional subspace. Here, we derive explicit and general formulae for a reduced-dimension description of such processes that is exact in the limit of small noise and well-separated slow and fast dynamics. The Michaelis-Menten law of enzyme-catalysed reactions, and the link between the Lotka-Volterra and Wright-Fisher processes are explored as a simple worked examples. Extensions of the method are presented for infinite dimensional systems and processes coupled to non-Gaussian noise sources.

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

Modeling biochemical transformation processes and information processing with Narrator.

PubMed

Mandel, Johannes J; Fuss, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-03-27

Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from http://www.narrator-tool.org.

Modeling biochemical transformation processes and information processing with Narrator

PubMed Central

Mandel, Johannes J; Fuß, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-01-01

Background Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Results Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Conclusion Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from . PMID:17389034

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.

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...

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.

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

Coupling biology and oceanography in models.

PubMed

Fennel, W; Neumann, T

2001-08-01

The dynamics of marine ecosystems, i.e. the changes of observable chemical-biological quantities in space and time, are driven by biological and physical processes. Predictions of future developments of marine systems need a theoretical framework, i.e. models, solidly based on research and understanding of the different processes involved. The natural way to describe marine systems theoretically seems to be the embedding of chemical-biological models into circulation models. However, while circulation models are relatively advanced the quantitative theoretical description of chemical-biological processes lags behind. This paper discusses some of the approaches and problems in the development of consistent theories and indicates the beneficial potential of the coupling of marine biology and oceanography in models.

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment. PMID:29303160

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.

Forensic molecular pathology: its impacts on routine work, education and training.

PubMed

Maeda, Hitoshi; Ishikawa, Takaki; Michiue, Tomomi

2014-03-01

The major role of forensic pathology is the investigation of human death in relevance to social risk management to determine the cause and process of death, especially in violent and unexpected sudden deaths, which involve social and medicolegal issues of ultimate, personal and public concerns. In addition to the identification of victims and biological materials, forensic molecular pathology contributes to general explanation of the human death process and assessment of individual death on the basis of biological molecular evidence, visualizing dynamic functional changes involved in the dying process that cannot be detected by morphology (pathophysiological or molecular biological vital reactions); the genetic background (genomics), dynamics of gene expression (up-/down-regulation: transcriptomics) and vital phenomena, involving activated biological mediators and degenerative products (proteomics) as well as metabolic deterioration (metabolomics), are detected by DNA analysis, relative quantification of mRNA transcripts using real-time reverse transcription-PCR (RT-PCR), and immunohisto-/immunocytochemistry combined with biochemistry, respectively. Thus, forensic molecular pathology involves the application of omic medical sciences to investigate the genetic basis, and cause and process of death at the biological molecular level in the context of forensic pathology, that is, 'advanced molecular autopsy'. These procedures can be incorporated into routine death investigations as well as guidance, education and training programs in forensic pathology for 'dynamic assessment of the cause and process of death' on the basis of autopsy and laboratory data. Postmortem human data can also contribute to understanding patients' critical conditions in clinical management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Kramers problem in evolutionary strategies

NASA Astrophysics Data System (ADS)

Dunkel, J.; Ebeling, W.; Schimansky-Geier, L.; Hänggi, P.

2003-06-01

We calculate the escape rates of different dynamical processes for the case of a one-dimensional symmetric double-well potential. In particular, we compare the escape rates of a Smoluchowski process, i.e., a corresponding overdamped Brownian motion dynamics in a metastable potential landscape, with the escape rates obtained for a biologically motivated model known as the Fisher-Eigen process. The main difference between the two models is that the dynamics of the Smoluchowski process is determined by local quantities, whereas the Fisher-Eigen process is based on a global coupling (nonlocal interaction). If considered in the context of numerical optimization algorithms, both processes can be interpreted as archetypes of physically or biologically inspired evolutionary strategies. In this sense, the results discussed in this work are utile in order to evaluate the efficiency of such strategies with regard to the problem of surmounting various barriers. We find that a combination of both scenarios, starting with the Fisher-Eigen strategy, provides a most effective evolutionary strategy.

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

High Density or Urban Sprawl: What Works Best in Biology?

PubMed

Oreopoulos, John; Gray-Owen, Scott D; Yip, Christopher M

2017-02-28

With new approaches in imaging-from new tools or reagents to processing algorithms-come unique opportunities and challenges to our understanding of biological processes, structures, and dynamics. Although innovations in super-resolution imaging are affording novel perspectives into how molecules structurally associate and localize in response to, or in order to initiate, specific signaling events in the cell, questions arise as to how to interpret these observations in the context of biological function. Just as each neighborhood in a city has its own unique vibe, culture, and indeed density, recent work has shown that membrane receptor behavior and action is governed by their localization and association state. There is tremendous potential in developing strategies for tracking how the populations of these molecular neighborhoods change dynamically.

Dynamic pathway modeling of signal transduction networks: a domain-oriented approach.

PubMed

Conzelmann, Holger; Gilles, Ernst-Dieter

2008-01-01

Mathematical models of biological processes become more and more important in biology. The aim is a holistic understanding of how processes such as cellular communication, cell division, regulation, homeostasis, or adaptation work, how they are regulated, and how they react to perturbations. The great complexity of most of these processes necessitates the generation of mathematical models in order to address these questions. In this chapter we provide an introduction to basic principles of dynamic modeling and highlight both problems and chances of dynamic modeling in biology. The main focus will be on modeling of s transduction pathways, which requires the application of a special modeling approach. A common pattern, especially in eukaryotic signaling systems, is the formation of multi protein signaling complexes. Even for a small number of interacting proteins the number of distinguishable molecular species can be extremely high. This combinatorial complexity is due to the great number of distinct binding domains of many receptors and scaffold proteins involved in signal transduction. However, these problems can be overcome using a new domain-oriented modeling approach, which makes it possible to handle complex and branched signaling pathways.

"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…

Cell Migration Analysis: A Low-Cost Laboratory Experiment for Cell and Developmental Biology Courses Using Keratocytes from Fish Scales

ERIC Educational Resources Information Center

Prieto, Daniel; Aparicio, Gonzalo; Sotelo-Silveira, Jose R.

2017-01-01

Cell and developmental processes are complex, and profoundly dependent on spatial relationships that change over time. Innovative educational or teaching strategies are always needed to foster deep comprehension of these processes and their dynamic features. However, laboratory exercises in cell and developmental biology at the undergraduate level…

High-speed atomic force microscopy coming of age

NASA Astrophysics Data System (ADS)

Ando, Toshio

2012-02-01

High-speed atomic force microscopy (HS-AFM) is now materialized. It allows direct visualization of dynamic structural changes and dynamic processes of functioning biological molecules in physiological solutions, at high spatiotemporal resolution. Dynamic molecular events unselectively appear in detail in an AFM movie, facilitating our understanding of how biological molecules operate to function. This review describes a historical overview of technical development towards HS-AFM, summarizes elementary devices and techniques used in the current HS-AFM, and then highlights recent imaging studies. Finally, future challenges of HS-AFM studies are briefly discussed.

Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces.

PubMed

Rodrigo, Daniel; Tittl, Andreas; Ait-Bouziad, Nadine; John-Herpin, Aurelian; Limaj, Odeta; Kelly, Christopher; Yoo, Daehan; Wittenberg, Nathan J; Oh, Sang-Hyun; Lashuel, Hilal A; Altug, Hatice

2018-06-04

A multitude of biological processes are enabled by complex interactions between lipid membranes and proteins. To understand such dynamic processes, it is crucial to differentiate the constituent biomolecular species and track their individual time evolution without invasive labels. Here, we present a label-free mid-infrared biosensor capable of distinguishing multiple analytes in heterogeneous biological samples with high sensitivity. Our technology leverages a multi-resonant metasurface to simultaneously enhance the different vibrational fingerprints of multiple biomolecules. By providing up to 1000-fold near-field intensity enhancement over both amide and methylene bands, our sensor resolves the interactions of lipid membranes with different polypeptides in real time. Significantly, we demonstrate that our label-free chemically specific sensor can analyze peptide-induced neurotransmitter cargo release from synaptic vesicle mimics. Our sensor opens up exciting possibilities for gaining new insights into biological processes such as signaling or transport in basic research as well as provides a valuable toolkit for bioanalytical and pharmaceutical applications.

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

Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge.

PubMed

Zhang, Yuji

2015-01-01

Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly "watch" the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems. In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among biological processes enriched in co-expressed genes under different conditions. The enriched biological processes include translation elongation, nucleosome assembly, and retina development. These network dynamics provide new insights into the impact of 1α, 25-Dihydroxyvitamin D3 treatment in bone and cartilage development. We developed a network-based approach to analyzing the DEGs at different time points by integrating molecular interactions and gene ontology information. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α, 25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.

Model-Data Fusion to Test Hypothesized Drivers of Lake Carbon Cycling Reveals Importance of Physical Controls

NASA Astrophysics Data System (ADS)

Hararuk, Oleksandra; Zwart, Jacob A.; Jones, Stuart E.; Prairie, Yves; Solomon, Christopher T.

2018-03-01

Formal integration of models and data to test hypotheses about the processes controlling carbon dynamics in lakes is rare, despite the importance of lakes in the carbon cycle. We built a suite of models (n = 102) representing different hypotheses about lake carbon processing, fit these models to data from a north-temperate lake using data assimilation, and identified which processes were essential for adequately describing the observations. The hypotheses that we tested concerned organic matter lability and its variability through time, temperature dependence of biological decay, photooxidation, microbial dynamics, and vertical transport of water via hypolimnetic entrainment and inflowing density currents. The data included epilimnetic and hypolimnetic CO2 and dissolved organic carbon, hydrologic fluxes, carbon loads, gross primary production, temperature, and light conditions at high frequency for one calibration and one validation year. The best models explained 76-81% and 64-67% of the variability in observed epilimnetic CO2 and dissolved organic carbon content in the validation data. Accurately describing C dynamics required accounting for hypolimnetic entrainment and inflowing density currents, in addition to accounting for biological transformations. In contrast, neither photooxidation nor variable organic matter lability improved model performance. The temperature dependence of biological decay (Q10) was estimated at 1.45, significantly lower than the commonly assumed Q10 of 2. By confronting multiple models of lake C dynamics with observations, we identified processes essential for describing C dynamics in a temperate lake at daily to annual scales, while also providing a methodological roadmap for using data assimilation to further improve understanding of lake C cycling.

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.

Global, quantitative and dynamic mapping of protein subcellular localization.

PubMed

Itzhak, Daniel N; Tyanova, Stefka; Cox, Jürgen; Borner, Georg Hh

2016-06-09

Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

Relation between the electromagnetic processes in the near-Earth space and dynamics of the biological resources in Russian Arctic

NASA Astrophysics Data System (ADS)

Makarova, L. N.; Shirochkov, A. V.; Tumanov, I. L.

The start of the satellite era in the Space explorations led to new and more profound knowledge of the solar physics and the sources of its activity. From these points of view, it is worthy to examine again the relations between biological processes and the solar activity. We explore the relation between dynamics of the solar activity (including the solar wind) and changes in population of some species of Arctic fauna (lemmings, polar foxes, caribous, wolves, elks, etc.). The data include statistical rows of various lengths (30 80 years). The best correlation between two data sets is found when the solar wind dynamic pressure as well as variations of the total solar irradiance (i.e., level of the solar UV radiation) is taken as the space parameters. Probably the electromagnetic fields of space origin are an important factor determining dynamics of population of the Arctic fauna species.

Dynamic-landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh

2017-01-01

The increasing need to predict how climate change will impact wildlife species has exposed limitations in how well current approaches model important biological processes at scales at which those processes interact with climate. We used a comprehensive approach that combined recent advances in landscape and population modeling into dynamic-landscape metapopulation...

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.

Exploiting Fast-Variables to Understand Population Dynamics and Evolution

NASA Astrophysics Data System (ADS)

Constable, George W. A.; McKane, Alan J.

2018-07-01

We describe a continuous-time modelling framework for biological population dynamics that accounts for demographic noise. In the spirit of the methodology used by statistical physicists, transitions between the states of the system are caused by individual events while the dynamics are described in terms of the time-evolution of a probability density function. In general, the application of the diffusion approximation still leaves a description that is quite complex. However, in many biological applications one or more of the processes happen slowly relative to the system's other processes, and the dynamics can be approximated as occurring within a slow low-dimensional subspace. We review these time-scale separation arguments and analyse the more simple stochastic dynamics that result in a number of cases. We stress that it is important to retain the demographic noise derived in this way, and emphasise this point by showing that it can alter the direction of selection compared to the prediction made from an analysis of the corresponding deterministic model.

Exploiting Fast-Variables to Understand Population Dynamics and Evolution

NASA Astrophysics Data System (ADS)

Constable, George W. A.; McKane, Alan J.

2017-11-01

We describe a continuous-time modelling framework for biological population dynamics that accounts for demographic noise. In the spirit of the methodology used by statistical physicists, transitions between the states of the system are caused by individual events while the dynamics are described in terms of the time-evolution of a probability density function. In general, the application of the diffusion approximation still leaves a description that is quite complex. However, in many biological applications one or more of the processes happen slowly relative to the system's other processes, and the dynamics can be approximated as occurring within a slow low-dimensional subspace. We review these time-scale separation arguments and analyse the more simple stochastic dynamics that result in a number of cases. We stress that it is important to retain the demographic noise derived in this way, and emphasise this point by showing that it can alter the direction of selection compared to the prediction made from an analysis of the corresponding deterministic model.

Creative design inspired by biological knowledge: Technologies and methods

NASA Astrophysics Data System (ADS)

Tan, Runhua; Liu, Wei; Cao, Guozhong; Shi, Yuan

2018-05-01

Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions.

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.

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.

The dynamics of correlated novelties.

PubMed

Tria, F; Loreto, V; Servedio, V D P; Strogatz, S H

2014-07-31

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called "expanding the adjacent possible". The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.

The dynamics of correlated novelties

NASA Astrophysics Data System (ADS)

Tria, F.; Loreto, V.; Servedio, V. D. P.; Strogatz, S. H.

2014-07-01

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called ``expanding the adjacent possible''. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.

The dynamics of correlated novelties

PubMed Central

Tria, F.; Loreto, V.; Servedio, V. D. P.; Strogatz, S. H.

2014-01-01

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called “expanding the adjacent possible”. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution. PMID:25080941

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

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

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.

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

Aesthetic episodes, domains, and the mind. Comment on: "Move me, astonish me... delight my eyes and brain: The Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates" by Matthew Pelowski et al.

NASA Astrophysics Data System (ADS)

Jacobsen, Thomas

2017-07-01

Aesthetic episodes, as all behavior, are situated. They take place as an interaction of person and situation variables [1]. There are different artistic and aesthetic domains that afford, in part, mutual mental processes, but also fundamentally different, e.g., modality-specific ones [2]. For the infinite number of possible aesthetic episodes a plethora of component processes can be selectively combined and dynamically configured. Following psychophysics' pragmatic dualism of mind and body, this results in dynamically configured (neuro-) biological networks subserving these mental processes [1]. These are, of course, also subject to evolutionary, biological, historical, cultural, and social change [1,3].

HOW CAN BIOLOGICALLY-BASED MODELING OF ARSENIC KINETICS AND DYNAMICS INFORM THE RISK ASSESSMENT PROCESS?

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic met...

Quantum Information Biology: From Information Interpretation of Quantum Mechanics to Applications in Molecular Biology and Cognitive Psychology

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2015-10-01

We discuss foundational issues of quantum information biology (QIB)—one of the most successful applications of the quantum formalism outside of physics. QIB provides a multi-scale model of information processing in bio-systems: from proteins and cells to cognitive and social systems. This theory has to be sharply distinguished from "traditional quantum biophysics". The latter is about quantum bio-physical processes, e.g., in cells or brains. QIB models the dynamics of information states of bio-systems. We argue that the information interpretation of quantum mechanics (its various forms were elaborated by Zeilinger and Brukner, Fuchs and Mermin, and D' Ariano) is the most natural interpretation of QIB. Biologically QIB is based on two principles: (a) adaptivity; (b) openness (bio-systems are fundamentally open). These principles are mathematically represented in the framework of a novel formalism— quantum adaptive dynamics which, in particular, contains the standard theory of open quantum systems.

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.

Multiple objects tracking in fluorescence microscopy.

PubMed

Kalaidzidis, Yannis

2009-01-01

Many processes in cell biology are connected to the movement of compact entities: intracellular vesicles and even single molecules. The tracking of individual objects is important for understanding cellular dynamics. Here we describe the tracking algorithms which have been developed in the non-biological fields and successfully applied to object detection and tracking in biological applications. The characteristics features of the different algorithms are compared.

Multiscale Physical and Biological Dynamics in the Philippines Archipelago: Predictions and Processes

DTIC Science & Technology

2011-01-01

for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 2010 2. REPORT...TYPE 3. DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE MultiscalePhysical and Biological Dynamics in the Philippines Archipelago...chemistry,  and   radioisotopes  in  the  Southeast  Asian  Basins.  J.  Geophys.  Res.,  91:14,345-­‐14,354.   Cabrera

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.

Individual differences in the perception of biological motion and fragmented figures are not correlated

PubMed Central

Jung, Eunice L.; Zadbood, Asieh; Lee, Sang-Hun; Tomarken, Andrew J.; Blake, Randolph

2013-01-01

We live in a cluttered, dynamic visual environment that poses a challenge for the visual system: for objects, including those that move about, to be perceived, information specifying those objects must be integrated over space and over time. Does a single, omnibus mechanism perform this grouping operation, or does grouping depend on separate processes specialized for different feature aspects of the object? To address this question, we tested a large group of healthy young adults on their abilities to perceive static fragmented figures embedded in noise and to perceive dynamic point-light biological motion figures embedded in dynamic noise. There were indeed substantial individual differences in performance on both tasks, but none of the statistical tests we applied to this data set uncovered a significant correlation between those performance measures. These results suggest that the two tasks, despite their superficial similarity, require different segmentation and grouping processes that are largely unrelated to one another. Whether those processes are embodied in distinct neural mechanisms remains an open question. PMID:24198799

Individual differences in the perception of biological motion and fragmented figures are not correlated.

PubMed

Jung, Eunice L; Zadbood, Asieh; Lee, Sang-Hun; Tomarken, Andrew J; Blake, Randolph

2013-01-01

WE LIVE IN A CLUTTERED, DYNAMIC VISUAL ENVIRONMENT THAT POSES A CHALLENGE FOR THE VISUAL SYSTEM: for objects, including those that move about, to be perceived, information specifying those objects must be integrated over space and over time. Does a single, omnibus mechanism perform this grouping operation, or does grouping depend on separate processes specialized for different feature aspects of the object? To address this question, we tested a large group of healthy young adults on their abilities to perceive static fragmented figures embedded in noise and to perceive dynamic point-light biological motion figures embedded in dynamic noise. There were indeed substantial individual differences in performance on both tasks, but none of the statistical tests we applied to this data set uncovered a significant correlation between those performance measures. These results suggest that the two tasks, despite their superficial similarity, require different segmentation and grouping processes that are largely unrelated to one another. Whether those processes are embodied in distinct neural mechanisms remains an open question.

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.

How Can Biologically-Based Modeling of Arsenic Kinetics and Dynamics Inform the Risk Assessment Process? -- ETD

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic me...

Coupled Modeling of Rhizosphere and Reactive Transport Processes

NASA Astrophysics Data System (ADS)

Roque-Malo, S.; Kumar, P.

2017-12-01

The rhizosphere, as a bio-diverse plant root-soil interface, hosts many hydrologic and biochemical processes, including nutrient cycling, hydraulic redistribution, and soil carbon dynamics among others. The biogeochemical function of root networks, including the facilitation of nutrient cycling through absorption and rhizodeposition, interaction with micro-organisms and fungi, contribution to biomass, etc., plays an important role in myriad Critical Zone processes. Despite this knowledge, the role of the rhizosphere on watershed-scale ecohydrologic functions in the Critical Zone has not been fully characterized, and specifically, the extensive capabilities of reactive transport models (RTMs) have not been applied to these hydrobiogeochemical dynamics. This study uniquely links rhizospheric processes with reactive transport modeling to couple soil biogeochemistry, biological processes, hydrologic flow, hydraulic redistribution, and vegetation dynamics. Key factors in the novel modeling approach are: (i) bi-directional effects of root-soil interaction, such as simultaneous root exudation and nutrient absorption; (ii) multi-state biomass fractions in soil (i.e. living, dormant, and dead biological and root materials); (iii) expression of three-dimensional fluxes to represent both vertical and lateral interconnected flows and processes; and (iv) the potential to include the influence of non-stationary external forcing and climatic factors. We anticipate that the resulting model will demonstrate the extensive effects of plant root dynamics on ecohydrologic functions at the watershed scale and will ultimately contribute to a better characterization of efflux from both agricultural and natural systems.

Statistical inference for noisy nonlinear ecological dynamic systems.

PubMed

Wood, Simon N

2010-08-26

Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

Parameterization and Sensitivity Analysis of a Complex Simulation Model for Mosquito Population Dynamics, Dengue Transmission, and Their Control

PubMed Central

Ellis, Alicia M.; Garcia, Andres J.; Focks, Dana A.; Morrison, Amy C.; Scott, Thomas W.

2011-01-01

Models can be useful tools for understanding the dynamics and control of mosquito-borne disease. More detailed models may be more realistic and better suited for understanding local disease dynamics; however, evaluating model suitability, accuracy, and performance becomes increasingly difficult with greater model complexity. Sensitivity analysis is a technique that permits exploration of complex models by evaluating the sensitivity of the model to changes in parameters. Here, we present results of sensitivity analyses of two interrelated complex simulation models of mosquito population dynamics and dengue transmission. We found that dengue transmission may be influenced most by survival in each life stage of the mosquito, mosquito biting behavior, and duration of the infectious period in humans. The importance of these biological processes for vector-borne disease models and the overwhelming lack of knowledge about them make acquisition of relevant field data on these biological processes a top research priority. PMID:21813844

Global, quantitative and dynamic mapping of protein subcellular localization

PubMed Central

Itzhak, Daniel N; Tyanova, Stefka; Cox, Jürgen; Borner, Georg HH

2016-01-01

Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology. DOI: http://dx.doi.org/10.7554/eLife.16950.001 PMID:27278775

Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.

Economic Analysis of Biological Invasions in Forests

Treesearch

Tomas P. Holmes; Julian Aukema; Jeffrey Englin; Robert G. Haight; Kent Kovacs; Brian Leung

2014-01-01

Biological invasions of native forests by nonnative pests result from complex stochastic processes that are difficult to predict. Although economic optimization models describe efficient controls across the stages of an invasion, the ability to calibrate such models is constrained by lack of information on pest population dynamics and consequent economic damages. Here...

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.

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.

Aggregation of flexible polyelectrolytes: Phase diagram and dynamics.

PubMed

Tom, Anvy Moly; Rajesh, R; Vemparala, Satyavani

2017-10-14

Similarly charged polymers in solution, known as polyelectrolytes, are known to form aggregated structures in the presence of oppositely charged counterions. Understanding the dependence of the equilibrium phases and the dynamics of the process of aggregation on parameters such as backbone flexibility and charge density of such polymers is crucial for insights into various biological processes which involve biological polyelectrolytes such as protein, DNA, etc. Here, we use large-scale coarse-grained molecular dynamics simulations to obtain the phase diagram of the aggregated structures of flexible charged polymers and characterize the morphology of the aggregates as well as the aggregation dynamics, in the presence of trivalent counterions. Three different phases are observed depending on the charge density: no aggregation, a finite bundle phase where multiple small aggregates coexist with a large aggregate and a fully phase separated phase. We show that the flexibility of the polymer backbone causes strong entanglement between charged polymers leading to additional time scales in the aggregation process. Such slowing down of the aggregation dynamics results in the exponent, characterizing the power law decay of the number of aggregates with time, to be dependent on the charge density of the polymers. These results are contrary to those obtained for rigid polyelectrolytes, emphasizing the role of backbone flexibility.

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.

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.

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

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.

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

Biological conservation law as an emerging functionality in dynamical neuronal networks.

PubMed

Podobnik, Boris; Jusup, Marko; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M; Stanley, H Eugene

2017-11-07

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Biological conservation law as an emerging functionality in dynamical neuronal networks

PubMed Central

Podobnik, Boris; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M.

2017-01-01

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective. PMID:29078286

FATE AND EFFECTS OF PHOSPHORUS ADDITIONS IN SOILS UNDER N2-FIXING RED ALDER

EPA Science Inventory

Soil phosphorus (P) dynamics are controlled by the interaction of geochemical, biochemical and biological processes, and changing species composition or management may alter the relative importance of these processes. We examined the role of these processes in two plantations of...

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

Modelling fungal growth in heterogeneous soil: analyses of the effect of soil physical structure on fungal community dynamics

NASA Astrophysics Data System (ADS)

Falconer, R.; Radoslow, P.; Grinev, D.; Otten, W.

2009-04-01

Fungi play a pivital role in soil ecosystems contributing to plant productivity. The underlying soil physical and biological processes responsible for community dynamics are interrelated and, at present, poorly understood. If these complex processes can be understood then this knowledge can be managed with an aim to providing more sustainable agriculture. Our understanding of microbial dynamics in soil has long been hampered by a lack of a theoretical framework and difficulties in observation and quantification. We will demonstrate how the spatial and temporal dynamics of fungi in soil can be understood by linking mathematical modelling with novel techniques that visualise the complex structure of the soil. The combination of these techniques and mathematical models opens up new possibilities to understand how the physical structure of soil affects fungal colony dynamics and also how fungal dynamics affect soil structure. We will quantify, using X ray tomography, soil structure for a range of artificially prepared microcosms. We characterise the soil structures using soil metrics such as porosity, fractal dimension, and the connectivity of the pore volume. Furthermore we will use the individual based fungal colony growth model of Falconer et al. 2005, which is based on the physiological processes of fungi, to assess the effect of soil structure on microbial dynamics by qualifying biomass abundances and distributions. We demonstrate how soil structure can critically affect fungal species interactions with consequences for biological control and fungal biodiversity.

Object-based attentional modulation of biological motion processing: spatiotemporal dynamics using functional magnetic resonance imaging and electroencephalography.

PubMed

Safford, Ashley S; Hussey, Elizabeth A; Parasuraman, Raja; Thompson, James C

2010-07-07

Although it is well documented that the ability to perceive biological motion is mediated by the lateral temporal cortex, whether and when neural activity in this brain region is modulated by attention is unknown. In particular, it is unclear whether the processing of biological motion requires attention or whether such stimuli are processed preattentively. Here, we used functional magnetic resonance imaging, high-density electroencephalography, and cortically constrained source estimation methods to investigate the spatiotemporal effects of attention on the processing of biological motion. Directing attention to tool motion in overlapping movies of biological motion and tool motion suppressed the blood oxygenation level-dependent (BOLD) response of the right superior temporal sulcus (STS)/middle temporal gyrus (MTG), while directing attention to biological motion suppressed the BOLD response of the left inferior temporal sulcus (ITS)/MTG. Similarly, category-based modulation of the cortical current source density estimates from the right STS/MTG and left ITS was observed beginning at approximately 450 ms following stimulus onset. Our results indicate that the cortical processing of biological motion is strongly modulated by attention. These findings argue against preattentive processing of biological motion in the presence of stimuli that compete for attention. Our findings also suggest that the attention-based segregation of motion category-specific responses only emerges relatively late (several hundred milliseconds) in processing.

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.

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.

Understanding past, contemporary, and future dynamics of plants, populations, and communities using Sonoran Desert winter annuals.

PubMed

Huxman, Travis E; Kimball, Sarah; Angert, Amy L; Gremer, Jennifer R; Barron-Gafford, Greg A; Venable, D Lawrence

2013-07-01

Global change requires plant ecologists to predict future states of biological diversity to aid the management of natural communities, thus introducing a number of significant challenges. One major challenge is considering how the many interacting features of biological systems, including ecophysiological processes, plant life histories, and species interactions, relate to performance in the face of a changing environment. We have employed a functional trait approach to understand the individual, population, and community dynamics of a model system of Sonoran Desert winter annual plants. We have used a comprehensive approach that connects physiological ecology and comparative biology to population and community dynamics, while emphasizing both ecological and evolutionary processes. This approach has led to a fairly robust understanding of past and contemporary dynamics in response to changes in climate. In this community, there is striking variation in physiological and demographic responses to both precipitation and temperature that is described by a trade-off between water-use efficiency (WUE) and relative growth rate (RGR). This community-wide trade-off predicts both the demographic and life history variation that contribute to species coexistence. Our framework has provided a mechanistic explanation to the recent warming, drying, and climate variability that has driven a surprising shift in these communities: cold-adapted species with more buffered population dynamics have increased in relative abundance. These types of comprehensive approaches that acknowledge the hierarchical nature of biology may be especially useful in aiding prediction. The emerging, novel and nonstationary climate constrains our use of simplistic statistical representations of past plant behavior in predicting the future, without understanding the mechanistic basis of change.

Ergodicity convergence test suggests telomere motion obeys fractional dynamics

NASA Astrophysics Data System (ADS)

Kepten, E.; Bronshtein, I.; Garini, Y.

2011-04-01

Anomalous diffusion, observed in many biological processes, is a generalized description of a wide variety of processes, all obeying the same law of mean-square displacement. Identifying the basic mechanisms of these observations is important for deducing the nature of the biophysical systems measured. We implement a previously suggested method for distinguishing between fractional Langevin dynamics, fractional Brownian motion, and continuous time random walk based on the ergodic nature of the data. We apply the method together with the recently suggested P-variation test and the displacement correlation to the lately measured dynamics of telomeres in the nucleus of mammalian cells and find strong evidence that the telomeres motion obeys fractional dynamics. The ergodic dynamics are observed experimentally to fit fractional Brownian or Langevin dynamics.

Visualizing time-related data in biology, a review

PubMed Central

Secrier, Maria; Schneider, Reinhard

2014-01-01

Time is of the essence in biology as in so much else. For example, monitoring disease progression or the timing of developmental defects is important for the processes of drug discovery and therapy trials. Furthermore, an understanding of the basic dynamics of biological phenomena that are often strictly time regulated (e.g. circadian rhythms) is needed to make accurate inferences about the evolution of biological processes. Recent advances in technologies have enabled us to measure timing effects more accurately and in more detail. This has driven related advances in visualization and analysis tools that try to effectively exploit this data. Beyond timeline plots, notable attempts at more involved temporal interpretation have been made in recent years, but awareness of the available resources is still limited within the scientific community. Here, we review some advances in biological visualization of time-driven processes and consider how they aid data analysis and interpretation. PMID:23585583

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.

Evolution and social epidemiology.

PubMed

Nishi, Akihiro

2015-11-01

Evolutionary biology, which aims to explain the dynamic process of shaping the diversity of life, has not yet significantly affected thinking in social epidemiology. Current challenges in social epidemiology include understanding how social exposures can affect our biology, explaining the dynamics of society and health, and designing better interventions that are mindful of the impact of exposures during critical periods. I review how evolutionary concepts and tools, such as fitness gradient in cultural evolution, evolutionary game theory, and contemporary evolution in cancer, can provide helpful insights regarding social epidemiology. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Virtual Cell Animation Collection: Tools for Teaching Molecular and Cellular Biology

PubMed Central

Reindl, Katie M.; White, Alan R.; Johnson, Christina; Vender, Bradley; Slator, Brian M.; McClean, Phillip

2015-01-01

A cell is a minifactory in which structures and molecules are assembled, rearranged, disassembled, packaged, sorted, and transported. Because cellular structures and molecules are invisible to the human eye, students often have difficulty conceptualizing the dynamic nature of cells that function at multiple scales across time and space. To represent these dynamic cellular processes, the Virtual Cell Productions team at North Dakota State University develops freely available multimedia materials to support molecular and cellular biology learning inside and outside the high school and university classroom. PMID:25856580

Features of Knowledge Building in Biology: Understanding Undergraduate Students' Ideas about Molecular Mechanisms

ERIC Educational Resources Information Center

Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S.

2016-01-01

Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections…

Supporting Upper-Level Undergraduate Students in Building a Systems Perspective in a Botany Course

ERIC Educational Resources Information Center

Zangori, Laura; Koontz, Jason A.

2017-01-01

Undergraduate biology majors require biological literacy about the critical and dynamic relationships between plants and ecosystems and the effect human-made processes have on these systems. To support students in understanding systems relationships, we redesigned an undergraduate botany course using an ecological framework and embedded systems…

Poissonian steady states: from stationary densities to stationary intensities.

PubMed

Eliazar, Iddo

2012-10-01

Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.

Poissonian steady states: From stationary densities to stationary intensities

NASA Astrophysics Data System (ADS)

Eliazar, Iddo

2012-10-01

Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.

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

Quantitative phase imaging characterization of tumor-associated blood vessel formation on a chip

NASA Astrophysics Data System (ADS)

Guo, Peng; Huang, Jing; Moses, Marsha A.

2018-02-01

Angiogenesis, the formation of new blood vessels from existing ones, is a biological process that has an essential role in solid tumor growth, development, and progression. Recent advances in Lab-on-a-Chip technology has created an opportunity for scientists to observe endothelial cell (EC) behaviors during the dynamic process of angiogenesis using a simple and economical in vitro platform that recapitulates in vivo blood vessel formation. Here, we use quantitative phase imaging (QPI) microscopy to continuously and non-invasively characterize the dynamic process of tumor cell-induced angiogenic sprout formation on a microfluidic chip. The live tumor cell-induced angiogenic sprouts are generated by multicellular endothelial sprouting into 3 dimensional (3D) Matrigel using human umbilical vein endothelial cells (HUVECs). By using QPI, we quantitatively measure a panel of cellular morphological and behavioral parameters of each individual EC participating in this sprouting. In this proof-of-principle study, we demonstrate that QPI is a powerful tool that can provide real-time quantitative analysis of biological processes in in vitro 3D biomimetic devices, which, in turn, can improve our understanding of the biology underlying functional tissue engineering.

Emulating short-term synaptic dynamics with memristive devices

NASA Astrophysics Data System (ADS)

Berdan, Radu; Vasilaki, Eleni; Khiat, Ali; Indiveri, Giacomo; Serb, Alexandru; Prodromakis, Themistoklis

2016-01-01

Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.

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

Modeling nitrous oxide production during biological nitrogen removal via nitrification and denitrification: extensions to the general ASM models.

PubMed

Ni, Bing-Jie; Ruscalleda, Maël; Pellicer-Nàcher, Carles; Smets, Barth F

2011-09-15

Nitrous oxide (N(2)O) can be formed during biological nitrogen (N) removal processes. In this work, a mathematical model is developed that describes N(2)O production and consumption during activated sludge nitrification and denitrification. The well-known ASM process models are extended to capture N(2)O dynamics during both nitrification and denitrification in biological N removal. Six additional processes and three additional reactants, all involved in known biochemical reactions, have been added. The validity and applicability of the model is demonstrated by comparing simulations with experimental data on N(2)O production from four different mixed culture nitrification and denitrification reactor study reports. Modeling results confirm that hydroxylamine oxidation by ammonium oxidizers (AOB) occurs 10 times slower when NO(2)(-) participates as final electron acceptor compared to the oxic pathway. Among the four denitrification steps, the last one (N(2)O reduction to N(2)) seems to be inhibited first when O(2) is present. Overall, N(2)O production can account for 0.1-25% of the consumed N in different nitrification and denitrification systems, which can be well simulated by the proposed model. In conclusion, we provide a modeling structure, which adequately captures N(2)O dynamics in autotrophic nitrification and heterotrophic denitrification driven biological N removal processes and which can form the basis for ongoing refinements.

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.

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.

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.

New Interest in Wild Forest Products in Europe as an Expression of Biocultural Dynamics.

PubMed

Wiersum, K F

2017-01-01

In Europe, interest in wild forest products is increasing. Such products may be interpreted in a biological sense as deriving from autonomously growing forest species or in a biocultural sense as reflecting dynamics in human living with biodiversity through re-wilding of earlier domesticated species. In this article I elaborate the idea that the new interests reflect biocultural dynamics. First, I identify these dynamics as involving both domestication and re-wilding and characterize these processes as involving biological, environmental, and cultural dimensions. Next, I present a comparative review of two approaches to re-wilding forest production in the Netherlands: meat production from new types of natural grazing systems, and food production from plants re-introduced to the wild. The first approach is based on the stimulation of naturally occurring ecological processes and the second on the stimulation of new forms of experiencing bio-cultural heritage. The examples demonstrate that the new interests in wild forest products involve both a return to earlier stages of domestication in an ecological sense and a new phase of acculturation to evolving socio-cultural conditions.

Atomistic details of protein dynamics and the role of hydration water

DOE PAGES

Khodadadi, Sheila; Sokolov, Alexei P.

2016-05-04

The importance of protein dynamics for their biological activity is nowwell recognized. Different experimental and computational techniques have been employed to study protein dynamics, hierarchy of different processes and the coupling between protein and hydration water dynamics. But, understanding the atomistic details of protein dynamics and the role of hydration water remains rather limited. Based on overview of neutron scattering, molecular dynamic simulations, NMR and dielectric spectroscopy results we present a general picture of protein dynamics covering time scales from faster than ps to microseconds and the influence of hydration water on different relaxation processes. Internal protein dynamics spread overmore » a wide time range fromfaster than picosecond to longer than microseconds. We suggest that the structural relaxation in hydrated proteins appears on the microsecond time scale, while faster processes present mostly motion of side groups and some domains. Hydration water plays a crucial role in protein dynamics on all time scales. It controls the coupled protein-hydration water relaxation on 10 100 ps time scale. Our process defines the friction for slower protein dynamics. Analysis suggests that changes in amount of hydration water affect not only general friction, but also influence significantly the protein's energy landscape.« less

Atomistic details of protein dynamics and the role of hydration water

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

Khodadadi, Sheila; Sokolov, Alexei P.

The importance of protein dynamics for their biological activity is nowwell recognized. Different experimental and computational techniques have been employed to study protein dynamics, hierarchy of different processes and the coupling between protein and hydration water dynamics. But, understanding the atomistic details of protein dynamics and the role of hydration water remains rather limited. Based on overview of neutron scattering, molecular dynamic simulations, NMR and dielectric spectroscopy results we present a general picture of protein dynamics covering time scales from faster than ps to microseconds and the influence of hydration water on different relaxation processes. Internal protein dynamics spread overmore » a wide time range fromfaster than picosecond to longer than microseconds. We suggest that the structural relaxation in hydrated proteins appears on the microsecond time scale, while faster processes present mostly motion of side groups and some domains. Hydration water plays a crucial role in protein dynamics on all time scales. It controls the coupled protein-hydration water relaxation on 10 100 ps time scale. Our process defines the friction for slower protein dynamics. Analysis suggests that changes in amount of hydration water affect not only general friction, but also influence significantly the protein's energy landscape.« less

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.

Dynamics of relaxation to a stationary state for interacting molecular motors

NASA Astrophysics Data System (ADS)

Gomes, Luiza V. F.; Kolomeisky, Anatoly B.

2018-01-01

Motor proteins are active enzymatic molecules that drive a variety of biological processes, including transfer of genetic information, cellular transport, cell motility and muscle contraction. It is known that these biological molecular motors usually perform their cellular tasks by acting collectively, and there are interactions between individual motors that specify the overall collective behavior. One of the fundamental issues related to the collective dynamics of motor proteins is the question if they function at stationary-state conditions. To investigate this problem, we analyze a relaxation to the stationary state for the system of interacting molecular motors. Our approach utilizes a recently developed theoretical framework, which views the collective dynamics of motor proteins as a totally asymmetric simple exclusion process of interacting particles, where interactions are taken into account via a thermodynamically consistent approach. The dynamics of relaxation to the stationary state is analyzed using a domain-wall method that relies on a mean-field description, which takes into account some correlations. It is found that the system quickly relaxes for repulsive interactions, while attractive interactions always slow down reaching the stationary state. It is also predicted that for some range of parameters the fastest relaxation might be achieved for a weak repulsive interaction. Our theoretical predictions are tested with Monte Carlo computer simulations. The implications of our findings for biological systems are briefly discussed.

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.

The dynamic lift of developmental process.

PubMed

Smith, Linda B; Breazeal, Cynthia

2007-01-01

What are the essential properties of human intelligence, currently unparalleled in its power relative to other biological forms and relative to artificial forms of intelligence? We suggest that answering this question depends critically on understanding developmental process. This paper considers three principles potentially essential to building human-like intelligence: the heterogeneity of the component processes, the embedding of development in a social world, and developmental processes that change the cognitive system as a function of the history of soft-assemblies of these heterogeneous processes in specific tasks. The paper uses examples from human development and from developmental robotics to show how these processes also may underlie biological intelligence and enable us to generate more advanced forms of artificial intelligence.

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.

Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific Phenomena

NASA Astrophysics Data System (ADS)

Corredor, Javier; Gaydos, Matthew; Squire, Kurt

2014-06-01

This article explores how learning biological concepts can be facilitated by playing a video game that depicts interactions and processes at the subcellular level. Particularly, this article reviews the effects of a real-time strategy game that requires players to control the behavior of a virus and interact with cell structures in a way that resembles the actual behavior of biological agents. The evaluation of the video game presented here aims at showing that video games have representational advantages that facilitate the construction of dynamic mental models. Ultimately, the article shows that when video game's characteristics come in contact with expert knowledge during game design, the game becomes an excellent medium for supporting the learning of disciplinary content related to dynamic processes. In particular, results show that students who participated in a game-based intervention aimed at teaching biology described a higher number of temporal-dependent interactions as measured by the coding of verbal protocols and drawings than students who used texts and diagrams to learn the same topic.

The Dynamic Lift of Developmental Process

ERIC Educational Resources Information Center

Smith, Linda B.; Breazeal, Cynthia

2007-01-01

What are the essential properties of human intelligence, currently unparalleled in its power relative to other biological forms and relative to artificial forms of intelligence? We suggest that answering this question depends critically on understanding developmental process. This paper considers three principles potentially essential to building…

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software.

PubMed

Zuckerman, Daniel M; Chong, Lillian T

2017-05-22

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling-the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes-protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.

Some uses of wavelets for imaging dynamic processes in live cochlear structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, J.

2007-09-01

A variety of image and signal processing algorithms based on wavelet filtering tools have been developed during the last few decades, that are well adapted to the experimental variability typically encountered in live biological microscopy. A number of processing tools are reviewed, that use wavelets for adaptive image restoration and for motion or brightness variation analysis by optical flow computation. The usefulness of these tools for biological imaging is illustrated in the context of the restoration of images of the inner ear and the analysis of cochlear motion patterns in two and three dimensions. I also report on recent work that aims at capturing fluorescence intensity changes associated with vesicle dynamics at synaptic zones of sensory hair cells. This latest application requires one to separate the intensity variations associated with the physiological process under study from the variations caused by motion of the observed structures. A wavelet optical flow algorithm for doing this is presented, and its effectiveness is demonstrated on artificial and experimental image sequences.

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.

Shelf Sea Oxygen Dynamics: A year of Glider Measurements

NASA Astrophysics Data System (ADS)

Williams, C. A. J.; Palmer, M.; Mahaffey, C.; Jardine, J.

2016-02-01

Oxygen (O) is involved in most biogeochemical processes in the ocean, and dissolved oxygen (DO) is a well-established indicator for biological activity via the estimate of apparent oxygen utilisation (AOU). In the deep waters of the open ocean, the AOU provides a valuable insight into the ocean's biological carbon pump. However, in the physically dynamic and highly productive shallow shelf seas, interpretation of the O distribution and the magnitude of AOU is complex. Physical processes, such as diapycnal mixing, entrainment and horizontal advection act to ventilate waters below the thermocline and thus increase O and decrease AOU. In contrast, biological remineralisation of organic material below the thermocline will consume O and increase AOU. Here, we use 1 year of high-resolution data from >20 glider deployments in the seasonally stratified NW European Shelf Sea to identify and quantify the physical and biological processes that control the DO distribution and magnitude of AOU in shelf seas. A 200km transect between the shelf edge and the central Celtic Sea (CCS) was repeated between November 2014 and August 2015, thus capturing key periods in the seasonal cycling in shelf seas, specifically the onset of stratification, the spring bloom, stratified summer period and breakdown of stratification. The gliders collected data for DO, temperature, salinity, chlorophyll fluorescence, CDOM, backscatter and turbulence. In addition, direct measurements of turbulent dissipation from the Ocean Microstructure Glider deployed during the campaign provided estimates of mixing at CCS and the shelf break, allowing accurate quantification of the vertical fluxes of O. At the end of the stratified period the DO concentration was higher and AOU was lower at the shelf break (80 µM) compared to at CCS on shelf (>95 µM) (Fig 1). Estimates of vertical DO fluxes indicate that this horizontal variation in DO and AOU was partly attributed to enhanced mixing via internal waves at the shelf break ventilating waters below the thermocline, rather than decreased biological O consumption at the shelf break. Taking into consideration physical mixing processes, we provide a robust estimate of the biological O consumption over a seasonal cycle and highlight the need to consider the impact of physical processes on O dynamics in shallow shelf sea systems.

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.

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

DOE PAGES

Wang, Zhongrui; Joshi, Saumil; Savel’ev, Sergey E.; ...

2016-09-26

The accumulation and extrusion of Ca 2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. In order to emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca 2+, respectively. Furthermore, the diffusive memristormore » and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.« less

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.

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

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.

Extracting biomarkers of commitment to cancer development: potential role of vibrational spectroscopy in systems biology.

PubMed

Theophilou, Georgios; Paraskevaidi, Maria; Lima, Kássio M G; Kyrgiou, Maria; Martin-Hirsch, Pierre L; Martin, Francis L

2015-05-01

The complex processes driving cancer have so far impeded the discovery of dichotomous biomarkers associated with its initiation and progression. Reductionist approaches utilizing 'omics' technologies have met some success in identifying molecular alterations associated with carcinogenesis. Systems biology is an emerging science that combines high-throughput investigation techniques to define the dynamic interplay between regulatory biological systems in response to internal and external cues. Vibrational spectroscopy has the potential to play an integral role within systems biology research approaches. It is capable of examining global models of carcinogenesis by scrutinizing chemical bond alterations within molecules. The application of infrared or Raman spectroscopic approaches coupled with computational analysis under the systems biology umbrella can assist the transition of biomarker research from the molecular level to the system level. The comprehensive representation of carcinogenesis as a multilevel biological process will inevitably revolutionize cancer-related healthcare by personalizing risk prediction and prevention.

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.

Effects of predation efficiencies on the dynamics of a tritrophic food chain.

PubMed

Cassinari, Maria Paola; Groppi, Maria; Tebaldi, Claudio

2007-07-01

In this paper the dynamics of a tritrophic food chain (resource, consumer, top predator) is investigated, with particular attention not only to equilibrium states but also to cyclic behaviours that the system may exhibit. The analysis is performed in terms of two bifurcation parameters, denoted by p and q, which measure the efficiencies of the interaction processes. The persistence of the system is discussed, characterizing in the (p; q) plane the regions of existence and stability of biologically significant steady states and those of existence of limit cycles. The bifurcations occurring are discussed, and their implications with reference to biological control problems are considered. Examples of the rich dynamics exhibited by the model, including a chaotic regime, are described.

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.

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.

Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

NASA Astrophysics Data System (ADS)

Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

2016-11-01

The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

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.

Long-term fragmentation effects on the distribution and dynamics of canopy gaps in a tropical montane forest

Treesearch

Nicholas R. Vaughn; Gregory P. Asner; Christian P. Giardina

2015-01-01

Fragmentation alters forest canopy structure through various mechanisms, which in turn drive subsequent changes to biogeochemical processes and biological diversity. Using repeated airborne LiDAR (Light Detection and Ranging) mappings, we investigated the size distribution and dynamics of forest canopy gaps across a topical montane forest landscape in Hawaii naturally...

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

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.

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.

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.

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

PubMed

Bettenbühl, Mario; Rusconi, Marco; Engbert, Ralf; Holschneider, Matthias

2012-01-01

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

Microbial Community Structures and Dynamics in the O3/BAC Drinking Water Treatment Process

PubMed Central

Tian, Jian; Lu, Jun; Zhang, Yu; Li, Jian-Cheng; Sun, Li-Chen; Hu, Zhang-Li

2014-01-01

Effectiveness of drinking water treatment, in particular pathogen control during the water treatment process, is always a major public health concern. In this investigation, the application of PCR-DGGE technology to the analysis of microbial community structures and dynamics in the drinking water treatment process revealed several dominant microbial populations including: α-Proteobacteria, β-Proteobacteria, γ-Proteobacteria, Bacteroidetes, Actinobacteria Firmicutes and Cyanobacteria. α-Proteobacteria and β-Proteobacteria were the dominant bacteria during the whole process. Bacteroidetes and Firmicutes were the dominant bacteria before and after treatment, respectively. Firmicutes showed season-dependent changes in population dynamics. Importantly, γ-Proteobacteria, which is a class of medically important bacteria, was well controlled by the O3/biological activated carbon (BAC) treatment, resulting in improved effluent water bio-safety. PMID:24937529

Automatic Screening for Perturbations in Boolean Networks.

PubMed

Schwab, Julian D; Kestler, Hans A

2018-01-01

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

2016-03-01

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

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.

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

Uncovering a Predictive Molecular Signature for the Onset of NASH-Related Fibrosis in a Translational NASH Mouse Model.

PubMed

van Koppen, Arianne; Verschuren, Lars; van den Hoek, Anita M; Verheij, Joanne; Morrison, Martine C; Li, Kelvin; Nagabukuro, Hiroshi; Costessi, Adalberto; Caspers, Martien P M; van den Broek, Tim J; Sagartz, John; Kluft, Cornelis; Beysen, Carine; Emson, Claire; van Gool, Alain J; Goldschmeding, Roel; Stoop, Reinout; Bobeldijk-Pastorova, Ivana; Turner, Scott M; Hanauer, Guido; Hanemaaijer, Roeland

2018-01-01

The incidence of nonalcoholic steatohepatitis (NASH) is increasing. The pathophysiological mechanisms of NASH and the sequence of events leading to hepatic fibrosis are incompletely understood. The aim of this study was to gain insight into the dynamics of key molecular processes involved in NASH and to rank early markers for hepatic fibrosis. A time-course study in low-density lipoprotein-receptor knockout. Leiden mice on a high-fat diet was performed to identify the temporal dynamics of key processes contributing to NASH and fibrosis. An integrative systems biology approach was used to elucidate candidate markers linked to the active fibrosis process by combining transcriptomics, dynamic proteomics, and histopathology. The translational value of these findings were confirmed using human NASH data sets. High-fat-diet feeding resulted in obesity, hyperlipidemia, insulin resistance, and NASH with fibrosis in a time-dependent manner. Temporal dynamics of key molecular processes involved in the development of NASH were identified, including lipid metabolism, inflammation, oxidative stress, and fibrosis. A data-integrative approach enabled identification of the active fibrotic process preceding histopathologic detection using a novel molecular fibrosis signature. Human studies were used to identify overlap of genes and processes and to perform a network biology-based prioritization to rank top candidate markers representing the early manifestation of fibrosis. An early predictive molecular signature was identified that marked the active profibrotic process before histopathologic fibrosis becomes manifest. Early detection of the onset of NASH and fibrosis enables identification of novel blood-based biomarkers to stratify patients at risk, development of new therapeutics, and help shorten (pre)clinical experimental time frames.

Differential Variance Analysis: a direct method to quantify and visualize dynamic heterogeneities

NASA Astrophysics Data System (ADS)

Pastore, Raffaele; Pesce, Giuseppe; Caggioni, Marco

2017-03-01

Many amorphous materials show spatially heterogenous dynamics, as different regions of the same system relax at different rates. Such a signature, known as Dynamic Heterogeneity, has been crucial to understand the nature of the jamming transition in simple model systems and is currently considered very promising to characterize more complex fluids of industrial and biological relevance. Unfortunately, measurements of dynamic heterogeneities typically require sophisticated experimental set-ups and are performed by few specialized groups. It is now possible to quantitatively characterize the relaxation process and the emergence of dynamic heterogeneities using a straightforward method, here validated on video microscopy data of hard-sphere colloidal glasses. We call this method Differential Variance Analysis (DVA), since it focuses on the variance of the differential frames, obtained subtracting images at different time-lags. Moreover, direct visualization of dynamic heterogeneities naturally appears in the differential frames, when the time-lag is set to the one corresponding to the maximum dynamic susceptibility. This approach opens the way to effectively characterize and tailor a wide variety of soft materials, from complex formulated products to biological tissues.

CFD simulation of fluid dynamic and biokinetic processes within activated sludge reactors under intermittent aeration regime.

PubMed

Sánchez, F; Rey, H; Viedma, A; Nicolás-Pérez, F; Kaiser, A S; Martínez, M

2018-08-01

Due to the aeration system, biological reactors are the most energy-consuming facilities of convectional WWTPs. Many biological reactors work under intermittent aeration regime; the optimization of the aeration process (air diffuser layout, air flow rate per diffuser, aeration length …) is necessary to ensure an efficient performance; satisfying the effluent requirements with the minimum energy consumption. This work develops a CFD modelling of an activated sludge reactor (ASR) which works under intermittent aeration regime. The model considers the fluid dynamic and biological processes within the ASR. The biological simulation, which is transient, takes into account the intermittent aeration regime. The CFD modelling is employed for the selection of the aeration system of an ASR. Two different aeration configurations are simulated. The model evaluates the aeration power consumption necessary to satisfy the effluent requirements. An improvement of 2.8% in terms of energy consumption is achieved by modifying the air diffuser layout. An analysis of the influence of the air flow rate per diffuser on the ASR performance is carried out. The results show a reduction of 14.5% in the energy consumption of the aeration system when the air flow rate per diffuser is reduced. The model provides an insight into the aeration inefficiencies produced within ASRs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Can Horton hear the whos? The importance of scale in mosquito-borne disease.

PubMed

Lord, C C; Alto, B W; Anderson, S L; Connelly, C R; Day, J F; Richards, S L; Smartt, C T; Tabachnick, W J

2014-03-01

The epidemiology of vector-borne pathogens is determined by mechanisms and interactions at different scales of biological organization, from individual-level cellular processes to community interactions between species and with the environment. Most research, however, focuses on one scale or level with little integration between scales or levels within scales. Understanding the interactions between levels and how they influence our perception of vector-borne pathogens is critical. Here two examples of biological scales (pathogen transmission and mosquito mortality) are presented to illustrate some of the issues of scale and to explore how processes on different levels may interact to influence mosquito-borne pathogen transmission cycles. Individual variation in survival, vector competence, and other traits affect population abundance, transmission potential, and community structure. Community structure affects interactions between individuals such as competition and predation, and thus influences the individual-level dynamics and transmission potential. Modeling is a valuable tool to assess interactions between scales and how processes at different levels can affect transmission dynamics. We expand an existing model to illustrate the types of studies needed, showing that individual-level variation in viral dose acquired or needed for infection can influence the number of infectious vectors. It is critical that interactions within and among biological scales and levels of biological organization are understood for greater understanding of pathogen transmission with the ultimate goal of improving control of vector-borne pathogens.

Quantitative imaging of mammalian transcriptional dynamics: from single cells to whole embryos.

PubMed

Zhao, Ziqing W; White, Melanie D; Bissiere, Stephanie; Levi, Valeria; Plachta, Nicolas

2016-12-23

Probing dynamic processes occurring within the cell nucleus at the quantitative level has long been a challenge in mammalian biology. Advances in bio-imaging techniques over the past decade have enabled us to directly visualize nuclear processes in situ with unprecedented spatial and temporal resolution and single-molecule sensitivity. Here, using transcription as our primary focus, we survey recent imaging studies that specifically emphasize the quantitative understanding of nuclear dynamics in both time and space. These analyses not only inform on previously hidden physical parameters and mechanistic details, but also reveal a hierarchical organizational landscape for coordinating a wide range of transcriptional processes shared by mammalian systems of varying complexity, from single cells to whole embryos.

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.

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.

Punctuated equilibrium and power law in economic dynamics

NASA Astrophysics Data System (ADS)

Gupta, Abhijit Kar

2012-02-01

This work is primarily based on a recently proposed toy model by Thurner et al. (2010) [3] on Schumpeterian economic dynamics (inspired by the idea of economist Joseph Schumpeter [9]). Interestingly, punctuated equilibrium has been shown to emerge from the dynamics. The punctuated equilibrium and Power law are known to be associated with similar kinds of biologically relevant evolutionary models proposed in the past. The occurrence of the Power law is a signature of Self-Organised Criticality (SOC). In our view, power laws can be obtained by controlling the dynamics through incorporating the idea of feedback into the algorithm in some way. The so-called 'feedback' was achieved by introducing the idea of fitness and selection processes in the biological evolutionary models. Therefore, we examine the possible emergence of a power law by invoking the concepts of 'fitness' and 'selection' in the present model of economic evolution.

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

Algorithm for cellular reprogramming.

PubMed

Ronquist, Scott; Patterson, Geoff; Muir, Lindsey A; Lindsly, Stephen; Chen, Haiming; Brown, Markus; Wicha, Max S; Bloch, Anthony; Brockett, Roger; Rajapakse, Indika

2017-11-07

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle-synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Copyright © 2017 the Author(s). Published by PNAS.

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.

Nucleophilic catalysis of acylhydrazone equilibration for protein-directed dynamic covalent chemistry

PubMed Central

Bhat, Venugopal T.; Caniard, Anne M.; Luksch, Torsten; Brenk, Ruth; Campopiano, Dominic J.; Greaney, Michael F.

2010-01-01

Dynamic covalent chemistry uses reversible chemical reactions to set up an equilibrating network of molecules at thermodynamic equilibrium, which can adjust its composition in response to any agent capable of altering the free energy of the system. When the target is a biological macromolecule, such as a protein, the process corresponds to the protein directing the synthesis of its own best ligand. Here, we demonstrate that reversible acylhydrazone formation is an effective chemistry for biological dynamic combinatorial library formation. In the presence of aniline as a nucleophilic catalyst, dynamic combinatorial libraries equilibrate rapidly at pH 6.2, are fully reversible, and may be switched on or off by means of a change in pH. We have interfaced these hydrazone dynamic combinatorial libraries with two isozymes from the glutathione S-transferase class of enzyme, and observed divergent amplification effects, where each protein selects the best-fitting hydrazone for the hydrophobic region of its active site. PMID:20489719

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.

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.

Reversing DNA Methylation: Mechanisms, Genomics, and Biological Functions

PubMed Central

Wu, Hao; Zhang, Yi

2014-01-01

Methylation of cytosines in the mammalian genome represents a key epigenetic modification and is dynamically regulated during development. Compelling evidence now suggests that dynamic regulation of DNA methylation is mainly achieved through a cyclic enzymatic cascade comprised of cytosine methylation, iterative oxidation of methyl group by TET dioxygenases, and restoration of unmodified cytosines by either replication-dependent dilution or DNA glycosylase-initiated base excision repair. In this review, we discuss the mechanism and function of DNA demethylation in mammalian genomes, focusing particularly on how developmental modulation of the cytosine-modifying pathway is coupled to active reversal of DNA methylation in diverse biological processes. PMID:24439369

The unforeseen challenge: from genotype-to-phenotype in cell populations

NASA Astrophysics Data System (ADS)

Braun, Erez

2015-02-01

Biological cells present a paradox, in that they show simultaneous stability and flexibility, allowing them to adapt to new environments and to evolve over time. The emergence of stable cell states depends on genotype-to-phenotype associations, which essentially reflect the organization of gene regulatory modes. The view taken here is that cell-state organization is a dynamical process in which the molecular disorder manifests itself in a macroscopic order. The genome does not determine the ordered cell state; rather, it participates in this process by providing a set of constraints on the spectrum of regulatory modes, analogous to boundary conditions in physical dynamical systems. We have developed an experimental framework, in which cell populations are exposed to unforeseen challenges; novel perturbations they had not encountered before along their evolutionary history. This approach allows an unbiased view of cell dynamics, uncovering the potential of cells to evolve and develop adapted stable states. In the last decade, our experiments have revealed a coherent set of observations within this framework, painting a picture of the living cell that in many ways is not aligned with the conventional one. Of particular importance here, is our finding that adaptation of cell-state organization is essentially an efficient exploratory dynamical process rather than one founded on random mutations. Based on our framework, a set of concepts underlying cell-state organization—exploration evolving by global, non-specific, dynamics of gene activity—is presented here. These concepts have significant consequences for our understanding of the emergence and stabilization of a cell phenotype in diverse biological contexts. Their implications are discussed for three major areas of biological inquiry: evolution, cell differentiation and cancer. There is currently no unified theoretical framework encompassing the emergence of order, a stable state, in the living cell. Hopefully, the integrated picture described here will provide a modest contribution towards a physics theory of the cell.

Sequential chemical-biological processes for the treatment of industrial wastewaters: review of recent progresses and critical assessment.

PubMed

Guieysse, Benoit; Norvill, Zane N

2014-02-28

When direct wastewater biological treatment is unfeasible, a cost- and resource-efficient alternative to direct chemical treatment consists of combining biological treatment with a chemical pre-treatment aiming to convert the hazardous pollutants into more biodegradable compounds. Whereas the principles and advantages of sequential treatment have been demonstrated for a broad range of pollutants and process configurations, recent progresses (2011-present) in the field provide the basis for refining assessment of feasibility, costs, and environmental impacts. This paper thus reviews recent real wastewater demonstrations at pilot and full scale as well as new process configurations. It also discusses new insights on the potential impacts of microbial community dynamics on process feasibility, design and operation. Finally, it sheds light on a critical issue that has not yet been properly addressed in the field: integration requires complex and tailored optimization and, of paramount importance to full-scale application, is sensitive to uncertainty and variability in the inputs used for process design and operation. Future research is therefore critically needed to improve process control and better assess the real potential of sequential chemical-biological processes for industrial wastewater treatment. Copyright © 2013 Elsevier B.V. All rights reserved.

Biology-Inspired Autonomous Control

DTIC Science & Technology

2011-08-31

from load sensing in a turbulent flow field with high levels of plant uncertainty and optical feedback latency. The results of this paper suggest... Mimicry of biological systems, in the form of precise mathematical or physical dynamical modeling, is yielding impressive insight into the underlying...processing and plants , the aerospace industry has been slow to accept adaptive control. In the past decade however, newer methods for design of adaptive

Qualitative dynamics semantics for SBGN process description.

PubMed

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loïc

2016-06-16

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Emerging biomedical applications of synthetic biology.

PubMed

Weber, Wilfried; Fussenegger, Martin

2011-11-29

Synthetic biology aims to create functional devices, systems and organisms with novel and useful functions on the basis of catalogued and standardized biological building blocks. Although they were initially constructed to elucidate the dynamics of simple processes, designed devices now contribute to the understanding of disease mechanisms, provide novel diagnostic tools, enable economic production of therapeutics and allow the design of novel strategies for the treatment of cancer, immune diseases and metabolic disorders, such as diabetes and gout, as well as a range of infectious diseases. In this Review, we cover the impact and potential of synthetic biology for biomedical applications.

Assessing the dynamics of the upper soil layer relative to soil management practices

NASA Astrophysics Data System (ADS)

Hatfield, J.; Wacha, K.; Dold, C.

2017-12-01

The upper layer of the soil is the critical interface between the soil and the atmosphere and is the most dynamic in response to management practices. One of the soil properties most reflective to changes in management is the stability of the aggregates because this property controls infiltration of water and exchange of gases. An aggregation model has been developed based on the factors that control how aggregates form and the forces which degrade aggregates. One of the major factors for this model is the storage of carbon into the soil and the interaction with the soil biological component. To increase soil biology requires a stable microclimate that provides food, water, shelter, and oxygen which in turn facilitates the incorporation of organic material into forms that can be combined with soil particles to create stable aggregates. The processes that increase aggregate size and stability are directly linked the continual functioning of the biological component which in turn changes the physical and chemical properties of the soil. Soil aggregates begin to degrade as soon as there is no longer a supply of organic material into the soil. These processes can range from removal of organic material and excessive tillage. To increase aggregation of the upper soil layer requires a continual supply of organic material and the biological activity that incorporates organic material into substances that create a stable aggregate. Soils that exhibit stable soil aggregates at the surface have a prolonged infiltration rate with less runoff and a gas exchange that ensures adequate oxygen for maximum biological activity. Quantifying the dynamics of the soil surface layer provides a quantitative understanding of how management practices affect aggregate stability.

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.

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

PubMed Central

Zuckerman, Daniel M.; Chong, Lillian T.

2018-01-01

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling—the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes—protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation. PMID:28301772

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

QM/MM MD and Free Energy Simulation Study of Methyl Transfer Processes Catalyzed by PKMTs and PRMTs.

PubMed

Chu, Yuzhuo; Guo, Hong

2015-09-01

Methyl transfer processes catalyzed by protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs) control important biological events including transcriptional regulation and cell signaling. One important property of these enzymes is that different PKMTs and PRMTs catalyze the formation of different methylated product (product specificity). These different methylation states lead to different biological outcomes. Here, we review the results of quantum mechanics/molecular mechanics molecular dynamics and free energy simulations that have been performed to study the reaction mechanism of PKMTs and PRMTs and the mechanism underlying the product specificity of the methyl transfer processes.

QM/MM MD and free energy simulation study of methyl transfer processes catalyzed by PKMTs and PRMTs.

PubMed

Chu, Yuzhuo; Guo, Hong

2015-01-16

Methyl transfer processes catalyzed by protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs) control important biological events including transcriptional regulation and cell signaling. One important property of these enzymes is that different PKMTs and PRMTs catalyze the formation of different methylated product (product specificity). These different methylation states lead to different biological outcomes. Here we review the results of quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) and free energy simulations that have been performed to study the reaction mechanism of PKMTs and PRMTs and the mechanism underlying the product specificity of the methyl transfer processes.

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.

Investigating biomolecular recognition at the cell surface using atomic force microscopy.

PubMed

Wang, Congzhou; Yadavalli, Vamsi K

2014-05-01

Probing the interaction forces that drive biomolecular recognition on cell surfaces is essential for understanding diverse biological processes. Force spectroscopy has been a widely used dynamic analytical technique, allowing measurement of such interactions at the molecular and cellular level. The capabilities of working under near physiological environments, combined with excellent force and lateral resolution make atomic force microscopy (AFM)-based force spectroscopy a powerful approach to measure biomolecular interaction forces not only on non-biological substrates, but also on soft, dynamic cell surfaces. Over the last few years, AFM-based force spectroscopy has provided biophysical insight into how biomolecules on cell surfaces interact with each other and induce relevant biological processes. In this review, we focus on describing the technique of force spectroscopy using the AFM, specifically in the context of probing cell surfaces. We summarize recent progress in understanding the recognition and interactions between macromolecules that may be found at cell surfaces from a force spectroscopy perspective. We further discuss the challenges and future prospects of the application of this versatile technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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.

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.

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.

Dynamical Systems Theory: Application to Pedagogy

NASA Astrophysics Data System (ADS)

Abraham, Jane L.

Theories of learning affect how cognition is viewed, and this subsequently leads to the style of pedagogical practice that is used in education. Traditionally, educators have relied on a variety of theories on which to base pedagogy. Behavioral learning theories influenced the teaching/learning process for over 50 years. In the 1960s, the information processing approach brought the mind back into the learning process. The current emphasis on constructivism integrates the views of Piaget, Vygotsky, and cognitive psychology. Additionally, recent scientific advances have allowed researchers to shift attention to biological processes in cognition. The problem is that these theories do not provide an integrated approach to understanding principles responsible for differences among students in cognitive development and learning ability. Dynamical systems theory offers a unifying theoretical framework to explain the wider context in which learning takes place and the processes involved in individual learning. This paper describes how principles of Dynamic Systems Theory can be applied to cognitive processes of students, the classroom community, motivation to learn, and the teaching/learning dynamic giving educational psychologists a framework for research and pedagogy.

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.

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

Bedform development and morphodynamics in mixed cohesive sediment substrates: the importance of winnowing and flocculation

NASA Astrophysics Data System (ADS)

Ye, Leiping; Parsons, Daniel; Manning, Andrew

2016-04-01

There remains a lack of process-based knowledge of sediment dynamics within flows over bedforms generated in complex mixtures of cohesionless sand and biologically-active cohesive muds in natural estuarine flow systems. The work to be presented forms a part of the UK NERC "COHesive BEDforms (COHBED)" project which aims to fill this gap in knowledge. Herein results from a field survey in sub-tidal zone of Dee estuary (NW, England) and a set of large-scale laboratory experiments, conducted using mixtures of non-cohesive sands, cohesive muds and Xanthan gum (as a proxy for the biological stickiness of Extracellular Polymeric Substances (EPS)) will be presented. The results indicate the significance of biological-active cohesive sediments in controlling winnowing rates and flocculation dynamics, which contributes significantly to rates of bedform evolution.

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Treesearch

Louis R. Iverson; Frank R. Thompson; Stephen Matthews; Matthew Peters; Anantha Prasad; William D. Dijak; Jacob Fraser; Wen J. Wang; Brice Hanberry; Hong He; Maria Janowiak; Patricia Butler; Leslie Brandt; Chris Swanston

2016-01-01

Context. Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process...

Dynamic Urban Growth Models

DOT National Transportation Integrated Search

1978-12-01

In the report the concept of 'order by fluctuation,' that has appeared recently in physico-chemical and biological systems, is applied to the description of urban growth. It is shown that fluctuations play a vital role in the evolutionary process of ...

Dynamic Urban Growth Models

DOT National Transportation Integrated Search

1979-12-01

In the report the concept of 'order by fluctuation,' that has appeared recently in physico-chemical and biological systems, is applied to the description of urban growth. It is shown that fluctuations play a vital role in the evolutionary process of ...

High-resolution modeling of a marine ecosystem using the FRESCO hydroecological model

NASA Astrophysics Data System (ADS)

Zalesny, V. B.; Tamsalu, R.

2009-02-01

The FRESCO (Finnish Russian Estonian Cooperation) mathematical model describing a marine hydroecosystem is presented. The methodology of the numerical solution is based on the method of multicomponent splitting into physical and biological processes, spatial coordinates, etc. The model is used for the reproduction of physical and biological processes proceeding in the Baltic Sea. Numerical experiments are performed with different spatial resolutions for four marine basins that are enclosed into one another: the Baltic Sea, the Gulf of Finland, the Tallinn-Helsinki water area, and Tallinn Bay. Physical processes are described by the equations of nonhydrostatic dynamics, including the k-ω parametrization of turbulence. Biological processes are described by the three-dimensional equations of an aquatic ecosystem with the use of a size-dependent parametrization of biochemical reactions. The main goal of this study is to illustrate the efficiency of the developed numerical technique and to demonstrate the importance of a high spatial resolution for water basins that have complex bottom topography, such as the Baltic Sea. Detailed information about the atmospheric forcing, bottom topography, and coastline is very important for the description of coastal dynamics and specific features of a marine ecosystem. Experiments show that the spatial inhomogeneity of hydroecosystem fields is caused by the combined effect of upwelling, turbulent mixing, surface-wave breaking, and temperature variations, which affect biochemical reactions.

Single-Molecule Sensing with Nanopore Confinement: From Chemical Reactions to Biological Interactions.

PubMed

Lin, Yao; Ying, Yi-Lun; Gao, Rui; Long, Yi-Tao

2018-03-25

The nanopore can generate an electrochemical confinement for single-molecule sensing that help understand the fundamental chemical principle in nanoscale dimensions. By observing the generated ionic current, individual bond-making and bond-breaking steps, single biomolecule dynamic conformational changes and electron transfer processes that occur within pore can be monitored with high temporal and current resolution. These single-molecule studies in nanopore confinement are revealing information about the fundamental chemical and biological processes that cannot be extracted from ensemble measurements. In this Concept article, we introduce and discuss the electrochemical confinement effects on single-molecule covalent reactions, conformational dynamics of individual molecules and host-guest interactions in protein nanopores. Then, we extend the concept of nanopore confinement effects to confine electrochemical redox reactions in solid-state nanopores for developing new sensing mechanisms. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The model of drugs distribution dynamics in biological tissue

NASA Astrophysics Data System (ADS)

Ginevskij, D. A.; Izhevskij, P. V.; Sheino, I. N.

2017-09-01

The dose distribution by Neutron Capture Therapy follows the distribution of 10B in the tissue. The modern models of pharmacokinetics of drugs describe the processes occurring in conditioned "chambers" (blood-organ-tumor), but fail to describe the spatial distribution of the drug in the tumor and in normal tissue. The mathematical model of the spatial distribution dynamics of drugs in the tissue, depending on the concentration of the drug in the blood, was developed. The modeling method is the representation of the biological structure in the form of a randomly inhomogeneous medium in which the 10B distribution occurs. The parameters of the model, which cannot be determined rigorously in the experiment, are taken as the quantities subject to the laws of the unconnected random processes. The estimates of 10B distribution preparations in the tumor and healthy tissue, inside/outside the cells, are obtained.

Roadmap on semiconductor-cell biointerfaces

NASA Astrophysics Data System (ADS)

Tian, Bozhi; Xu, Shuai; Rogers, John A.; Cestellos-Blanco, Stefano; Yang, Peidong; Carvalho-de-Souza, João L.; Bezanilla, Francisco; Liu, Jia; Bao, Zhenan; Hjort, Martin; Cao, Yuhong; Melosh, Nicholas; Lanzani, Guglielmo; Benfenati, Fabio; Galli, Giulia; Gygi, Francois; Kautz, Rylan; Gorodetsky, Alon A.; Kim, Samuel S.; Lu, Timothy K.; Anikeeva, Polina; Cifra, Michal; Krivosudský, Ondrej; Havelka, Daniel; Jiang, Yuanwen

2018-05-01

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world.

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.

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.

Application of biospeckles for assessment of structural and cellular changes in muscle tissue

NASA Astrophysics Data System (ADS)

Maksymenko, Oleksandr P.; Muravsky, Leonid I.; Berezyuk, Mykola I.

2015-09-01

A modified spatial-temporal speckle correlation technique for operational assessment of structural changes in muscle tissues after slaughtering is considered. Coefficient of biological activity as a quantitative indicator of structural changes of biochemical processes in biological tissues is proposed. The experimental results have shown that this coefficient properly evaluates the biological activity of pig and chicken muscle tissue samples. Studying the degradation processes in muscle tissue during long-time storage in a refrigerator by measuring the spatial-temporal dynamics of biospeckle patterns is carried out. The reduction of the bioactivity level of refrigerated muscle tissue samples connected with the initiation of muscle fiber cracks and ruptures, reduction of sarcomeres, nuclei deformation, nuclear chromatin diminishing, and destruction of mitochondria is analyzed.

Control mechanisms for stochastic biochemical systems via computation of reachable sets.

PubMed

Lakatos, Eszter; Stumpf, Michael P H

2017-08-01

Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear. We present an approach to studying the impact of control measures on motifs of molecular interactions that addresses the problems faced in many biological systems: stochasticity, parameter uncertainty and nonlinearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.

Control mechanisms for stochastic biochemical systems via computation of reachable sets

PubMed Central

Lakatos, Eszter

2017-01-01

Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear. We present an approach to studying the impact of control measures on motifs of molecular interactions that addresses the problems faced in many biological systems: stochasticity, parameter uncertainty and nonlinearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters. PMID:28878957

Molecular dynamics simulations through GPU video games technologies

PubMed Central

Loukatou, Styliani; Papageorgiou, Louis; Fakourelis, Paraskevas; Filntisi, Arianna; Polychronidou, Eleftheria; Bassis, Ioannis; Megalooikonomou, Vasileios; Makałowski, Wojciech; Vlachakis, Dimitrios; Kossida, Sophia

2016-01-01

Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations. PMID:27525251

Teachers' Journal Club: Bridging between the Dynamics of Biological Discoveries and Biology Teachers

ERIC Educational Resources Information Center

Brill, Gilat; Falk, Hedda; Yarden, Anat

2003-01-01

Since biology is one of the most dynamic research fields within the natural sciences, the gap between the accumulated knowledge in biology and the knowledge that is taught in schools, increases rapidly with time. Our long-term objective is to develop means to bridge between the dynamics of biological discoveries and the biology teachers and…

Feedbacks between geomorphology and biota controlling Earth surface processes and landforms: A review of foundation concepts and current understandings

NASA Astrophysics Data System (ADS)

Corenblit, Dov; Baas, Andreas C. W.; Bornette, Gudrun; Darrozes, José; Delmotte, Sébastien; Francis, Robert A.; Gurnell, Angela M.; Julien, Frédéric; Naiman, Robert J.; Steiger, Johannes

2011-06-01

This review article presents recent advances in the field of biogeomorphology related to the reciprocal coupling between Earth surface processes and landforms, and ecological and evolutionary processes. The aim is to present to the Earth Science community ecological and evolutionary concepts and associated recent conceptual developments for linking geomorphology and biota. The novelty of the proposed perspective is that (1) in the presence of geomorphologic-engineer species, which modify sediment and landform dynamics, natural selection operating at the scale of organisms may have consequences for the physical components of ecosystems, and particularly Earth surface processes and landforms; and (2) in return, these modifications of geomorphologic processes and landforms often feed back to the ecological characteristics of the ecosystem (structure and function) and thus to biological characteristics of engineer species and/or other species (adaptation and speciation). The main foundation concepts from ecology and evolutionary biology which have led only recently to an improved conception of landform dynamics in geomorphology are reviewed and discussed. The biogeomorphologic macroevolutionary insights proposed explicitly integrate geomorphologic niche-dimensions and processes within an ecosystem framework and reflect current theories of eco-evolutionary and ecological processes. Collectively, these lead to the definition of an integrated model describing the overall functioning of biogeomorphologic systems over ecological and evolutionary timescales.

Differential dynamic microscopy of weakly scattering and polydisperse protein-rich clusters

NASA Astrophysics Data System (ADS)

Safari, Mohammad S.; Vorontsova, Maria A.; Poling-Skutvik, Ryan; Vekilov, Peter G.; Conrad, Jacinta C.

2015-10-01

Nanoparticle dynamics impact a wide range of biological transport processes and applications in nanomedicine and natural resource engineering. Differential dynamic microscopy (DDM) was recently developed to quantify the dynamics of submicron particles in solutions from fluctuations of intensity in optical micrographs. Differential dynamic microscopy is well established for monodisperse particle populations, but has not been applied to solutions containing weakly scattering polydisperse biological nanoparticles. Here we use bright-field DDM (BDDM) to measure the dynamics of protein-rich liquid clusters, whose size ranges from tens to hundreds of nanometers and whose total volume fraction is less than 10-5. With solutions of two proteins, hemoglobin A and lysozyme, we evaluate the cluster diffusion coefficients from the dependence of the diffusive relaxation time on the scattering wave vector. We establish that for weakly scattering populations, an optimal thickness of the sample chamber exists at which the BDDM signal is maximized at the smallest sample volume. The average cluster diffusion coefficient measured using BDDM is consistently lower than that obtained from dynamic light scattering at a scattering angle of 90∘. This apparent discrepancy is due to Mie scattering from the polydisperse cluster population, in which larger clusters preferentially scatter more light in the forward direction.

Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

NASA Astrophysics Data System (ADS)

Kirst, Christoph

It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

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.

Heat dissipation guides activation in signaling proteins.

PubMed

Weber, Jeffrey K; Shukla, Diwakar; Pande, Vijay S

2015-08-18

Life is fundamentally a nonequilibrium phenomenon. At the expense of dissipated energy, living things perform irreversible processes that allow them to propagate and reproduce. Within cells, evolution has designed nanoscale machines to do meaningful work with energy harnessed from a continuous flux of heat and particles. As dictated by the Second Law of Thermodynamics and its fluctuation theorem corollaries, irreversibility in nonequilibrium processes can be quantified in terms of how much entropy such dynamics produce. In this work, we seek to address a fundamental question linking biology and nonequilibrium physics: can the evolved dissipative pathways that facilitate biomolecular function be identified by their extent of entropy production in general relaxation processes? We here synthesize massive molecular dynamics simulations, Markov state models (MSMs), and nonequilibrium statistical mechanical theory to probe dissipation in two key classes of signaling proteins: kinases and G-protein-coupled receptors (GPCRs). Applying machinery from large deviation theory, we use MSMs constructed from protein simulations to generate dynamics conforming to positive levels of entropy production. We note the emergence of an array of peaks in the dynamical response (transient analogs of phase transitions) that draw the proteins between distinct levels of dissipation, and we see that the binding of ATP and agonist molecules modifies the observed dissipative landscapes. Overall, we find that dissipation is tightly coupled to activation in these signaling systems: dominant entropy-producing trajectories become localized near important barriers along known biological activation pathways. We go on to classify an array of equilibrium and nonequilibrium molecular switches that harmonize to promote functional dynamics.

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.

Inter-species investigation of the mechano-regulation of bone healing: comparison of secondary bone healing in sheep and rat.

PubMed

Checa, Sara; Prendergast, Patrick J; Duda, Georg N

2011-04-29

Inter-species differences in regeneration exist in various levels. One aspect is the dynamics of bone regeneration and healing, e.g. small animals show a faster healing response when compared to large animals. Mechanical as well as biological factors are known to play a key role in the process. However, it remains so far unknown whether different animals follow at all comparable mechano-biological rules during tissue regeneration, and in particular during bone healing. In this study, we investigated whether differences observed in vivo in the dynamics of bone healing between rat and sheep are only due to differences in the animal size or whether these animals have a different mechano-biological response during the healing process. Histological sections from in vivo experiments were compared to in silico predictions of a mechano-biological computer model for the simulation of bone healing. Investigations showed that the healing processes in both animal models occur under significantly different levels of mechanical stimuli within the callus region, which could explain histological observations of early intramembranous ossification at the endosteal side. A species-specific adaptation of a mechano-biological model allowed a qualitative match of model predictions with histological observations. Specifically, when keeping cell activity processes at the same rate, the amount of tissue straining defining favorable mechanical conditions for the formation of bone had to be increased in the large animal model, with respect to the small animal, to achieve a qualitative agreement of model predictions with histological data. These findings illustrate that geometrical (size) differences alone cannot explain the distinctions seen in the histological appearance of secondary bone healing in sheep and rat. It can be stated that significant differences in the mechano-biological regulation of the healing process exist between these species. Future investigations should aim towards understanding whether these differences are due to differences in cell behavior, material properties of the newly formed tissues within the callus and/or differences in response to the mechanical environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Studying chemical reactions in biological systems with MBN Explorer: implementation of molecular mechanics with dynamical topology

NASA Astrophysics Data System (ADS)

Sushko, Gennady B.; Solov'yov, Ilia A.; Verkhovtsev, Alexey V.; Volkov, Sergey N.; Solov'yov, Andrey V.

2016-01-01

The concept of molecular mechanics force field has been widely accepted nowadays for studying various processes in biomolecular systems. In this paper, we suggest a modification for the standard CHARMM force field that permits simulations of systems with dynamically changing molecular topologies. The implementation of the modified force field was carried out in the popular program MBN Explorer, and, to support the development, we provide several illustrative case studies where dynamical topology is necessary. In particular, it is shown that the modified molecular mechanics force field can be applied for studying processes where rupture of chemical bonds plays an essential role, e.g., in irradiation- or collision-induced damage, and also in transformation and fragmentation processes involving biomolecular systems. Contribution to the Topical Issue "COST Action Nano-IBCT: Nano-scale Processes Behind Ion-Beam Cancer Therapy", edited by Andrey V. Solov'yov, Nigel Mason, Gustavo Garcia and Eugene Surdutovich.

A Computational Model Predicting Disruption of Blood Vessel Development

EPA Science Inventory

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a varie...

Dynamic and diverse sugar signaling

PubMed Central

Li, Lei; Sheen, Jen

2016-01-01

Sugars fuel life and exert numerous regulatory actions that are fundamental to all life forms. There are two principal mechanisms underlie sugar “perception and signal transduction” in biological systems. Direct sensing and signaling is triggered via sugar-binding sensors with a broad range of affinity and specificity, whereas sugar-derived bioenergetic molecules and metabolites modulate signaling proteins and indirectly relay sugar signals. This review discusses the emerging sugar signals and potential sugar sensors discovered in plant systems. The findings leading to informative understanding of physiological regulation by sugars are considered and assessed. Comparative transcriptome analyses highlight the primary and dynamic sugar responses and reveal the convergent and specific regulators of key biological processes in the sugar-signaling network. PMID:27423125

Scaling in cognitive performance reflects multiplicative multifractal cascade dynamics

PubMed Central

Stephen, Damian G.; Anastas, Jason R.; Dixon, James A.

2012-01-01

Self-organized criticality purports to build multi-scaled structures out of local interactions. Evidence of scaling in various domains of biology may be more generally understood to reflect multiplicative interactions weaving together many disparate scales. The self-similarity of power-law scaling entails homogeneity: fluctuations distribute themselves similarly across many spatial and temporal scales. However, this apparent homogeneity can be misleading, especially as it spans more scales. Reducing biological processes to one power-law relationship neglects rich cascade dynamics. We review recent research into multifractality in executive-function cognitive tasks and propose that scaling reflects not criticality but instead interactions across multiple scales and among fluctuations of multiple sizes. PMID:22529819

A systematic petri net approach for multiple-scale modeling and simulation of biochemical processes.

PubMed

Chen, Ming; Hu, Minjie; Hofestädt, Ralf

2011-06-01

A method to exploit hybrid Petri nets for modeling and simulating biochemical processes in a systematic way was introduced. Both molecular biology and biochemical engineering aspects are manipulated. With discrete and continuous elements, the hybrid Petri nets can easily handle biochemical factors such as metabolites concentration and kinetic behaviors. It is possible to translate both molecular biological behavior and biochemical processes workflow into hybrid Petri nets in a natural manner. As an example, penicillin production bioprocess is modeled to illustrate the concepts of the methodology. Results of the dynamic of production parameters in the bioprocess were simulated and observed diagrammatically. Current problems and post-genomic perspectives were also discussed.

Plasmonic-based nanoprobes for dynamic sensing of single tumor cells (Conference Presentation)

NASA Astrophysics Data System (ADS)

Chen, Zixuan

2017-02-01

We described here two plasmonic-based nanoprobes with purpose of imaging dynamic biologic process of single tumor cells. At first, we proposed a multi-modified core-shell gold@silver nanorods for real-time monitoring the entire autophagy process at single-cell level. Autophagy is vital for understanding the mechanisms of human pathologies, developing novel drugs and exploring approaches for autophagy controlling. The plasmon resonance scattering spectra of the nanoprobes was superoxide radicals (O2•-)-dependent, a major indicator of cell autophagy, and suitable for real-time monitoring at single-cell level. More importantly, with the introduction of `relay probe' operation, two types of O2•-regulating autophagy processes were successfully traced from the beginning to the end, and the possible mechanism was also proposed. According to our results, intracellular O2•- level controlled the autophagy process by mediating the autolysosome generation. Different starvation approaches can induce different autophagy processes, such as diverse steady state time-consuming. In addition, a plasmonic-based nanothermometer was prepared via dense thermosensitive polymer (pNIPAAm) capping on gold nanorods, of which the plasmon resonance spectra was linearly dependent on adjacent temperature. In this work, the white light transmitted dark-field illuminator was replaced by a laser total internal reflection dark-field microscope (LTIR-DFM) system in order to overcome the low-throughput and inexorable biological scattering background of DFM, as well as interference from mechanic noise, nanoprobe direction, optical system drift, etc. With this nanothermometer, we have successfully captured temporal biological thermal process (thermogenesis) occurred in single tumor cells, providing a new potential strategy for in-situ cellular analysis.

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

The Grand Challenges of Command and Control Policy

DTIC Science & Technology

2006-06-01

Memetic Warfare Memes are ideas that can be modeled and simulated. In a modern journalistic environment, dynamic information feedback from the theater...output type such that both adversarial meme processes and our counter anti- memetic activity could be modeled, simulated, and assessed. I am now...opposing force of the consequence of using biological or chemical weapons on the invading American forces. Do we have the proper memetic dynamics

Preoutbreak dynamics of a recently established invasive herbivore: roles of natural enemies and habitat structure in stage-specific performance of gypsy moth (Lepidoptera: Lymantriidae) populations in northeastern Wisconsin

Treesearch

Rebecca Hoffman Gray; Craig G. Lorimer; Patrick C. Tobin; Kenneth F. Raffa

2008-01-01

A major challenge to addressing biological invasions is that the need for emergency responses often precludes opportunities to analyze the dynamics between initial establishment and population eruption. Thus, a broader understanding of underlying processes and management opportunities is often lacking. We examined the effects of habitat structure and natural enemies on...

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

Protein bio-corona: critical issue in immune nanotoxicology.

PubMed

Neagu, Monica; Piperigkou, Zoi; Karamanou, Konstantina; Engin, Ayse Basak; Docea, Anca Oana; Constantin, Carolina; Negrei, Carolina; Nikitovic, Dragana; Tsatsakis, Aristidis

2017-03-01

With the expansion of the nanomedicine field, the knowledge focusing on the behavior of nanoparticles in the biological milieu has rapidly escalated. Upon introduction to a complex biological system, nanomaterials dynamically interact with all the encountered biomolecules and form the protein "bio-corona." The decoration with these surface biomolecules endows nanoparticles with new properties. The present review will address updates of the protein bio-corona characteristics as influenced by nanoparticle's physicochemical properties and by the particularities of the encountered biological milieu. Undeniably, bio-corona generation influences the efficacy of the nanodrug and guides the actions of innate and adaptive immunity. Exploiting the dynamic process of protein bio-corona development in combination with the new engineered horizons of drugs linked to nanoparticles could lead to innovative functional nanotherapies. Therefore, bio-medical nanotechnologies should focus on the interactions of nanoparticles with the immune system for both safety and efficacy reasons.

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.

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.

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.

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

PubMed Central

2010-01-01

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system. Results In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Conclusions Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics. PMID:21143785

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

PubMed

Ghosh, Preetam; Ghosh, Samik; Basu, Kalyan; Das, Sajal K; Zhang, Chaoyang

2010-12-01

The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.

Quantitative real-time imaging of glutathione

USDA-ARS?s Scientific Manuscript database

Glutathione plays many important roles in biological processes; however, the dynamic changes of glutathione concentrations in living cells remain largely unknown. Here, we report a reversible reaction-based fluorescent probe—designated as RealThiol (RT)—that can quantitatively monitor the real-time ...

Dynamics of Urban Evolution : Volume 1. Inter-Urban Evolution.

DOT National Transportation Integrated Search

1978-10-01

The concept of "order by fluctuation," that has appeared recently in physico-chemical and biological systems, is applied to the description of urban growth. It is shown that fluctuations play a vital role in the evolutionary process of urban growth. ...

Quantum biological channel modeling and capacity calculation.

PubMed

Djordjevic, Ivan B

2012-12-10

Quantum mechanics has an important role in photosynthesis, magnetoreception, and evolution. There were many attempts in an effort to explain the structure of genetic code and transfer of information from DNA to protein by using the concepts of quantum mechanics. The existing biological quantum channel models are not sufficiently general to incorporate all relevant contributions responsible for imperfect protein synthesis. Moreover, the problem of determination of quantum biological channel capacity is still an open problem. To solve these problems, we construct the operator-sum representation of biological channel based on codon basekets (basis vectors), and determine the quantum channel model suitable for study of the quantum biological channel capacity and beyond. The transcription process, DNA point mutations, insertions, deletions, and translation are interpreted as the quantum noise processes. The various types of quantum errors are classified into several broad categories: (i) storage errors that occur in DNA itself as it represents an imperfect storage of genetic information, (ii) replication errors introduced during DNA replication process, (iii) transcription errors introduced during DNA to mRNA transcription, and (iv) translation errors introduced during the translation process. By using this model, we determine the biological quantum channel capacity and compare it against corresponding classical biological channel capacity. We demonstrate that the quantum biological channel capacity is higher than the classical one, for a coherent quantum channel model, suggesting that quantum effects have an important role in biological systems. The proposed model is of crucial importance towards future study of quantum DNA error correction, developing quantum mechanical model of aging, developing the quantum mechanical models for tumors/cancer, and study of intracellular dynamics in general.

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.

[Dynamics of soil physical properties and biological soil crust during the vegetation restoration process of abandoned croplands in the Ordos Plateau, China].

PubMed

Cai, Wen Tao; Li, He Yi; Lai, Li Ming; Zhang, Xiao Long; Guan, Tian Yu; Zhou, Ji Hua; Jiang, Lian He; Zheng, Yuan Run

2017-03-18

A series of typical abandoned croplands in the regions of Ruanliang and Yingliang in the Ordos Plateau, China, were selected, and dynamics of the surface litter, biological soil crust and soil bulk density, soil texture, and soil moisture in different soil layers were investigated. The results showed that in the abandoned cropland in Ruanliang, the clay particle content and surface litter of the surface soil layer (0-10 cm) increased during the restoration process, while that of soil bulk density substantially decreased and soil water content slightly increased in the surface soil. In the medium soil layer (10-30 cm), the clay particle content increased and the soil water content slightly decreased. In the deep soil layer (30-50 cm), there was a relatively large variation in the physical properties. In the abandoned cropland in Yingliang, the coverage of litter and the coverage and thickness of the biological soil crust increased during the abandonment process. The surface soil bulk density, soil clay particle content and soil water content remained constant in 0-10 cm soil layer, while the physical properties varied substantially in 10-40 cm soil layer. The shallow distribution of the soil water content caused by the accumulation of the litter and clay particles on the soil surface might be the key reason of the replacement of the semi-shrub Artemisia ordosica community with a perennial grass community over the last 20 years of the abandoned cropland in Ruanliang. The relatively high soil water content in the shallow layer and the development of the biological soil crust might explain why the abandoned cropland in Yingliang was not invaded by the semi-shrub A. ordosica during the restoration process.

Tracking single particle rotation: Probing dynamics in four dimensions

DOE PAGES

Anthony, Stephen Michael; Yu, Yan

2015-04-29

Direct visualization and tracking of small particles at high spatial and temporal resolution provides a powerful approach to probing complex dynamics and interactions in chemical and biological processes. Analysis of the rotational dynamics of particles adds a new dimension of information that is otherwise impossible to obtain with conventional 3-D particle tracking. In this review, we survey recent advances in single-particle rotational tracking, with highlights on the rotational tracking of optically anisotropic Janus particles. Furthermore, strengths and weaknesses of the various particle tracking methods, and their applications are discussed.

Modeling Selection and Extinction Mechanisms of Biological Systems

NASA Astrophysics Data System (ADS)

Amirjanov, Adil

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

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.

4D Biofabrication of Branching Multicellular Structures: A Morphogenesis Simulation Based on Turing’s Reaction-Diffusion Dynamics

NASA Astrophysics Data System (ADS)

Zhu, Xiaolu; Yang, Hao

2017-12-01

The recently emerged four-dimensional (4D) biofabrication technique aims to create dynamic three-dimensional (3D) biological structures that can transform their shapes or functionalities with time when an external stimulus is imposed or when cell postprinting self-assembly occurs. The evolution of 3D pattern of branching geometry via self-assembly of cells is critical for 4D biofabrication of artificial organs or tissues with branched geometry. However, it is still unclear that how the formation and evolution of these branching pattern are biologically encoded. We study the 4D fabrication of lung branching structures utilizing a simulation model on the reaction-diffusion mechanism, which is established using partial differential equations of four variables, describing the reaction and diffusion process of morphogens with time during the development process of lung branching. The simulation results present the forming process of 3D branching pattern, and also interpret the behaviors of side branching and tip splitting as the stalk growing, through 3D visualization of numerical simulation.

The bioelectric code: An ancient computational medium for dynamic control of growth and form.

PubMed

Levin, Michael; Martyniuk, Christopher J

2018-02-01

What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering. Copyright © 2017 Elsevier B.V. All rights reserved.

The value of mechanistic biophysical information for systems-level understanding of complex biological processes such as cytokinesis.

PubMed

Pollard, Thomas D

2014-12-02

This review illustrates the value of quantitative information including concentrations, kinetic constants and equilibrium constants in modeling and simulating complex biological processes. Although much has been learned about some biological systems without these parameter values, they greatly strengthen mechanistic accounts of dynamical systems. The analysis of muscle contraction is a classic example of the value of combining an inventory of the molecules, atomic structures of the molecules, kinetic constants for the reactions, reconstitutions with purified proteins and theoretical modeling to account for the contraction of whole muscles. A similar strategy is now being used to understand the mechanism of cytokinesis using fission yeast as a favorable model system. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Dynamics of bubble collapse under vessel confinement in 2D hydrodynamic experiments

NASA Astrophysics Data System (ADS)

Shpuntova, Galina; Austin, Joanna

2013-11-01

One trauma mechanism in biomedical treatment techniques based on the application of cumulative pressure pulses generated either externally (as in shock-wave lithotripsy) or internally (by laser-induced plasma) is the collapse of voids. However, prediction of void-collapse driven tissue damage is a challenging problem, involving complex and dynamic thermomechanical processes in a heterogeneous material. We carry out a series of model experiments to investigate the hydrodynamic processes of voids collapsing under dynamic loading in configurations designed to model cavitation with vessel confinement. The baseline case of void collapse near a single interface is also examined. Thin sheets of tissue-surrogate polymer materials with varying acoustic impedance are used to create one or two parallel material interfaces near the void. Shadowgraph photography and two-color, single-frame particle image velocimetry quantify bubble collapse dynamics including jetting, interface dynamics and penetration, and the response of the surrounding material. Research supported by NSF Award #0954769, ``CAREER: Dynamics and damage of void collapse in biological materials under stress wave loading.''

Balancing Broad Ideas with Context: An Evaluation of Student Accuracy in Describing Ecosystem Processes after a System-Level Intervention

ERIC Educational Resources Information Center

Jordan, Rebecca C.; Brooks, Wesley R.; Hmelo-Silver, Cindy; Eberbach, Catherine; Sinha, Suparna

2014-01-01

Promoting student understanding of ecosystem processes is critical to biological education. Yet, teaching complex life systems can be difficult because systems are dynamic and often behave in a non-linear manner. In this paper, we discuss assessment results from a middle school classroom intervention in which a conceptual representation framework…

New understanding of rhizosphere processes enabled by advances in molecular and spatially resolved techniques

DOE PAGES

Hess, Nancy J.; Pasa-Tolic, Ljiljana; Bailey, Vanessa L.; ...

2017-04-12

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

New understanding of rhizosphere processes enabled by advances in molecular and spatially resolved techniques

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

Hess, Nancy J.; Paša-Tolić, Ljiljana; Bailey, Vanessa L.

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

Integrated omics for the identification of key functionalities in biological wastewater treatment microbial communities.

PubMed

Narayanasamy, Shaman; Muller, Emilie E L; Sheik, Abdul R; Wilmes, Paul

2015-05-01

Biological wastewater treatment plants harbour diverse and complex microbial communities which prominently serve as models for microbial ecology and mixed culture biotechnological processes. Integrated omic analyses (combined metagenomics, metatranscriptomics, metaproteomics and metabolomics) are currently gaining momentum towards providing enhanced understanding of community structure, function and dynamics in situ as well as offering the potential to discover novel biological functionalities within the framework of Eco-Systems Biology. The integration of information from genome to metabolome allows the establishment of associations between genetic potential and final phenotype, a feature not realizable by only considering single 'omes'. Therefore, in our opinion, integrated omics will become the future standard for large-scale characterization of microbial consortia including those underpinning biological wastewater treatment processes. Systematically obtained time and space-resolved omic datasets will allow deconvolution of structure-function relationships by identifying key members and functions. Such knowledge will form the foundation for discovering novel genes on a much larger scale compared with previous efforts. In general, these insights will allow us to optimize microbial biotechnological processes either through better control of mixed culture processes or by use of more efficient enzymes in bioengineering applications. © 2015 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Cell migration analysis: A low-cost laboratory experiment for cell and developmental biology courses using keratocytes from fish scales.

PubMed

Prieto, Daniel; Aparicio, Gonzalo; Sotelo-Silveira, Jose R

2017-11-01

Cell and developmental processes are complex, and profoundly dependent on spatial relationships that change over time. Innovative educational or teaching strategies are always needed to foster deep comprehension of these processes and their dynamic features. However, laboratory exercises in cell and developmental biology at the undergraduate level do not often take into account the time dimension. In this article, we provide a laboratory exercise focused in cell migration, aiming to stimulate thinking in time and space dimensions through a simplification of more complex processes occurring in cell or developmental biology. The use of open-source tools for the analysis, as well as the whole package of raw results (available at http://github.com/danielprieto/keratocyte) make it suitable for its implementation in courses with very diverse budgets. Aiming to facilitate the student's transition from science-students to science-practitioners we propose an exercise of scientific thinking, and an evaluation method. This in turn is communicated here to facilitate the finding of common caveats and weaknesses in the process of producing simple scientific communications describing the results achieved. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(6):475-482, 2017. © 2017 The International Union of Biochemistry and Molecular Biology.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

PubMed Central

Forsberg, Erica M; Huan, Tao; Rinehart, Duane; Benton, H Paul; Warth, Benedikt; Hilmers, Brian; Siuzdak, Gary

2018-01-01

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min. PMID:29494574

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Water dynamics: Gliding and trudging

NASA Astrophysics Data System (ADS)

Itoh, Yoshimitsu; Aida, Takuzo

2017-10-01

Water is increasingly recognized as being of paramount importance in biological processes, yet its exact role remains difficult to elucidate. Now, the motion of water molecules within and around a synthetic peptide-amphiphile nanofibre has been precisely determined, showing significant differences between its core and surface.

Dynamics of Urban Evolution : Volume 2. Intra-Urban Evolution.

DOT National Transportation Integrated Search

1978-10-01

In this report the concept of "order by fluctuation," that has appeared recently in physico-chemical and biological systems, is applied to the description of urban growth. It is shown that fluctuations play a vital role in the evolutionary process of...

Biogeochemistry of hypersaline microbial mats illustrates the dynamics of modern microbial ecosystems and the early evolution of the biosphere

NASA Technical Reports Server (NTRS)

Des Marais, David J.

2003-01-01

Photosynthetic microbial mats are remarkably complete self-sustaining ecosystems at the millimeter scale, yet they have substantially affected environmental processes on a planetary scale. These mats may be direct descendents of the most ancient biological communities in which even oxygenic photosynthesis might have developed. Photosynthetic mats are excellent natural laboratories to help us to learn how microbial populations associate to control dynamic biogeochemical gradients.

Optical methods for measuring DNA folding

NASA Astrophysics Data System (ADS)

Smith, Adam D.; Ukogu, Obinna A.; Devenica, Luka M.; White, Elizabeth D.; Carter, Ashley R.

2017-03-01

One of the most important biological processes is the dynamic folding and unfolding of deoxyribonucleic acid (DNA). The folding process is crucial for DNA to fit within the boundaries of the cell, while the unfolding process is essential for DNA replication and transcription. To accommodate both processes, the cell employs a highly active folding mechanism that has been the subject of intense study over the last few decades. Still, many open questions remain. What are the pathways for folding or unfolding? How does the folding equilibrium shift? And, what is the energy landscape for a particular process? Here, we review these emerging questions and the in vitro, optical methods that have provided answers, introducing the topic for those physicists seeking to step into biology. Specifically, we discuss two iconic experiments for DNA folding, the tethered particle motion (TPM) experiment and the optical tweezers experiment.

Holographic femtosecond laser processing and its application to biological materials (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hayasaki, Yoshio

2017-02-01

Femtosecond laser processing is a promising tool for fabricating novel and useful structures on the surfaces of and inside materials. An enormous number of pulse irradiation points will be required for fabricating actual structures with millimeter scale, and therefore, the throughput of femtosecond laser processing must be improved for practical adoption of this technique. One promising method to improve throughput is parallel pulse generation based on a computer-generated hologram (CGH) displayed on a spatial light modulator (SLM), a technique called holographic femtosecond laser processing. The holographic method has the advantages such as high throughput, high light use efficiency, and variable, instantaneous, and 3D patterning. Furthermore, the use of an SLM gives an ability to correct unknown imperfections of the optical system and inhomogeneity in a sample using in-system optimization of the CGH. Furthermore, the CGH can adaptively compensate in response to dynamic unpredictable mechanical movements, air and liquid disturbances, a shape variation and deformation of the target sample, as well as adaptive wavefront control for environmental changes. Therefore, it is a powerful tool for the fabrication of biological cells and tissues, because they have free form, variable, and deformable structures. In this paper, we present the principle and the experimental setup of holographic femtosecond laser processing, and the effective way for processing the biological sample. We demonstrate the femtosecond laser processing of biological materials and the processing properties.

Analyzing the dynamics of cell cycle processes from fixed samples through ergodic principles.

PubMed

Wheeler, Richard John

2015-11-05

Tools to analyze cyclical cellular processes, particularly the cell cycle, are of broad value for cell biology. Cell cycle synchronization and live-cell time-lapse observation are widely used to analyze these processes but are not available for many systems. Simple mathematical methods built on the ergodic principle are a well-established, widely applicable, and powerful alternative analysis approach, although they are less widely used. These methods extract data about the dynamics of a cyclical process from a single time-point "snapshot" of a population of cells progressing through the cycle asynchronously. Here, I demonstrate application of these simple mathematical methods to analysis of basic cyclical processes--cycles including a division event, cell populations undergoing unicellular aging, and cell cycles with multiple fission (schizogony)--as well as recent advances that allow detailed mapping of the cell cycle from continuously changing properties of the cell such as size and DNA content. This includes examples using existing data from mammalian, yeast, and unicellular eukaryotic parasite cell biology. Through the ongoing advances in high-throughput cell analysis by light microscopy, electron microscopy, and flow cytometry, these mathematical methods are becoming ever more important and are a powerful complementary method to traditional synchronization and time-lapse cell cycle analysis methods. © 2015 Wheeler. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication 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).

Oceanic biogeochemical controls on global dynamics of persistent organic pollutants.

PubMed

Dachs, Jordi; Lohmann, Rainer; Ockenden, Wendy A; Méjanelle, Laurence; Eisenreich, Steven J; Jones, Kevin C

2002-10-15

Understanding and quantifying the global dynamics and sinks of persistent organic pollutants (POPs) is important to assess their environmental impact and fate. Air-surface exchange processes, where temperature plays a central role in controlling volatilization and deposition, are of key importance in controlling global POP dynamics. The present study is an assessment of the role of oceanic biogeochemical processes, notably phytoplankton uptake and vertical fluxes of particles, on the global dynamics of POPs. Field measurements of atmospheric polychlorinated biphenyls (PCBs), polychlorinated dibenzodioxins (PCDDs), and furans (PCDFs) are combined with remote sensing estimations of oceanic temperature, wind speed, and chlorophyll, to model the interactions between air-water exchange, phytoplankton uptake, and export of organic matter and POPs out of the mixed surface ocean layer. Deposition is enhanced in the mid-high latitudes and is driven by sinking marine particulate matter, rather than by a cold condensation effect. However, the relative contribution of the biological pump is a function of the physical-chemical properties of POPs. It is concluded that oceanic biogeochemical processes play a critical role in controlling the global dynamics and the ultimate sink of POPs.

Ultimate computing. Biomolecular consciousness and nano Technology

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

Hameroff, S.R.

1987-01-01

The book advances the premise that the cytoskeleton is the cell's nervous system, the biological controller/computer. If indeed cytoskeletal dynamics in the nanoscale (billionth meter, billionth second) are the texture of intracellular information processing, emerging ''NanoTechnologies'' (scanning tunneling microscopy, Feynman machines, von Neumann replicators, etc.) should enable direct monitoring, decoding and interfacing between biological and technological information devices. This in turn could result in important biomedical applications and perhaps a merger of mind and machine: Ultimate Computing.

A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

PubMed Central

Khammash, Mustafa

2014-01-01

Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191

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.

Bacteria as computers making computers

PubMed Central

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments. PMID:19016882

Bacteria as computers making computers.

PubMed

Danchin, Antoine

2009-01-01

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

Ultrasensitive investigations of biological systems by fluorescence correlation spectroscopy.

PubMed

Haustein, Elke; Schwille, Petra

2003-02-01

Fluorescence correlation spectroscopy (FCS) extracts information about molecular dynamics from the tiny fluctuations that can be observed in the emission of small ensembles of fluorescent molecules in thermodynamic equilibrium. Employing a confocal setup in conjunction with highly dilute samples, the average number of fluorescent particles simultaneously within the measurement volume (approximately 1 fl) is minimized. Among the multitude of chemical and physical parameters accessible by FCS are local concentrations, mobility coefficients, rate constants for association and dissociation processes, and even enzyme kinetics. As any reaction causing an alteration of the primary measurement parameters such as fluorescence brightness or mobility can be monitored, the application of this noninvasive method to unravel processes in living cells is straightforward. Due to the high spatial resolution of less than 0.5 microm, selective measurements in cellular compartments, e.g., to probe receptor-ligand interactions on cell membranes, are feasible. Moreover, the observation of local molecular dynamics provides access to environmental parameters such as local oxygen concentrations, pH, or viscosity. Thus, this versatile technique is of particular attractiveness for researchers striving for quantitative assessment of interactions and dynamics of small molecular quantities in biologically relevant systems.

A Biologically Informed, Mechanistic Model of Desert Shrub Population Dynamics Bearing on Arid Landscape Evolution

NASA Astrophysics Data System (ADS)

Worman, Stacey; Furbish, David; Fathel, Siobhan

2014-05-01

In arid landscapes, desert shrubs individually and collectively modify how sediment is transported (e.g by wind, overland-flow, and rain-splash). Addressing how desert shrubs modify landscapes on geomorphic timescales therefore necessitates spanning multiple shrub lifetimes and accounting for how processes affecting shrub dynamics on these longer timescales (e.g. fire, grazing, drought, and climate change) may in turn impact sediment transport. To fulfill this need, we present a mechanistic model of the spatiotemporal dynamics of a desert-shrub population that uses a simple accounting framework and tracks individual shrubs as they enter, age, and exit the population (via recruitment, growth, and mortality). Our model is novel insomuch as it (1) features a strong biophysical foundation, (2) mimics well-documented aspects of how shrub populations respond to changes in precipitation, and (3) possesses the process granularity appropriate for use in geomorphic simulations. In a complimentary abstract (Fathel et al. 2014), we demonstrate the potential of this biological model by coupling it to a physical model of rain-splash sediment transport: We mechanistically reproduce the empirical observation that the erosion rate of a hillslope decreases as its vegetation coverage increases and we predict erosion rates under different climate-change scenarios.

Systems microscopy: an emerging strategy for the life sciences.

PubMed

Lock, John G; Strömblad, Staffan

2010-05-01

Dynamic cellular processes occurring in time and space are fundamental to all physiology and disease. To understand complex and dynamic cellular processes therefore demands the capacity to record and integrate quantitative multiparametric data from the four spatiotemporal dimensions within which living cells self-organize, and to subsequently use these data for the mathematical modeling of cellular systems. To this end, a raft of complementary developments in automated fluorescence microscopy, cell microarray platforms, quantitative image analysis and data mining, combined with multivariate statistics and computational modeling, now coalesce to produce a new research strategy, "systems microscopy", which facilitates systems biology analyses of living cells. Systems microscopy provides the crucial capacities to simultaneously extract and interrogate multiparametric quantitative data at resolution levels ranging from the molecular to the cellular, thereby elucidating a more comprehensive and richly integrated understanding of complex and dynamic cellular systems. The unique capacities of systems microscopy suggest that it will become a vital cornerstone of systems biology, and here we describe the current status and future prospects of this emerging field, as well as outlining some of the key challenges that remain to be overcome. Copyright 2010 Elsevier Inc. All rights reserved.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

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

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

Optical trapping for analytical biotechnology.

PubMed

Ashok, Praveen C; Dholakia, Kishan

2012-02-01

We describe the exciting advances of using optical trapping in the field of analytical biotechnology. This technique has opened up opportunities to manipulate biological particles at the single cell or even at subcellular levels which has allowed an insight into the physical and chemical mechanisms of many biological processes. The ability of this technique to manipulate microparticles and measure pico-Newton forces has found several applications such as understanding the dynamics of biological macromolecules, cell-cell interactions and the micro-rheology of both cells and fluids. Furthermore we may probe and analyse the biological world when combining trapping with analytical techniques such as Raman spectroscopy and imaging. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated smoother for the numerical decoupling of dynamics models.

PubMed

Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.

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

Hydrologic processes and nutrient dynamics in a pristine mountain catchment

USGS Publications Warehouse

F. Richard Hauer,; Fagre, Daniel B.; Stanford, Jack A.

2002-01-01

Nutrient dynamics in watersheds have been used as an ecosystem-level indicator of overall ecosystem function or response to disturbance (e.g. Borman.N et al. 1974, WEBSTER et al. 1992). The examination of nutrients has been evaluated to determine responses to logging practices or other changes in watershed land use. Nutrient dynamics have been related to changing physical and biological characteristics (Mulholl AND 1992, CHESTNUT & McDowell 2000). Herein, the concentrations and dynamics of nitrogen, phosphorus and particulate organic carbon were examined in a large pristine watershed because they are affected by changes in discharge directly from the catchment and after passage through a large oligotrophic lake. 

Calcium ions in aqueous solutions: Accurate force field description aided by ab initio molecular dynamics and neutron scattering

NASA Astrophysics Data System (ADS)

Martinek, Tomas; Duboué-Dijon, Elise; Timr, Štěpán; Mason, Philip E.; Baxová, Katarina; Fischer, Henry E.; Schmidt, Burkhard; Pluhařová, Eva; Jungwirth, Pavel

2018-06-01

We present a combination of force field and ab initio molecular dynamics simulations together with neutron scattering experiments with isotopic substitution that aim at characterizing ion hydration and pairing in aqueous calcium chloride and formate/acetate solutions. Benchmarking against neutron scattering data on concentrated solutions together with ion pairing free energy profiles from ab initio molecular dynamics allows us to develop an accurate calcium force field which accounts in a mean-field way for electronic polarization effects via charge rescaling. This refined calcium parameterization is directly usable for standard molecular dynamics simulations of processes involving this key biological signaling ion.

Investigating Processes of Materials Formation via Liquid Phase and Cryogenic TEM

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

De Yoreo, James J.; Sommerdijk, Nico

2016-06-14

The formation of materials in solutions is a widespread phenomenon in synthetic, biological and geochemical systems, occurring through dynamic processes of nucleation, self-assembly, crystal growth, and coarsening. The recent advent of liquid phase TEM and advances in cryogenic TEM are transforming our understanding of these phenomena by providing new insights into the underlying physical and chemical mechanisms. The techniques have been applied to metallic and semiconductor nanoparticles, geochemical and biological minerals, electrochemical systems, macromolecular complexes, and selfassembling systems, both organic and inorganic. New instrumentation and methodologies currently on the horizon promise new opportunities for advancing the science of materials synthesis.

Reconstructing the regulatory circuit of cell fate determination in yeast mating response.

PubMed

Shao, Bin; Yuan, Haiyu; Zhang, Rongfei; Wang, Xuan; Zhang, Shuwen; Ouyang, Qi; Hao, Nan; Luo, Chunxiong

2017-07-01

Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.

Efficient implementation of constant pH molecular dynamics on modern graphics processors.

PubMed

Arthur, Evan J; Brooks, Charles L

2016-09-15

The treatment of pH sensitive ionization states for titratable residues in proteins is often omitted from molecular dynamics (MD) simulations. While static charge models can answer many questions regarding protein conformational equilibrium and protein-ligand interactions, pH-sensitive phenomena such as acid-activated chaperones and amyloidogenic protein aggregation are inaccessible to such models. Constant pH molecular dynamics (CPHMD) coupled with the Generalized Born with a Simple sWitching function (GBSW) implicit solvent model provide an accurate framework for simulating pH sensitive processes in biological systems. Although this combination has demonstrated success in predicting pKa values of protein structures, and in exploring dynamics of ionizable side-chains, its speed has been an impediment to routine application. The recent availability of low-cost graphics processing unit (GPU) chipsets with thousands of processing cores, together with the implementation of the accurate GBSW implicit solvent model on those chipsets (Arthur and Brooks, J. Comput. Chem. 2016, 37, 927), provide an opportunity to improve the speed of CPHMD and ionization modeling greatly. Here, we present a first implementation of GPU-enabled CPHMD within the CHARMM-OpenMM simulation package interface. Depending on the system size and nonbonded force cutoff parameters, we find speed increases of between one and three orders of magnitude. Additionally, the algorithm scales better with system size than the CPU-based algorithm, thus allowing for larger systems to be modeled in a cost effective manner. We anticipate that the improved performance of this methodology will open the door for broad-spread application of CPHMD in its modeling pH-mediated biological processes. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

F-BAR family proteins, emerging regulators for cell membrane dynamic changes-from structure to human diseases.

PubMed

Liu, Suxuan; Xiong, Xinyu; Zhao, Xianxian; Yang, Xiaofeng; Wang, Hong

2015-05-09

Eukaryotic cell membrane dynamics change in curvature during physiological and pathological processes. In the past ten years, a novel protein family, Fes/CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain proteins, has been identified to be the most important coordinators in membrane curvature regulation. The F-BAR domain family is a member of the Bin/Amphiphysin/Rvs (BAR) domain superfamily that is associated with dynamic changes in cell membrane. However, the molecular basis in membrane structure regulation and the biological functions of F-BAR protein are unclear. The pathophysiological role of F-BAR protein is unknown. This review summarizes the current understanding of structure and function in the BAR domain superfamily, classifies F-BAR family proteins into nine subfamilies based on domain structure, and characterizes F-BAR protein structure, domain interaction, and functional relevance. In general, F-BAR protein binds to cell membrane via F-BAR domain association with membrane phospholipids and initiates membrane curvature and scission via Src homology-3 (SH3) domain interaction with its partner proteins. This process causes membrane dynamic changes and leads to seven important cellular biological functions, which include endocytosis, phagocytosis, filopodium, lamellipodium, cytokinesis, adhesion, and podosome formation, via distinct signaling pathways determined by specific domain-binding partners. These cellular functions play important roles in many physiological and pathophysiological processes. We further summarize F-BAR protein expression and mutation changes observed in various diseases and developmental disorders. Considering the structure feature and functional implication of F-BAR proteins, we anticipate that F-BAR proteins modulate physiological and pathophysiological processes via transferring extracellular materials, regulating cell trafficking and mobility, presenting antigens, mediating extracellular matrix degradation, and transmitting signaling for cell proliferation.

Trends in fluorescence imaging and related techniques to unravel biological information.

PubMed

Haustein, Elke; Schwille, Petra

2007-09-01

Optical microscopy is among the most powerful tools that the physical sciences have ever provided biology. It is indispensable for basic lab work, as well as for cutting edge research, as the visual monitoring of life processes still belongs to the most compelling evidences for a multitude of biomedical applications. Along with the rapid development of new probes and methods for the analysis of laser induced fluorescence, optical microscopy over past years experienced a vast increase of both new techniques and novel combinations of established methods to study biological processes with unprecedented spatial and temporal precision. On the one hand, major technical advances have significantly improved spatial resolution. On the other hand, life scientists are moving toward three- and even four-dimensional cell biology and biophysics involving time as a crucial coordinate to quantitatively understand living specimen. Monitoring the whole cell or tissue in real time, rather than producing snap-shot-like two-dimensional projections, will enable more physiological and, thus, more clinically relevant experiments, whereas an increase in temporal resolution facilitates monitoring fast nonperiodic processes as well as the quantitative analysis of characteristic dynamics.

Trends in fluorescence imaging and related techniques to unravel biological information

PubMed Central

Haustein, Elke; Schwille, Petra

2007-01-01

Optical microscopy is among the most powerful tools that the physical sciences have ever provided biology. It is indispensable for basic lab work, as well as for cutting edge research, as the visual monitoring of life processes still belongs to the most compelling evidences for a multitude of biomedical applications. Along with the rapid development of new probes and methods for the analysis of laser induced fluorescence, optical microscopy over past years experienced a vast increase of both new techniques and novel combinations of established methods to study biological processes with unprecedented spatial and temporal precision. On the one hand, major technical advances have significantly improved spatial resolution. On the other hand, life scientists are moving toward three- and even four-dimensional cell biology and biophysics involving time as a crucial coordinate to quantitatively understand living specimen. Monitoring the whole cell or tissue in real time, rather than producing snap-shot-like two-dimensional projections, will enable more physiological and, thus, more clinically relevant experiments, whereas an increase in temporal resolution facilitates monitoring fast nonperiodic processes as well as the quantitative analysis of characteristic dynamics. PMID:19404444

Dynamic single photon emission computed tomography—basic principles and cardiac applications

PubMed Central

Gullberg, Grant T; Reutter, Bryan W; Sitek, Arkadiusz; Maltz, Jonathan S; Budinger, Thomas F

2011-01-01

The very nature of nuclear medicine, the visual representation of injected radiopharmaceuticals, implies imaging of dynamic processes such as the uptake and wash-out of radiotracers from body organs. For years, nuclear medicine has been touted as the modality of choice for evaluating function in health and disease. This evaluation is greatly enhanced using single photon emission computed tomography (SPECT), which permits three-dimensional (3D) visualization of tracer distributions in the body. However, to fully realize the potential of the technique requires the imaging of in vivo dynamic processes of flow and metabolism. Tissue motion and deformation must also be addressed. Absolute quantification of these dynamic processes in the body has the potential to improve diagnosis. This paper presents a review of advancements toward the realization of the potential of dynamic SPECT imaging and a brief history of the development of the instrumentation. A major portion of the paper is devoted to the review of special data processing methods that have been developed for extracting kinetics from dynamic cardiac SPECT data acquired using rotating detector heads that move as radiopharmaceuticals exchange between biological compartments. Recent developments in multi-resolution spatiotemporal methods enable one to estimate kinetic parameters of compartment models of dynamic processes using data acquired from a single camera head with slow gantry rotation. The estimation of kinetic parameters directly from projection measurements improves bias and variance over the conventional method of first reconstructing 3D dynamic images, generating time–activity curves from selected regions of interest and then estimating the kinetic parameters from the generated time–activity curves. Although the potential applications of SPECT for imaging dynamic processes have not been fully realized in the clinic, it is hoped that this review illuminates the potential of SPECT for dynamic imaging, especially in light of new developments that enable measurement of dynamic processes directly from projection measurements. PMID:20858925

TOPICAL REVIEW: Dynamic single photon emission computed tomography—basic principles and cardiac applications

NASA Astrophysics Data System (ADS)

Gullberg, Grant T.; Reutter, Bryan W.; Sitek, Arkadiusz; Maltz, Jonathan S.; Budinger, Thomas F.

2010-10-01

The very nature of nuclear medicine, the visual representation of injected radiopharmaceuticals, implies imaging of dynamic processes such as the uptake and wash-out of radiotracers from body organs. For years, nuclear medicine has been touted as the modality of choice for evaluating function in health and disease. This evaluation is greatly enhanced using single photon emission computed tomography (SPECT), which permits three-dimensional (3D) visualization of tracer distributions in the body. However, to fully realize the potential of the technique requires the imaging of in vivo dynamic processes of flow and metabolism. Tissue motion and deformation must also be addressed. Absolute quantification of these dynamic processes in the body has the potential to improve diagnosis. This paper presents a review of advancements toward the realization of the potential of dynamic SPECT imaging and a brief history of the development of the instrumentation. A major portion of the paper is devoted to the review of special data processing methods that have been developed for extracting kinetics from dynamic cardiac SPECT data acquired using rotating detector heads that move as radiopharmaceuticals exchange between biological compartments. Recent developments in multi-resolution spatiotemporal methods enable one to estimate kinetic parameters of compartment models of dynamic processes using data acquired from a single camera head with slow gantry rotation. The estimation of kinetic parameters directly from projection measurements improves bias and variance over the conventional method of first reconstructing 3D dynamic images, generating time-activity curves from selected regions of interest and then estimating the kinetic parameters from the generated time-activity curves. Although the potential applications of SPECT for imaging dynamic processes have not been fully realized in the clinic, it is hoped that this review illuminates the potential of SPECT for dynamic imaging, especially in light of new developments that enable measurement of dynamic processes directly from projection measurements.

Multi-level and hybrid modelling approaches for systems biology.

PubMed

Bardini, R; Politano, G; Benso, A; Di Carlo, S

2017-01-01

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

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…

Accelerated molecular dynamics simulations of the octopamine receptor using GPUs: discovery of an alternate agonist-binding position.

PubMed

Kastner, Kevin W; Izaguirre, Jesús A

2016-10-01

Octopamine receptors (OARs) perform key biological functions in invertebrates, making this class of G-protein coupled receptors (GPCRs) worth considering for insecticide development. However, no crystal structures and very little research exists for OARs. Furthermore, GPCRs are large proteins, are suspended in a lipid bilayer, and are activated on the millisecond timescale, all of which make conventional molecular dynamics (MD) simulations infeasible, even if run on large supercomputers. However, accelerated Molecular Dynamics (aMD) simulations can reduce this timescale to even hundreds of nanoseconds, while running the simulations on graphics processing units (GPUs) would enable even small clusters of GPUs to have processing power equivalent to hundreds of CPUs. Our results show that aMD simulations run on GPUs can successfully obtain the active and inactive state conformations of a GPCR on this reduced timescale. Furthermore, we discovered a potential alternate active-state agonist-binding position in the octopamine receptor which has yet to be observed and may be a novel GPCR agonist-binding position. These results demonstrate that a complex biological system with an activation process on the millisecond timescale can be successfully simulated on the nanosecond timescale using a simple computing system consisting of a small number of GPUs. Proteins 2016; 84:1480-1489. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Trace Water as Prominent Factor to Induce Peptide Self-Assembly: Dynamic Evolution and Governing Interactions in Ionic Liquids.

PubMed

Wang, Juan; Yuan, Chengqian; Han, Yuchun; Wang, Yilin; Liu, Xiaomin; Zhang, Suojiang; Yan, Xuehai

2017-11-01

The interaction between water and biomolecules including peptides is of critical importance for forming high-level architectures and triggering life's functions. However, the bulk aqueous environment has limitations in detecting the kinetics and mechanisms of peptide self-assembly, especially relating to interactions of trace water. With ionic liquids (ILs) as a nonconventional medium, herein, it is discovered that trace amounts of water play a decisive role in triggering self-assembly of a biologically derived dipeptide. ILs provide a suitable nonaqueous environment, enabling us to mediate water content and follow the dynamic evolution of peptide self-assembly. The trace water is found to be involved in the assembly process of dipeptide, especially leading to the formation of stable noncovalent dipeptide oligomers in the early stage of nucleation, as evident by both experimental studies and theoretical simulations. The thermodynamics of the growth process is mainly governed by a synergistic effect of hydrophobic interaction and hydrogen bonds. Each step of assembly presents a different trend in thermodynamic energy. The dynamic evolution of assembly process can be efficiently mediated by changing trace water content. The decisive role of trace water in triggering and mediating self-assembly of biomolecules provides a new perspective in understanding supramolecular chemistry and molecular self-organization in biology. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evolutionary games under incompetence.

PubMed

Kleshnina, Maria; Filar, Jerzy A; Ejov, Vladimir; McKerral, Jody C

2018-02-26

The adaptation process of a species to a new environment is a significant area of study in biology. As part of natural selection, adaptation is a mutation process which improves survival skills and reproductive functions of species. Here, we investigate this process by combining the idea of incompetence with evolutionary game theory. In the sense of evolution, incompetence and training can be interpreted as a special learning process. With focus on the social side of the problem, we analyze the influence of incompetence on behavior of species. We introduce an incompetence parameter into a learning function in a single-population game and analyze its effect on the outcome of the replicator dynamics. Incompetence can change the outcome of the game and its dynamics, indicating its significance within what are inherently imperfect natural systems.

The Dynamic Surface Tension of Water

PubMed Central

2017-01-01

The surface tension of water is an important parameter for many biological or industrial processes, and roughly a factor of 3 higher than that of nonpolar liquids such as oils, which is usually attributed to hydrogen bonding and dipolar interactions. Here we show by studying the formation of water drops that the surface tension of a freshly created water surface is even higher (∼90 mN m–1) than under equilibrium conditions (∼72 mN m–1) with a relaxation process occurring on a long time scale (∼1 ms). Dynamic adsorption effects of protons or hydroxides may be at the origin of this dynamic surface tension. However, changing the pH does not significantly change the dynamic surface tension. It also seems unlikely that hydrogen bonding or dipole orientation effects play any role at the relatively long time scale probed in the experiments. PMID:28301160

The Dynamic Surface Tension of Water.

PubMed

Hauner, Ines M; Deblais, Antoine; Beattie, James K; Kellay, Hamid; Bonn, Daniel

2017-04-06

The surface tension of water is an important parameter for many biological or industrial processes, and roughly a factor of 3 higher than that of nonpolar liquids such as oils, which is usually attributed to hydrogen bonding and dipolar interactions. Here we show by studying the formation of water drops that the surface tension of a freshly created water surface is even higher (∼90 mN m -1 ) than under equilibrium conditions (∼72 mN m -1 ) with a relaxation process occurring on a long time scale (∼1 ms). Dynamic adsorption effects of protons or hydroxides may be at the origin of this dynamic surface tension. However, changing the pH does not significantly change the dynamic surface tension. It also seems unlikely that hydrogen bonding or dipole orientation effects play any role at the relatively long time scale probed in the experiments.

TOPICAL REVIEW: The restaurant at the end of the random walk: recent developments in the description of anomalous transport by fractional dynamics

NASA Astrophysics Data System (ADS)

Metzler, Ralf; Klafter, Joseph

2004-08-01

Fractional dynamics has experienced a firm upswing during the past few years, having been forged into a mature framework in the theory of stochastic processes. A large number of research papers developing fractional dynamics further, or applying it to various systems have appeared since our first review article on the fractional Fokker-Planck equation (Metzler R and Klafter J 2000a, Phys. Rep. 339 1-77). It therefore appears timely to put these new works in a cohesive perspective. In this review we cover both the theoretical modelling of sub- and superdiffusive processes, placing emphasis on superdiffusion, and the discussion of applications such as the correct formulation of boundary value problems to obtain the first passage time density function. We also discuss extensively the occurrence of anomalous dynamics in various fields ranging from nanoscale over biological to geophysical and environmental systems.

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.

Exploiting single-cell variability to infer the dynamics of immune responses

NASA Astrophysics Data System (ADS)

Höfer, Thomas

Cell division, differentiation, migration and death determine the dynamics of immune responses. These processes are regulated by a multitude of biochemical signals which, at present, cannot faithfully be reconstituted outside the living organism. However, quantitative measurements in living organisms have been limited. In recent years experimental techniques for the ``fate mapping'' of single immune cells have been developed that allow performing parallel single-cell experiments in an experimental animal. The resulting data are more informative about underlying biological processes than traditional measurements. I will show how the theory of stochastic dynamical systems can be used to infer the topology and dynamics of cell differentiation pathways from such data. The focus will be on joint theoretical and experimental work addressing: (i) the development of immune cells during hematopoiesis, and (ii) T cell responses to diverse pathogens. I will discuss questions on the nature of cellular variability that are posed by these new findings.

Hydration status and diurnal trophic interactions shape microbial community function in desert biocrusts

NASA Astrophysics Data System (ADS)

Kim, Minsu; Or, Dani

2017-12-01

Biological soil crusts (biocrusts) are self-organised thin assemblies of microbes, lichens, and mosses that are ubiquitous in arid regions and serve as important ecological and biogeochemical hotspots. Biocrust ecological function is intricately shaped by strong gradients of water, light, oxygen, and dynamics in the abundance and spatial organisation of the microbial community within a few millimetres of the soil surface. We report a mechanistic model that links the biophysical and chemical processes that shape the functioning of biocrust representative microbial communities that interact trophically and respond dynamically to cycles of hydration, light, and temperature. The model captures key features of carbon and nitrogen cycling within biocrusts, such as microbial activity and distribution (during early stages of biocrust establishment) under diurnal cycles and the associated dynamics of biogeochemical fluxes at different hydration conditions. The study offers new insights into the highly dynamic and localised processes performed by microbial communities within thin desert biocrusts.

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.

Kinetics from Replica Exchange Molecular Dynamics Simulations.

PubMed

Stelzl, Lukas S; Hummer, Gerhard

2017-08-08

Transitions between metastable states govern many fundamental processes in physics, chemistry and biology, from nucleation events in phase transitions to the folding of proteins. The free energy surfaces underlying these processes can be obtained from simulations using enhanced sampling methods. However, their altered dynamics makes kinetic and mechanistic information difficult or impossible to extract. Here, we show that, with replica exchange molecular dynamics (REMD), one can not only sample equilibrium properties but also extract kinetic information. For systems that strictly obey first-order kinetics, the procedure to extract rates is rigorous. For actual molecular systems whose long-time dynamics are captured by kinetic rate models, accurate rate coefficients can be determined from the statistics of the transitions between the metastable states at each replica temperature. We demonstrate the practical applicability of the procedure by constructing master equation (Markov state) models of peptide and RNA folding from REMD simulations.

Nitrate denitrification with nitrite or nitrous oxide as intermediate products: Stoichiometry, kinetics and dynamics of stable isotope signatures.

PubMed

Vavilin, V A; Rytov, S V

2015-09-01

A kinetic analysis of nitrate denitrification by a single or two species of denitrifying bacteria with glucose or ethanol as a carbon source and nitrite or nitrous oxide as intermediate products was performed using experimental data published earlier (Menyailo and Hungate, 2006; Vidal-Gavilan et al., 2013). Modified Monod kinetics was used in the dynamic biological model. The special equations were added to the common dynamic biological model to describe how isotopic fractionation between N species changes. In contrast to the generally assumed first-order kinetics, in this paper, the traditional Rayleigh equation describing stable nitrogen and oxygen isotope fractionation in nitrate was derived from the dynamic isotopic equations for any type of kinetics. In accordance with the model, in Vidal-Gavilan's experiments, the maximum specific rate of nitrate reduction was proved to be less for ethanol compared to glucose. Conversely, the maximum specific rate of nitrite reduction was proved to be much less for glucose compared to ethanol. Thus, the intermediate nitrite concentration was negligible for the ethanol experiment, while it was significant for the glucose experiment. In Menyailo's and Hungate's experiments, the low value of maximum specific rate of nitrous oxide reduction gives high intermediate value of nitrous oxide concentration. The model showed that the dynamics of nitrogen and oxygen isotope signatures are responding to the biological dynamics. Two microbial species instead of single denitrifying bacteria are proved to be more adequate to describe the total process of nitrate denitrification to dinitrogen. Copyright © 2015 Elsevier Ltd. All rights reserved.

The relativity of biological function.

PubMed

Laubichler, Manfred D; Stadler, Peter F; Prohaska, Sonja J; Nowick, Katja

2015-12-01

Function is a central concept in biological theories and explanations. Yet discussions about function are often based on a narrow understanding of biological systems and processes, such as idealized molecular systems or simple evolutionary, i.e., selective, dynamics. Conflicting conceptions of function continue to be used in the scientific literature to support certain claims, for instance about the fraction of "functional DNA" in the human genome. Here we argue that all biologically meaningful interpretations of function are necessarily context dependent. This implies that they derive their meaning as well as their range of applicability only within a specific theoretical and measurement context. We use this framework to shed light on the current debate about functional DNA and argue that without considering explicitly the theoretical and measurement contexts all attempts to integrate biological theories are prone to fail.

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.

Guided by curvature: shaping cells by coupling curved membrane proteins and cytoskeletal forces.

PubMed

Gov, N S

2018-05-26

Eukaryote cells have flexible membranes that allow them to have a variety of dynamical shapes. The shapes of the cells serve important biological functions, both for cells within an intact tissue, and during embryogenesis and cellular motility. How cells control their shapes and the structures that they form on their surface has been a subject of intensive biological research, exposing the building blocks that cells use to deform their membranes. These processes have also drawn the interest of theoretical physicists, aiming to develop models based on physics, chemistry and nonlinear dynamics. Such models explore quantitatively different possible mechanisms that the cells can employ to initiate the spontaneous formation of shapes and patterns on their membranes. We review here theoretical work where one such class of mechanisms was investigated: the coupling between curved membrane proteins, and the cytoskeletal forces that they recruit. Theory indicates that this coupling gives rise to a rich variety of membrane shapes and dynamics, while experiments indicate that this mechanism appears to drive many cellular shape changes.This article is part of the theme issue 'Self-organization in cell biology'. © 2018 The Author(s).

An Earth's Future Special Collection: Impacts of the coastal dynamics of sea level rise on low-gradient coastal landscapes

NASA Astrophysics Data System (ADS)

Kidwell, David M.; Dietrich, J. Casey; Hagen, Scott C.; Medeiros, Stephen C.

2017-01-01

Rising sea level represents a significant threat to coastal communities and ecosystems, including altered habitats and increased vulnerability to coastal storms and recurrent inundation. This threat is exemplified in the northern Gulf of Mexico, where low topography, marshes, and a prevalence of tropical storms have resulted in extensive coastal impacts. The ability to facilitate adaptation and mitigation measures relies, in part, on the development of robust predictive capabilities that incorporate complex biological processes with physical dynamics. Initiated in 2010, the 6-year Ecological Effects of Sea Level Rise—Northern Gulf of Mexico project applied a transdisciplinary science approach to develop a suite of integrated modeling platforms informed by empirical data that are capable of evaluating a range of climate change scenarios. This special issue highlights resultant integrated models focused on tidal hydrodynamics, shoreline morphology, oyster ecology, coastal wetland vulnerability, and storm surges that demonstrate the need for dynamic models to incorporate feedbacks among physical and biological processes in assessments of sea level rise effects on coastal systems. Effects are projected to be significant, spatially variable and nonlinear relative to sea level rise rates. Scenarios of higher sea level rise rates are projected to exceed thresholds of wetland sustainability, and many regions will experience enhanced storm surges. Influenced by an extensive collaborative stakeholder engagement process, these assessments on the coastal dynamics of sea level rise provide a strong foundation for resilience measures in the northern Gulf of Mexico and a transferable approach for application to other coastal regions throughout the world.

Transcriptome dynamics along axolotl regenerative development are consistent with an extensive reduction in gene expression heterogeneity in dedifferentiated cells

PubMed Central

2017-01-01

Although in recent years the study of gene expression variation in the absence of genetic or environmental cues or gene expression heterogeneity has intensified considerably, many basic and applied biological fields still remain unaware of how useful the study of gene expression heterogeneity patterns might be for the characterization of biological systems and/or processes. Largely based on the modulator effect chromatin compaction has for gene expression heterogeneity and the extensive changes in chromatin compaction known to occur for specialized cells that are naturally or artificially induced to revert to less specialized states or dedifferentiate, I recently hypothesized that processes that concur with cell dedifferentiation would show an extensive reduction in gene expression heterogeneity. The confirmation of the existence of such trend could be of wide interest because of the biomedical and biotechnological relevance of cell dedifferentiation-based processes, i.e., regenerative development, cancer, human induced pluripotent stem cells, or plant somatic embryogenesis. Here, I report the first empirical evidence consistent with the existence of an extensive reduction in gene expression heterogeneity for processes that concur with cell dedifferentiation by analyzing transcriptome dynamics along forearm regenerative development in Ambystoma mexicanum or axolotl. Also, I briefly discuss on the utility of the study of gene expression heterogeneity dynamics might have for the characterization of cell dedifferentiation-based processes, and the engineering of tools that afforded better monitoring and modulating such processes. Finally, I reflect on how a transitional reduction in gene expression heterogeneity for dedifferentiated cells can promote a long-term increase in phenotypic heterogeneity following cell dedifferentiation with potential adverse effects for biomedical and biotechnological applications. PMID:29134148

Vaporous Hydrogen Peroxide (VHP) Decontamination of a C-141B Starlifter Aircraft: Validation of VHP and Modified VHP (mVHP) Fumigation Decontamination Process via VHP-Sensor, Biological Indicator, and HD Simulant in a Large-Scale Environment

DTIC Science & Technology

2007-03-01

Geobacillus stearothermophilus biological indicator (BI) strips and coupons of three aircraft related surface materials contaminated with the same type...Starlifter Aircraft BIs Geobacillus stearothermophilus mVHP system Vaporizer modules Coupons HD Ammonia Computational flow dynamics CARC CEPS 16. SECURITY...21 18. G. stearothermophilus ATCC 7953VHP Exposure Test Results ..................... 33 19. Vapor Cup

Two-Dimensional Spectroscopy Is Being Used to Address Core Scientific Questions in Biology and Materials Science.

PubMed

Petti, Megan K; Lomont, Justin P; Maj, Michał; Zanni, Martin T

2018-02-15

Two-dimensional spectroscopy is a powerful tool for extracting structural and dynamic information from a wide range of chemical systems. We provide a brief overview of the ways in which two-dimensional visible and infrared spectroscopies are being applied to elucidate fundamental details of important processes in biological and materials science. The topics covered include amyloid proteins, photosynthetic complexes, ion channels, photovoltaics, batteries, as well as a variety of promising new methods in two-dimensional spectroscopy.

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.

Probabilistic Perception, Empathy, and Dynamic Homeostasis: Insights in Autism Spectrum Disorders and Conduct Disorders

PubMed Central

Guilé, Jean Marc

2013-01-01

Homeostasis is not a permanent and stable state but instead results from conflicting forces. Therefore, infants have to engage in dynamic exchanges with their environment, in biological, cognitive, and affective domains. Empathy is an adaptive response to these environmental challenges, which contributes to reaching proper dynamic homeostasis and development. Empathy relies on implicit interactive processes, namely probabilistic perception and synchrony, which will be reviewed in the article. If typically-developed neonates are fully equipped to automatically and synchronously interact with their human environment, conduct disorders (CD) and autism spectrum disorders (ASD) present with impairments in empathetic communication, e.g., emotional arousal and facial emotion processing. In addition sensorimotor resonance is lacking in ASD, and emotional concern and semantic empathy are impaired in CD with Callous-Unemotional traits. PMID:24479115

Fixation, transient landscape, and diffusion dilemma in stochastic evolutionary game dynamics

NASA Astrophysics Data System (ADS)

Zhou, Da; Qian, Hong

2011-09-01

Agent-based stochastic models for finite populations have recently received much attention in the game theory of evolutionary dynamics. Both the ultimate fixation and the pre-fixation transient behavior are important to a full understanding of the dynamics. In this paper, we study the transient dynamics of the well-mixed Moran process through constructing a landscape function. It is shown that the landscape playing a central theoretical “device” that integrates several lines of inquiries: the stable behavior of the replicator dynamics, the long-time fixation, and continuous diffusion approximation associated with asymptotically large population. Several issues relating to the transient dynamics are discussed: (i) multiple time scales phenomenon associated with intra- and inter-attractoral dynamics; (ii) discontinuous transition in stochastically stationary process akin to Maxwell construction in equilibrium statistical physics; and (iii) the dilemma diffusion approximation facing as a continuous approximation of the discrete evolutionary dynamics. It is found that rare events with exponentially small probabilities, corresponding to the uphill movements and barrier crossing in the landscape with multiple wells that are made possible by strong nonlinear dynamics, plays an important role in understanding the origin of the complexity in evolutionary, nonlinear biological systems.

The heparanome--the enigma of encoding and decoding heparan sulfate sulfation.

PubMed

Lamanna, William C; Kalus, Ina; Padva, Michael; Baldwin, Rebecca J; Merry, Catherine L R; Dierks, Thomas

2007-04-30

Heparan sulfate (HS) is a cell surface carbohydrate polymer modified with sulfate moieties whose highly ordered composition is central to directing specific cell signaling events. The ability of the cell to generate these information rich glycans with such specificity has opened up a new field of "heparanomics" which seeks to understand the systems involved in generating these cell type and developmental stage specific HS sulfation patterns. Unlike other instances where biological information is encrypted as linear sequences in molecules such as DNA, HS sulfation patterns are generated through a non-template driven process. Thus, deciphering the sulfation code and the dynamic nature of its generation has posed a new challenge to system biologists. The recent discovery of two sulfatases, Sulf1 and Sulf2, with the unique ability to edit sulfation patterns at the cell surface, has opened up a new dimension as to how we understand the regulation of HS sulfation patterning and pattern-dependent cell signaling events. This review will focus on the functional relationship between HS sulfation patterning and biological processes. Special attention will be given to Sulf1 and Sulf2 and how these key editing enzymes might act in concert with the HS biosynthetic enzymes to generate and regulate specific HS sulfation patterns in vivo. We will further explore the use of knock out mice as biological models for understanding the dynamic systems involved in generating HS sulfation patterns and their biological relevance. A brief overview of new technologies and innovations summarizes advances in the systems biology field for understanding non-template molecular networks and their influence on the "heparanome".

Network structure, topology, and dynamics in generalized models of synchronization

NASA Astrophysics Data System (ADS)

Lerman, Kristina; Ghosh, Rumi

2012-08-01

Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.

Bacteriophage ecology in environmental biotechnology processes.

PubMed

Shapiro, Orr H; Kushmaro, Ariel

2011-06-01

Heterotrophic bacteria are an integral part of any environmental biotechnology process (EBP). Therefore, factors controlling bacterial abundance, activity, and community composition are central to the understanding of such processes. Among these factors, top-down control by bacteriophage predation has so far received very limited attention. With over 10(8) particles per ml, phage appear to be the most numerous biological entities in EBP. Phage populations in EBP appear to be highly dynamic and to correlate with the population dynamics of their hosts and genomic evidence suggests bacteria evolve to avoid phage predation. Clearly, there is much to learn regarding bacteriophage in EBP before we can truly understand the microbial ecology of these globally important systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

Identification of Biokinetic Models Using the Concept of Extents.

PubMed

Mašić, Alma; Srinivasan, Sriniketh; Billeter, Julien; Bonvin, Dominique; Villez, Kris

2017-07-05

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

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.

A comparison of form processing involved in the perception of biological and nonbiological movements

PubMed Central

Thurman, Steven M.; Lu, Hongjing

2016-01-01

Although there is evidence for specialization in the human brain for processing biological motion per se, few studies have directly examined the specialization of form processing in biological motion perception. The current study was designed to systematically compare form processing in perception of biological (human walkers) to nonbiological (rotating squares) stimuli. Dynamic form-based stimuli were constructed with conflicting form cues (position and orientation), such that the objects were perceived to be moving ambiguously in two directions at once. In Experiment 1, we used the classification image technique to examine how local form cues are integrated across space and time in a bottom-up manner. By comparing with a Bayesian observer model that embodies generic principles of form analysis (e.g., template matching) and integrates form information according to cue reliability, we found that human observers employ domain-general processes to recognize both human actions and nonbiological object movements. Experiments 2 and 3 found differential top-down effects of spatial context on perception of biological and nonbiological forms. When a background does not involve social information, observers are biased to perceive foreground object movements in the direction opposite to surrounding motion. However, when a background involves social cues, such as a crowd of similar objects, perception is biased toward the same direction as the crowd for biological walking stimuli, but not for rotating nonbiological stimuli. The model provided an accurate account of top-down modulations by adjusting the prior probabilities associated with the internal templates, demonstrating the power and flexibility of the Bayesian approach for visual form perception. PMID:26746875

Enzyme-Activated Fluorogenic Probes for Live-Cell and in Vivo Imaging.

PubMed

Chyan, Wen; Raines, Ronald T

2018-06-20

Fluorogenic probes, small-molecule sensors that unmask brilliant fluorescence upon exposure to specific stimuli, are powerful tools for chemical biology. Those probes that respond to enzymatic activity illuminate the complex dynamics of biological processes at a level of spatiotemporal detail and sensitivity unmatched by other techniques. Here, we review recent advances in enzyme-activated fluorogenic probes for biological imaging. We organize our survey by enzyme classification, with emphasis on fluorophore masking strategies, modes of enzymatic activation, and the breadth of current and future applications. Key challenges such as probe selectivity and spectroscopic requirements are described alongside of therapeutic, diagnostic, and theranostic opportunities.

Biological forcing controls the chemistry of reef-building coral skeleton

NASA Astrophysics Data System (ADS)

Meibom, Anders; Mostefaoui, Smail; Cuif, Jean-Pierre; Dauphin, Yannicke; Houlbreque, Fanny; Dunbar, Robert; Constantz, Brent

2007-01-01

We present analyses of major elements C and Ca and trace elements N, S, Mg and Sr in a Porites sp. exoskeleton with a spatial resolution better than ˜150 nm. Trace element variations are evaluated directly against the ultra-structure of the skeleton and are ascribed to dynamic biological forcing. Individual growth layers in the bulk fibrous aragonite skeleton form on sub-daily timescales. Magnesium concentration variations are dramatically correlated with the growth layers, but are uncorrelated with Sr concentration variations. Observed (sub)seasonal relationships between water temperature and skeletal trace-element chemistry are secondary, mediated by sensitive biological processes to which classical thermodynamic formalism does not apply.

Computer retina that models the primate retina

NASA Astrophysics Data System (ADS)

Shah, Samir; Levine, Martin D.

1994-06-01

At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or `smart' sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The model exhibits many of the properties found in biological retina such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatio temporal contrast information. The model is validated by replicating experiments commonly performed by electrophysiologists on biological retinas and comparing the response of the computer retina to data from experiments in monkeys. In addition, the response of the model to synthetic images is shown. The experiments demonstrate that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an `artificial retina.'

Biological and physical controls in the Southern Ocean on past millennial-scale atmospheric CO2 changes.

PubMed

Gottschalk, Julia; Skinner, Luke C; Lippold, Jörg; Vogel, Hendrik; Frank, Norbert; Jaccard, Samuel L; Waelbroeck, Claire

2016-05-17

Millennial-scale climate changes during the last glacial period and deglaciation were accompanied by rapid changes in atmospheric CO2 that remain unexplained. While the role of the Southern Ocean as a 'control valve' on ocean-atmosphere CO2 exchange has been emphasized, the exact nature of this role, in particular the relative contributions of physical (for example, ocean dynamics and air-sea gas exchange) versus biological processes (for example, export productivity), remains poorly constrained. Here we combine reconstructions of bottom-water [O2], export production and (14)C ventilation ages in the sub-Antarctic Atlantic, and show that atmospheric CO2 pulses during the last glacial- and deglacial periods were consistently accompanied by decreases in the biological export of carbon and increases in deep-ocean ventilation via southern-sourced water masses. These findings demonstrate how the Southern Ocean's 'organic carbon pump' has exerted a tight control on atmospheric CO2, and thus global climate, specifically via a synergy of both physical and biological processes.

[The use of biological age on mental work capacity model in accelerated aging assessment of professional lorry-drivers].

PubMed

Bashkireva, A S

2012-01-01

The studies of biological age, aging rate, mental work capacity in professional drivers were conducted. The examination revealed peculiarities of system organization of functions determining the mental work capacity levels. Dynamics of the aging process of professional driver's organism in relation with calendar age and driving experience were shown using the biological age model. The results point at the premature decrease of the mental work capacity in professional drivers. It was proved, that premature age-related changes of physiologic and psychophysiologic indices in drivers are just "risk indicators", while long driving experience is a real risk factor, accelerating the aging process. The "risk group" with manifestations of accelerating aging was observed in 40-49-year old drivers with 15-19 years of professional experience. The expediency of using the following methods for the age rate estimation according to biologic age indices and necessity of prophylactic measures for premature and accelerated aging prevention among working population was demonstrated.

Biological and physical controls in the Southern Ocean on past millennial-scale atmospheric CO2 changes

PubMed Central

Gottschalk, Julia; Skinner, Luke C.; Lippold, Jörg; Vogel, Hendrik; Frank, Norbert; Jaccard, Samuel L.; Waelbroeck, Claire

2016-01-01

Millennial-scale climate changes during the last glacial period and deglaciation were accompanied by rapid changes in atmospheric CO2 that remain unexplained. While the role of the Southern Ocean as a 'control valve' on ocean–atmosphere CO2 exchange has been emphasized, the exact nature of this role, in particular the relative contributions of physical (for example, ocean dynamics and air–sea gas exchange) versus biological processes (for example, export productivity), remains poorly constrained. Here we combine reconstructions of bottom-water [O2], export production and 14C ventilation ages in the sub-Antarctic Atlantic, and show that atmospheric CO2 pulses during the last glacial- and deglacial periods were consistently accompanied by decreases in the biological export of carbon and increases in deep-ocean ventilation via southern-sourced water masses. These findings demonstrate how the Southern Ocean's 'organic carbon pump' has exerted a tight control on atmospheric CO2, and thus global climate, specifically via a synergy of both physical and biological processes. PMID:27187527

The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach.

PubMed

Calçada, Dulce; Vianello, Dario; Giampieri, Enrico; Sala, Claudia; Castellani, Gastone; de Graaf, Albert; Kremer, Bas; van Ommen, Ben; Feskens, Edith; Santoro, Aurelia; Franceschi, Claudio; Bouwman, Jildau

2014-01-01

Aging is a biological process characterized by the progressive functional decline of many interrelated physiological systems. In particular, aging is associated with the development of a systemic state of low-grade chronic inflammation (inflammaging), and with progressive deterioration of metabolic function. Systems biology has helped in identifying the mediators and pathways involved in these phenomena, mainly through the application of high-throughput screening methods, valued for their molecular comprehensiveness. Nevertheless, inflammation and metabolic regulation are dynamical processes whose behavior must be understood at multiple levels of biological organization (molecular, cellular, organ, and system levels) and on multiple time scales. Mathematical modeling of such behavior, with incorporation of mechanistic knowledge on interactions between inflammatory and metabolic mediators, may help in devising nutritional interventions capable of preventing, or ameliorating, the age-associated functional decline of the corresponding systems. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion.

PubMed

Peelen, Marius V; Wiggett, Alison J; Downing, Paul E

2006-03-16

Accurate perception of the actions and intentions of other people is essential for successful interactions in a social environment. Several cortical areas that support this process respond selectively in fMRI to static and dynamic displays of human bodies and faces. Here we apply pattern-analysis techniques to arrive at a new understanding of the neural response to biological motion. Functionally defined body-, face-, and motion-selective visual areas all responded significantly to "point-light" human motion. Strikingly, however, only body selectivity was correlated, on a voxel-by-voxel basis, with biological motion selectivity. We conclude that (1) biological motion, through the process of structure-from-motion, engages areas involved in the analysis of the static human form; (2) body-selective regions in posterior fusiform gyrus and posterior inferior temporal sulcus overlap with, but are distinct from, face- and motion-selective regions; (3) the interpretation of region-of-interest findings may be substantially altered when multiple patterns of selectivity are considered.

The Spring of Systems Biology-Driven Breeding.

PubMed

Lavarenne, Jérémy; Guyomarc'h, Soazig; Sallaud, Christophe; Gantet, Pascal; Lucas, Mikaël

2018-05-12

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Satellite Cells and the Muscle Stem Cell Niche

PubMed Central

Yin, Hang; Price, Feodor

2013-01-01

Adult skeletal muscle in mammals is a stable tissue under normal circumstances but has remarkable ability to repair after injury. Skeletal muscle regeneration is a highly orchestrated process involving the activation of various cellular and molecular responses. As skeletal muscle stem cells, satellite cells play an indispensible role in this process. The self-renewing proliferation of satellite cells not only maintains the stem cell population but also provides numerous myogenic cells, which proliferate, differentiate, fuse, and lead to new myofiber formation and reconstitution of a functional contractile apparatus. The complex behavior of satellite cells during skeletal muscle regeneration is tightly regulated through the dynamic interplay between intrinsic factors within satellite cells and extrinsic factors constituting the muscle stem cell niche/microenvironment. For the last half century, the advance of molecular biology, cell biology, and genetics has greatly improved our understanding of skeletal muscle biology. Here, we review some recent advances, with focuses on functions of satellite cells and their niche during the process of skeletal muscle regeneration. PMID:23303905

Hydrogen bond dynamics in bulk alcohols.

PubMed

Shinokita, Keisuke; Cunha, Ana V; Jansen, Thomas L C; Pshenichnikov, Maxim S

2015-06-07

Hydrogen-bonded liquids play a significant role in numerous chemical and biological phenomena. In the past decade, impressive developments in multidimensional vibrational spectroscopy and combined molecular dynamics-quantum mechanical simulation have established many intriguing features of hydrogen bond dynamics in one of the fundamental solvents in nature, water. The next class of a hydrogen-bonded liquid--alcohols--has attracted much less attention. This is surprising given such important differences between water and alcohols as the imbalance between the number of hydrogen bonds, each molecule can accept (two) and donate (one) and the very presence of the hydrophobic group in alcohols. Here, we use polarization-resolved pump-probe and 2D infrared spectroscopy supported by extensive theoretical modeling to investigate hydrogen bond dynamics in methanol, ethanol, and isopropanol employing the OH stretching mode as a reporter. The sub-ps dynamics in alcohols are similar to those in water as they are determined by similar librational and hydrogen-bond stretch motions. However, lower density of hydrogen bond acceptors and donors in alcohols leads to the appearance of slow diffusion-controlled hydrogen bond exchange dynamics, which are essentially absent in water. We anticipate that the findings herein would have a potential impact on fundamental chemistry and biology as many processes in nature involve the interplay of hydrophobic and hydrophilic groups.

The micro and macro of nutrients across biological scales.

PubMed

Warne, Robin W

2014-11-01

During the past decade, we have gained new insights into the profound effects that essential micronutrients and macronutrients have on biological processes ranging from cellular function, to whole-organism performance, to dynamics in ecological communities, as well as to the structure and function of ecosystems. For example, disparities between intake and organismal requirements for specific nutrients are known to strongly affect animal physiological performance and impose trade-offs in the allocations of resources. However, recent findings have demonstrated that life-history allocation trade-offs and even microevolutionary dynamics may often be a result of molecular-level constraints on nutrient and metabolic processing, in which limiting reactants are routed among competing biochemical pathways. In addition, recent work has shown that complex ecological interactions between organismal physiological states such as exposure to environmental stressors and infectious pathogens can alter organismal requirements for, and, processing of, nutrients, and even alter subsequent nutrient cycling in ecosystems. Furthermore, new research is showing that such interactions, coupled with evolutionary and biogeographical constraints on the biosynthesis and availability of essential nutrients and micronutrients play an important, but still under-studied role in the structuring and functioning of ecosystems. The purpose of this introduction to the symposium "The Micro and Macro of Nutrient Effects in Animal Physiology and Ecology" is to briefly review and highlight recent research that has dramatically advanced our understanding of how nutrients in their varied forms profoundly affect and shape ecological and evolutionary processes. © The Author 2014. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

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.

Role of Biotic and Abiotic Processes on Soil CO2 Dynamics in the McMurdo Dry Valleys, Antarctica

NASA Astrophysics Data System (ADS)

Risk, D. A.; Macintyre, C. M.; Lee, C.; Cary, C.; Shanhun, F.; Almond, P. C.

2016-12-01

In the harsh conditions of the Antarctic Dry Valleys, microbial activity has been recorded via measurements of soil carbon dioxide (CO2) concentration and surface efflux. However, high temporal resolution studies in the Dry Valleys have also shown that abiotic solubility-driven processes can strongly influence (and perhaps even dominate) the CO2 dynamics in these low flux environments and suggests that biological activity may be lower than previously thought. In this study, we aim to improve our understanding of CO2 dynamics (biotic and abiotic) in Antarctic Dry Valley soils using long-term automated measurements of soil CO2 surface flux and soil profile concentration at several sites, often at sub-diel frequency. We hypothesize that soil CO2 variations are driven primarily by environmental factors affecting CO2 solubility in soil solution, mainly temperature, and that these processes may even overprint biologic production in representative Dry Valley soils. Monitoring of all sites revealed only one likely biotic CO2 production event, lasting three weeks during the Austral summer and reaching fluxes of 0.4 µmol/m2/s. Under more typical low flux conditions (<0.10 µmol/m2/s) we observed a cyclical daily sink/source pattern consistent with CO2 solubility cycling that would not generally have been evident with normal synoptic afternoon sampling campaigns. Subsurface CO2 monitoring and a lab-controlled Antarctic soil simulation experiment confirmed that abiotic processes are capable of dominating soil CO2 variability. Diel temperature cycles crossing the freezing boundary revealed a dual abiotic cycle of solubility cycling and gas exclusion from ice formation observed only by high temporal frequency measurements (30 min). This work demonstrates a need for a numerical model to partition the dynamic abiotic processes underlying any biotic CO2 production in order to understand potential climate-change induced increases in microbial productivity in terrestrial Antarctica.

Understanding the ecocline at shallow coasts of the Baltic Sea

NASA Astrophysics Data System (ADS)

Lenartz, B.; Jurasinski, G.; Voss, M.; Janssen, M.

2017-12-01

We report on results of the Research Training Group Baltic TRANSCOAST. The overall aim of Baltic TRANSCOAST is to enhance our knowledge of the shallow coast ecocline. How is the marine coastal zone influenced by terrestrial processes? How is the terrestrial coastal zone influenced by marine processes? These questions lead our research within the three research fields covering hydro-dynamic, (bio)geochemical and biological processes. Regarding the hydro-dynamics we assess how the peatland's water balance, the current dynamics and hydraulic properties of the marine sediments and the subsoil influence sea water intrusions into the peatland and/or submarine groundwater discharge into the Baltic Sea. With respect to (bio)geochemical processes we address how (bio)geochemical transformation processes both in the marine and the terrestrial part of the coast are influenced by water and matter inputs from the respective other coastal domain. Finally, reagrding the biological processes, we are interested in revealing how the primary production and the composition of the micro- and macro-phytobenthos in the shallow Baltic Sea influence matter transformation processes. The integrative approach of Baltic TRANSCOAST allows us to get to grips with questions that are otherwise hard to tackle. For instance, we address how the pore water constituents drive microbial processes and the deposition of nutrients and and how they are impacted by sediment resuspension and translocation. We investigate how the hydrology of the peat layers interferes with the generation of trace gases and investigate the role of the nearby Warnow river and its plume and how this changes under the impact of wind direction and wind strength. For the latter we rely on data and models. Further, as a common basis for all topics addressed in Baltic TRANSCOAST we established the geology of the study area and learned that regional variability may play a major role in shaping the processes under study.

What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633

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.

CLIMATIC EFFECTS ON TUNDRA CARBON STORAGE INFERRED FROM EXPERIMENTAL DATA AND A MODEL

EPA Science Inventory

We used a process-based model of ecosystem carbon (C) and nitrogen (N)dynamics, MBL-GEM (Marine Biological Laboratory General Ecosystem Model), to integrated and analyze the results of several experiments that examined the response of arctic tussock tundra to manipulations of CO2...

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

Holm, Christian; Gompper, Gerhard; Dill, Ken A.

This special issue highlights new developments in theory and coarse-graining in biological and synthetic macromolecules and membranes. Such approaches give unique insights into the principles and design of the structures, dynamics, and assembly processes of these complex fluids and soft materials, where the length and time scales are often prohibitively long for fully atomistic modeling.

Universal statistics of terminal dynamics before collapse

NASA Astrophysics Data System (ADS)

Lenner, Nicolas; Eule, Stephan; Wolf, Fred

Recent biological developments have both drastically increased the precision as well as amount of generated data, allowing for a switching from pure mean value characterization of the process under consideration to an analysis of the whole ensemble, exploiting the stochastic nature of biology. We focus on the general class of non-equilibrium processes with distinguished terminal points as can be found in cell fate decision, check points or cognitive neuroscience. Aligning the data to a terminal point (e.g. represented as an absorbing boundary) allows to device a general methodology to characterize and reverse engineer the terminating history. Using a small noise approximation we derive mean variance and covariance of the aligned data for general finite time singularities.

Inter-species competition-facilitation in stochastic riparian vegetation dynamics.

PubMed

Tealdi, Stefano; Camporeale, Carlo; Ridolfi, Luca

2013-02-07

Riparian vegetation is a highly dynamic community that lives on river banks and which depends to a great extent on the fluvial hydrology. The stochasticity of the discharge and erosion/deposition processes in fact play a key role in determining the distribution of vegetation along a riparian transect. These abiotic processes interact with biotic competition/facilitation mechanisms, such as plant competition for light, water, and nutrients. In this work, we focus on the dynamics of plants characterized by three components: (1) stochastic forcing due to river discharges, (2) competition for resources, and (3) inter-species facilitation due to the interplay between vegetation and fluid dynamics processes. A minimalist stochastic bio-hydrological model is proposed for the dynamics of the biomass of two vegetation species: one species is assumed dominant and slow-growing, the other is subdominant, but fast-growing. The stochastic model is solved analytically and the probability density function of the plant biomasses is obtained as a function of both the hydrologic and biologic parameters. The impact of the competition/facilitation processes on the distribution of vegetation species along the riparian transect is investigated and remarkable effects are observed. Finally, a good qualitative agreement is found between the model results and field data. Copyright © 2012 Elsevier Ltd. All rights reserved.

Understanding the biological underpinnings of ecohydrological processes

NASA Astrophysics Data System (ADS)

Huxman, T. E.; Scott, R. L.; Barron-Gafford, G. A.; Hamerlynck, E. P.; Jenerette, D.; Tissue, D. T.; Breshears, D. D.; Saleska, S. R.

2012-12-01

Climate change presents a challenge for predicting ecosystem response, as multiple factors drive both the physical and life processes happening on the land surface and their interactions result in a complex, evolving coupled system. For example, changes in surface temperature and precipitation influence near-surface hydrology through impacts on system energy balance, affecting a range of physical processes. These changes in the salient features of the environment affect biological processes and elicit responses along the hierarchy of life (biochemistry to community composition). Many of these structural or process changes can alter patterns of soil water-use and influence land surface characteristics that affect local climate. Of the many features that affect our ability to predict the future dynamics of ecosystems, it is this hierarchical response of life that creates substantial complexity. Advances in the ability to predict or understand aspects of demography help describe thresholds in coupled ecohydrological system. Disentangling the physical and biological features that underlie land surface dynamics following disturbance are allowing a better understanding of the partitioning of water in the time-course of recovery. Better predicting the timing of phenology and key seasonal events allow for a more accurate description of the full functional response of the land surface to climate. In addition, explicitly considering the hierarchical structural features of life are helping to describe complex time-dependent behavior in ecosystems. However, despite this progress, we have yet to build an ability to fully account for the generalization of the main features of living systems into models that can describe ecohydrological processes, especially acclimation, assembly and adaptation. This is unfortunate, given that many key ecosystem services are functions of these coupled co-evolutionary processes. To date, both the lack of controlled measurements and experimentation has precluded determination of sufficient theoretical development. Understanding the land-surface response and feedback to climate change requires a mechanistic understanding of the coupling of ecological and hydrological processes and an expansion of theory from the life sciences to appropriately contribute to the broader Earth system science goal.

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.

Analyzing the dynamics of cell cycle processes from fixed samples through ergodic principles

PubMed Central

Wheeler, Richard John

2015-01-01

Tools to analyze cyclical cellular processes, particularly the cell cycle, are of broad value for cell biology. Cell cycle synchronization and live-cell time-lapse observation are widely used to analyze these processes but are not available for many systems. Simple mathematical methods built on the ergodic principle are a well-established, widely applicable, and powerful alternative analysis approach, although they are less widely used. These methods extract data about the dynamics of a cyclical process from a single time-point “snapshot” of a population of cells progressing through the cycle asynchronously. Here, I demonstrate application of these simple mathematical methods to analysis of basic cyclical processes—cycles including a division event, cell populations undergoing unicellular aging, and cell cycles with multiple fission (schizogony)—as well as recent advances that allow detailed mapping of the cell cycle from continuously changing properties of the cell such as size and DNA content. This includes examples using existing data from mammalian, yeast, and unicellular eukaryotic parasite cell biology. Through the ongoing advances in high-throughput cell analysis by light microscopy, electron microscopy, and flow cytometry, these mathematical methods are becoming ever more important and are a powerful complementary method to traditional synchronization and time-lapse cell cycle analysis methods. PMID:26543196

A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level.

PubMed

Gasqui, Patrick; Trommenschlager, Jean-Marie

2017-08-21

Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.

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

Hess, Nancy J.; Pasa-Tolic, Ljiljana; Bailey, Vanessa L.

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

2013 R&D 100 Award: Movie-mode electron microscope captures nanoscale

ScienceCinema

Lagrange, Thomas; Reed, Bryan

2018-01-26

A new instrument developed by LLNL scientists and engineers, the Movie Mode Dynamic Transmission Electron Microscope (MM-DTEM), captures billionth-of-a-meter-scale images with frame rates more than 100,000 times faster than those of conventional techniques. The work was done in collaboration with a Pleasanton-based company, Integrated Dynamic Electron Solutions (IDES) Inc. Using this revolutionary imaging technique, a range of fundamental and technologically important material and biological processes can be captured in action, in complete billionth-of-a-meter detail, for the first time. The primary application of MM-DTEM is the direct observation of fast processes, including microstructural changes, phase transformations and chemical reactions, that shape real-world performance of nanostructured materials and potentially biological entities. The instrument could prove especially valuable in the direct observation of macromolecular interactions, such as protein-protein binding and host-pathogen interactions. While an earlier version of the technology, Single Shot-DTEM, could capture a single snapshot of a rapid process, MM-DTEM captures a multiframe movie that reveals complex sequences of events in detail. It is the only existing technology that can capture multiple electron microscopy images in the span of a single microsecond.

2013 R&D 100 Award: Movie-mode electron microscope captures nanoscale

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

Lagrange, Thomas; Reed, Bryan

2014-04-03

A new instrument developed by LLNL scientists and engineers, the Movie Mode Dynamic Transmission Electron Microscope (MM-DTEM), captures billionth-of-a-meter-scale images with frame rates more than 100,000 times faster than those of conventional techniques. The work was done in collaboration with a Pleasanton-based company, Integrated Dynamic Electron Solutions (IDES) Inc. Using this revolutionary imaging technique, a range of fundamental and technologically important material and biological processes can be captured in action, in complete billionth-of-a-meter detail, for the first time. The primary application of MM-DTEM is the direct observation of fast processes, including microstructural changes, phase transformations and chemical reactions, that shapemore » real-world performance of nanostructured materials and potentially biological entities. The instrument could prove especially valuable in the direct observation of macromolecular interactions, such as protein-protein binding and host-pathogen interactions. While an earlier version of the technology, Single Shot-DTEM, could capture a single snapshot of a rapid process, MM-DTEM captures a multiframe movie that reveals complex sequences of events in detail. It is the only existing technology that can capture multiple electron microscopy images in the span of a single microsecond.« less

Dynamic photosynthesis in different environmental conditions.

PubMed

Kaiser, Elias; Morales, Alejandro; Harbinson, Jeremy; Kromdijk, Johannes; Heuvelink, Ep; Marcelis, Leo F M

2015-05-01

Incident irradiance on plant leaves often fluctuates, causing dynamic photosynthesis. Whereas steady-state photosynthetic responses to environmental factors have been extensively studied, knowledge of dynamic modulation of photosynthesis remains scarce and scattered. This review addresses this discrepancy by summarizing available data and identifying the research questions necessary to advance our understanding of interactions between environmental factors and dynamic behaviour of photosynthesis using a mechanistic framework. Firstly, dynamic photosynthesis is separated into sub-processes related to proton and electron transport, non-photochemical quenching, control of metabolite flux through the Calvin cycle (activation states of Rubisco and RuBP regeneration, and post-illumination metabolite turnover), and control of CO₂ supply to Rubisco (stomatal and mesophyll conductance changes). Secondly, the modulation of dynamic photosynthesis and its sub-processes by environmental factors is described. Increases in ambient CO₂ concentration and temperature (up to ~35°C) enhance rates of photosynthetic induction and decrease its loss, facilitating more efficient dynamic photosynthesis. Depending on the sensitivity of stomatal conductance, dynamic photosynthesis may additionally be modulated by air humidity. Major knowledge gaps exist regarding environmental modulation of loss of photosynthetic induction, dynamic changes in mesophyll conductance, and the extent of limitations imposed by stomatal conductance for different species and environmental conditions. The study of mutants or genetic transformants for specific processes under various environmental conditions could provide significant progress in understanding the control of dynamic photosynthesis. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics

PubMed Central

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements. PMID:28727850

Proteomics in chromatin biology and epigenetics: Elucidation of post-translational modifications of histone proteins by mass spectrometry.

PubMed

Sidoli, Simone; Cheng, Lei; Jensen, Ole N

2012-06-27

Histone proteins contribute to the maintenance and regulation of the dynamic chromatin structure, to gene activation, DNA repair and many other processes in the cell nucleus. Site-specific reversible and irreversible post-translational modifications of histone proteins mediate biological functions, including recruitment of transcription factors to specific DNA regions, assembly of epigenetic reader/writer/eraser complexes onto DNA, and modulation of DNA-protein interactions. Histones thereby regulate chromatin structure and function, propagate inheritance and provide memory functions in the cell. Dysfunctional chromatin structures and misregulation may lead to pathogenic states, including diabetes and cancer, and the mapping and quantification of multivalent post-translational modifications has therefore attracted significant interest. Mass spectrometry has quickly been accepted as a versatile tool to achieve insights into chromatin biology and epigenetics. High sensitivity and high mass accuracy and the ability to sequence post-translationally modified peptides and perform large-scale analyses make this technique very well suited for histone protein characterization. In this review we discuss a range of analytical methods and various mass spectrometry-based approaches for histone analysis, from sample preparation to data interpretation. Mass spectrometry-based proteomics is already an integrated and indispensable tool in modern chromatin biology, providing insights into the mechanisms and dynamics of nuclear and epigenetic processes. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry. Copyright © 2011 Elsevier B.V. All rights reserved.

Evolving Relevance of Neuroproteomics in Alzheimer's Disease.

PubMed

Lista, Simone; Zetterberg, Henrik; O'Bryant, Sid E; Blennow, Kaj; Hampel, Harald

2017-01-01

Substantial progress in the understanding of the biology of Alzheimer's disease (AD) has been achieved over the past decades. The early detection and diagnosis of AD and other age-related neurodegenerative diseases, however, remain a challenging scientific frontier. Therefore, the comprehensive discovery (relating to all individual, converging or diverging biochemical disease mechanisms), development, validation, and qualification of standardized biological markers with diagnostic and prognostic functions with a precise performance profile regarding specificity, sensitivity, and positive and negative predictive value are warranted.Methodological innovations in the area of exploratory high-throughput technologies, such as sequencing, microarrays, and mass spectrometry-based analyses of proteins/peptides, have led to the generation of large global molecular datasets from a multiplicity of biological systems, such as biological fluids, cells, tissues, and organs. Such methodological progress has shifted the attention to the execution of hypothesis-independent comprehensive exploratory analyses (opposed to the classical hypothesis-driven candidate approach), with the aim of fully understanding the biological systems in physiology and disease as a whole. The systems biology paradigm integrates experimental biology with accurate and rigorous computational modelling to describe and foresee the dynamic features of biological systems. The use of dynamically evolving technological platforms, including mass spectrometry, in the area of proteomics has enabled to rush the process of biomarker discovery and validation for refining significantly the diagnosis of AD. Currently, proteomics-which is part of the systems biology paradigm-is designated as one of the dominant matured sciences needed for the effective exploratory discovery of prospective biomarker candidates expected to play an effective role in aiding the early detection, diagnosis, prognosis, and therapy development in AD.

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

The Emergence of Temporal Structures in Dynamical Systems

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

2010-10-01

Dynamical systems in classical, relativistic and quantum physics are ruled by laws with time reversibility. Complex dynamical systems with time-irreversibility are known from thermodynamics, biological evolution, growth of organisms, brain research, aging of people, and historical processes in social sciences. Complex systems are systems that compromise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous emergence of distinctive temporal, spatial or functional structures. But, emergence is no mystery. In a general meaning, the emergence of macroscopic features results from the nonlinear interactions of the elements in a complex system. Mathematically, the emergence of irreversible structures is modelled by phase transitions in non-equilibrium dynamics of complex systems. These methods have been modified even for chemical, biological, economic and societal applications (e.g., econophysics). Emergence of irreversible structures can also be simulated by computational systems. The question arises how the emergence of irreversible structures is compatible with the reversibility of fundamental physical laws. It is argued that, according to quantum cosmology, cosmic evolution leads from symmetry to complexity of irreversible structures by symmetry breaking and phase transitions. Thus, arrows of time and aging processes are not only subjective experiences or even contradictions to natural laws, but they can be explained by quantum cosmology and the nonlinear dynamics of complex systems. Human experiences and religious concepts of arrows of time are considered in a modern scientific framework. Platonic ideas of eternity are at least understandable with respect to mathematical invariance and symmetry of physical laws. Heraclit’s world of change and dynamics can be mapped onto our daily real-life experiences of arrows of time.

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.

Assessment of the sustainability of bushmeat hunting based on dynamic bioeconomic models.

PubMed

Ling, S; Milner-Gulland, E J

2006-08-01

Open-access hunting is a dynamic system in which individual hunters respond to changes in system variables such as costs of hunting and prices obtained for their catch. Sustainability indices used by conservationists ignore these human processes and focus only on the biological sustainability of current offtake levels. This focus implicitly assumes that offtake is constant, says little about the actual sustainability of the system, and fails to provide any basis for predicting the impact of most feasible management interventions. A bioeconomic approach overcomes these limitations by explicitly integrating both the biological and human components of the system. We present a graphical representation of a simple bioeconomic model of bushmeat hunting and use it to demonstrate the importance of considering system dynamics when assessing sustainability. Our results show that commonly used static sustainability indices are often misleading. The best method to assess hunting sustainability is situation dependent, but characterizing supply and demand curves, even crudely, has greater potential than current approaches to provide robust predictions in the medium term.

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.

Analytical considerations and dimensionless analysis for a description of particle interactions in high pressure processes

NASA Astrophysics Data System (ADS)

Rauh, Cornelia; Delgado, Antonio

2010-12-01

High pressures of up to several hundreds of MPa are utilized in a wide range of applications in chemical, bio-, and food engineering, aiming at selective control of (bio-)chemical reactions. Non-uniformity of process conditions may threaten the safety and quality of the resulting products because processing conditions such as pressure, temperature, and treatment history are crucial for the course of (bio-)chemical reactions. Therefore, thermofluid-dynamical phenomena during the high pressure process have to be examined, and numerical tools to predict process uniformity and to optimize the processes have to be developed. Recently applied mathematical models and numerical simulations of laboratory and industrial scale high pressure processes investigating the mentioned crucial phenomena are based on continuum balancing models of thermofluid dynamics. Nevertheless, biological systems are complex fluids containing the relevant (bio-)chemical compounds (enzymes and microorganisms). These compounds are particles that interact with the surrounding medium and between each other. This contribution deals with thermofluid-dynamical interactions of the relevant particulate (bio-)chemical compounds (enzymes and microorganisms) with the surrounding fluid. By consideration of characteristic time and length scales and particle forces, the motion of the (bio-)chemical compounds is characterized.

Subtropical Gyre Variability Observed by Ocean Color Satellites

NASA Technical Reports Server (NTRS)

McClain, Charles R.; Signorini, Sergio R.; Christian, James R.

2002-01-01

The subtropical gyres of the world are extensive, coherent regions that occupy about 40% of the surface of the earth. Once thought to be homogeneous and static habitats, there is increasing evidence that mid-latitude gyres exhibit substantial physical and biological variability on a variety of time scales. While biological productivity within these oligotrophic regions may be relatively small, their immense size makes their total contribution significant. Global distributions of dynamic height derived from satellite altimeter data, and chlorophyll concentration derived from satellite ocean color data, show that the dynamic center of the gyres, the region of maximum dynamic height where the thermocline is deepest, does not coincide with the region of minimum chlorophyll concentration. The physical and biological processes by which this distribution of ocean properties is maintained, and the spatial and temporal scales of variability associated with these processes, are analyzed using global surface chlorophyll-a concentrations, sea surface height, sea surface temperature and surface winds from operational satellite and meteorological sources, and hydrographic data from climatologies and individual surveys. Seasonal and interannual variability in the areal extent of the subtropical gyres are examined using 8 months (November 1996 - June 1997) of OCTS and nearly 5 years (September 1997 - June 02) of SeaWiFS ocean color data and are interpreted in the context of climate variability and measured changes in other ocean properties (i.e., wind forcing, surface currents, Ekman pumping, and vertical mixing). The North Pacific and North Atlantic gyres are observed to be shrinking over this period, while the South Pacific, South Atlantic, and South Indian Ocean gyres appear to be expanding.

Mathematical modeling in biological populations through branching processes. Application to salmonid populations.

PubMed

Molina, Manuel; Mota, Manuel; Ramos, Alfonso

2015-01-01

This work deals with mathematical modeling through branching processes. We consider sexually reproducing animal populations where, in each generation, the number of progenitor couples is determined in a non-predictable environment. By using a class of two-sex branching processes, we describe their demographic dynamics and provide several probabilistic and inferential contributions. They include results about the extinction of the population and the estimation of the offspring distribution and its main moments. We also present an application to salmonid populations.

Implementing vertex dynamics models of cell populations in biology within a consistent computational framework.

PubMed

Fletcher, Alexander G; Osborne, James M; Maini, Philip K; Gavaghan, David J

2013-11-01

The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell-cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable. Copyright © 2013 Elsevier Ltd. All rights reserved.

Flexibility-Rigidity Coordination of the Dense Exopolysaccharide Matrix in Terrestrial Cyanobacteria Acclimated to Periodic Desiccation.

PubMed

Liu, Wen; Cui, Lijuan; Xu, Haiyan; Zhu, Zhaoxia; Gao, Xiang

2017-11-15

A dense exopolysaccharide (EPS) matrix is crucial for cyanobacterial survival in terrestrial xeric environments, in which cyanobacteria undergo frequent expansion and shrinkage processes during environmental desiccation-rehydration cycles. However, it is unclear how terrestrial cyanobacteria coordinate the structural dynamics of the EPS matrix upon expansion and shrinkage to avoid potential mechanical stress while benefiting from the matrix. In the present study, we sought to answer this question by investigating the gene expression, protein dynamics, enzymatic characteristics, and biological roles of WspA, an abundantly secreted protein, in the representative terrestrial cyanobacterium Nostoc flagelliforme The results demonstrated that WspA is a novel β-galactosidase that facilitates softening of the EPS matrix by breaking the polysaccharide backbone under substantial moisture or facilitates the thickening and relinkage of the broken matrix during the drying process, and thus these regulations are well correlated with moisture availability or desiccation-rehydration cycles. This coordination of flexibility and rigidity of the cyanobacterial extracellular matrix may contribute to a favorable balance of cell growth and stress resistance in xeric environments. IMPORTANCE How the exopolysaccharide matrix is dynamically coordinated by exoproteins to cope with frequent expansion and shrinkage processes in terrestrial colonial cyanobacteria remains unclear. Here we elucidated the biochemical identity and biological roles of a dominant exoprotein in these regulation processes. Our study thus gained insight into this regulative mechanism in cyanobacteria to combat periodic desiccation. In addition, the filamentous drought-adapted cyanobacterium Nostoc flagelliforme serves as an ideal model for us to explore this issue in this study. Copyright © 2017 American Society for Microbiology.

The Stochastic Evolutionary Game for a Population of Biological Networks Under Natural Selection

PubMed Central

Chen, Bor-Sen; Ho, Shih-Ju

2014-01-01

In this study, a population of evolutionary biological networks is described by a stochastic dynamic system with intrinsic random parameter fluctuations due to genetic variations and external disturbances caused by environmental changes in the evolutionary process. Since information on environmental changes is unavailable and their occurrence is unpredictable, they can be considered as a game player with the potential to destroy phenotypic stability. The biological network needs to develop an evolutionary strategy to improve phenotypic stability as much as possible, so it can be considered as another game player in the evolutionary process, ie, a stochastic Nash game of minimizing the maximum network evolution level caused by the worst environmental disturbances. Based on the nonlinear stochastic evolutionary game strategy, we find that some genetic variations can be used in natural selection to construct negative feedback loops, efficiently improving network robustness. This provides larger genetic robustness as a buffer against neutral genetic variations, as well as larger environmental robustness to resist environmental disturbances and maintain a network phenotypic traits in the evolutionary process. In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution. However, if the harbored neutral genetic variations are accumulated to a sufficiently large degree, and environmental disturbances are strong enough that the network robustness can no longer confer enough genetic robustness and environmental robustness, then the phenotype robustness might break down. In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution. Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics. PMID:24558296

Bioattractors: dynamical systems theory and the evolution of regulatory processes.

PubMed

Jaeger, Johannes; Monk, Nick

2014-06-01

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. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

PubMed Central

Brinkworth, Russell S. A.; O'Carroll, David C.

2009-01-01

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors. PMID:19893631

Numerical simulation of physicochemical interactions between oxygen atom and phosphatidylcholine due to direct irradiation of atmospheric pressure nonequilibrium plasma to biological membrane with quantum mechanical molecular dynamics

NASA Astrophysics Data System (ADS)

Uchida, Satoshi; Yoshida, Taketo; Tochikubo, Fumiyoshi

2017-10-01

Plasma medicine is one of the most attractive applications using atmospheric pressure nonequilibrium plasma. With respect to direct contact of the discharge plasma with a biological membrane, reactive oxygen species play an important role in induction of medical effects. However, complicated interactions between the plasma radicals and membrane have not been understood well. In the present work, we simulated elemental processes at the first stage of physicochemical interactions between oxygen atom and phosphatidylcholine using the quantum mechanical molecular dynamics code in a general software AMBER. The change in the above processes was classified according to the incident energy of oxygen atom. At an energy of 1 eV, the abstraction of a hydrogen atom and recombination to phosphatidylcholine were simultaneously occurred in chemical attachment of incident oxygen atom. The exothermal energy of the reaction was about 80% of estimated one based on the bond energies of ethane. An oxygen atom over 10 eV separated phosphatidylcholine partially. The behaviour became increasingly similar to physical sputtering. The reaction probability of oxygen atom was remarkably high in comparison with that of hydrogen peroxide. These results suggest that we can uniformly estimate various physicochemical dynamics of reactive oxygen species against membrane lipids.

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.

Microbial ecology of denitrification in biological wastewater treatment.

PubMed

Lu, Huijie; Chandran, Kartik; Stensel, David

2014-11-01

Globally, denitrification is commonly employed in biological nitrogen removal processes to enhance water quality. However, substantial knowledge gaps remain concerning the overall community structure, population dynamics and metabolism of different organic carbon sources. This systematic review provides a summary of current findings pertaining to the microbial ecology of denitrification in biological wastewater treatment processes. DNA fingerprinting-based analysis has revealed a high level of microbial diversity in denitrification reactors and highlighted the impacts of carbon sources in determining overall denitrifying community composition. Stable isotope probing, fluorescence in situ hybridization, microarrays and meta-omics further link community structure with function by identifying the functional populations and their gene regulatory patterns at the transcriptional and translational levels. This review stresses the need to integrate microbial ecology information into conventional denitrification design and operation at full-scale. Some emerging questions, from physiological mechanisms to practical solutions, for example, eliminating nitrous oxide emissions and supplementing more sustainable carbon sources than methanol, are also discussed. A combination of high-throughput approaches is next in line for thorough assessment of wastewater denitrifying community structure and function. Though denitrification is used as an example here, this synergy between microbial ecology and process engineering is applicable to other biological wastewater treatment processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Potential in vivo roles of nucleic acid triple-helices

PubMed Central

Buske, Fabian A

2011-01-01

The ability of double-stranded DNA to form a triple-helical structure by hydrogen bonding with a third strand is well established, but the biological functions of these structures remain largely unknown. There is considerable albeit circumstantial evidence for the existence of nucleic triplexes in vivo and their potential participation in a variety of biological processes including chromatin organization, DNA repair, transcriptional regulation and RNA processing has been investigated in a number of studies to date. There is also a range of possible mechanisms to regulate triplex formation through differential expression of triplex-forming RNAs, alteration of chromatin accessibility, sequence unwinding and nucleotide modifications. With the advent of next generation sequencing technology combined with targeted approaches to isolate triplexes, it is now possible to survey triplex formation with respect to their genomic context, abundance and dynamical changes during differentiation and development, which may open up new vistas in understanding genome biology and gene regulation. PMID:21525785

Fluorescence correlation spectroscopy: novel variations of an established technique.

PubMed

Haustein, Elke; Schwille, Petra

2007-01-01

Fluorescence correlation spectroscopy (FCS) is one of the major biophysical techniques used for unraveling molecular interactions in vitro and in vivo. It allows minimally invasive study of dynamic processes in biological specimens with extremely high temporal and spatial resolution. By recording and correlating the fluorescence fluctuations of single labeled molecules through the exciting laser beam, FCS gives information on molecular mobility and photophysical and photochemical reactions. By using dual-color fluorescence cross-correlation, highly specific binding studies can be performed. These have been extended to four reaction partners accessible by multicolor applications. Alternative detection schemes shift accessible time frames to slower processes (e.g., scanning FCS) or higher concentrations (e.g., TIR-FCS). Despite its long tradition, FCS is by no means dated. Rather, it has proven to be a highly versatile technique that can easily be adapted to solve specific biological questions, and it continues to find exciting applications in biology and medicine.

Transport of Proteins through Nanopores

NASA Astrophysics Data System (ADS)

Luan, Binquan

In biological cells, a malfunctioned protein (such as misfolded or damaged) is degraded by a protease in which an unfoldase actively drags the protein into a nanopore-like structure and then a peptidase cuts the linearized protein into small fragments (i.e. a recycling process). Mimicking this biological process, many experimental studies have focused on the transport of proteins through a biological protein pore or a synthetic solid-state nanopore. Potentially, the nanopore-based sensors can provide a platform for interrogating proteins that might be disease-related or be targeted by a new drug molecule. The single-profile of a protein chain inside an extremely small nanopore might even permit the sequencing of the protein. Here, through all-atom molecular dynamics simulations, I will show various types of protein transport through a nanopore and reveal the nanoscale mechanics/energetics that plays an important role governing the protein transport.

Pushing the lipid envelope: using bio-inspired nanocomposites to understand and exploit lipid membrane limitations

NASA Astrophysics Data System (ADS)

Montano, Gabriel

Lipids serve as the organizing matrix material for biological membranes, the site of interaction of cells with the external environment. . As such, lipids play a critical role in structure/function relationships of an extraordinary number of critical biological processes. In this talk, we will look at bio-inspired membrane assemblies to better understand the roles of lipids in biological systems as well as attempt to generate materials that can mimic and potentially advance upon biological membrane processes. First, we will investigate the response of lipids to adverse conditions. In particular, I will present data that demonstrates the response of lipids to harsh conditions and how such responses can be exploited to generate nanocomposite rearrangements. I will also show the effect of adding the endotoxin lipopolysaccharide (LPS) to lipid bilayer assemblies and describe implications on our understanding of LPS organization in biological systems as well as describe induced lipid modifications that can be exploited to organize membrane composites with precise, two-dimensional geometric control. Lastly, I will describe the use of amphiphilic block copolymers to create membrane nanocomposites capable of mimicking biological systems. In particular, I will describe the use of our polymer-based membranes in creating artificial photosynthetic assemblies that rival biological systems in function in a more flexible, dynamic matrix.

Formalizing Knowledge in Multi-Scale Agent-Based Simulations

PubMed Central

Somogyi, Endre; Sluka, James P.; Glazier, James A.

2017-01-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused. PMID:29338063

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

PubMed

Somogyi, Endre; Sluka, James P; Glazier, James A

2016-10-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.

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.

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

PubMed Central

Pearce, Timothy C.; Karout, Salah; Rácz, Zoltán; Capurro, Alberto; Gardner, Julian W.; Cole, Marina

2012-01-01

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales. PMID:23874265

Processes on the emergent landscapes of biochemical reaction networks and heterogeneous cell population dynamics: differentiation in living matters

PubMed Central

Li, Fangting

2017-01-01

The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical compositions x = (x1,x2, … ,xN) in a cell, where xℓ is the concentration of the ℓth biochemical species. Equivalently, for heterogeneous cell population dynamics xℓ is the number density of cells of the ℓth cell type. One of the insights derived from the landscape perspective is that the life history of an individual organism, which occurs on the hillsides of a landscape, is nearly deterministic and ‘programmed’, while population-wise an asynchronous non-equilibrium steady state resides mostly in the lowlands of the landscape. We argue that a dynamic ‘blue-sky’ bifurcation, as a representation of Waddington's landscape, is a more robust mechanism for a cell fate decision and subsequent differentiation than the widely pictured pitch-fork bifurcation. We revisit, in terms of the chemostatic driving forces upon active, living matter, the notions of near-equilibrium thermodynamic branches versus far-from-equilibrium states. The emergent landscape perspective permits a quantitative discussion of a wide range of biological phenomena as nonlinear, stochastic dynamics. PMID:28490602

Method for identifying biochemical and chemical reactions and micromechanical processes using nanomechanical and electronic signal identification

DOEpatents

Holzrichter, J.F.; Siekhaus, W.J.

1997-04-15

A scanning probe microscope, such as an atomic force microscope (AFM) or a scanning tunneling microscope (STM), is operated in a stationary mode on a site where an activity of interest occurs to measure and identify characteristic time-varying micromotions caused by biological, chemical, mechanical, electrical, optical, or physical processes. The tip and cantilever assembly of an AFM is used as a micromechanical detector of characteristic micromotions transmitted either directly by a site of interest or indirectly through the surrounding medium. Alternatively, the exponential dependence of the tunneling current on the size of the gap in the STM is used to detect micromechanical movement. The stationary mode of operation can be used to observe dynamic biological processes in real time and in a natural environment, such as polymerase processing of DNA for determining the sequence of a DNA molecule. 6 figs.

Method for identifying biochemical and chemical reactions and micromechanical processes using nanomechanical and electronic signal identification

DOEpatents

Holzrichter, John F.; Siekhaus, Wigbert J.

1997-01-01

A scanning probe microscope, such as an atomic force microscope (AFM) or a scanning tunneling microscope (STM), is operated in a stationary mode on a site where an activity of interest occurs to measure and identify characteristic time-varying micromotions caused by biological, chemical, mechanical, electrical, optical, or physical processes. The tip and cantilever assembly of an AFM is used as a micromechanical detector of characteristic micromotions transmitted either directly by a site of interest or indirectly through the surrounding medium. Alternatively, the exponential dependence of the tunneling current on the size of the gap in the STM is used to detect micromechanical movement. The stationary mode of operation can be used to observe dynamic biological processes in real time and in a natural environment, such as polymerase processing of DNA for determining the sequence of a DNA molecule.

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

PREFACE: Cooperative dynamics Cooperative dynamics

NASA Astrophysics Data System (ADS)

Gov, Nir

2011-09-01

The dynamics within living cells are dominated by non-equilibrium processes that consume chemical energy (usually in the form of ATP, adenosine triphosphate) and convert it into mechanical forces and motion. The mechanisms that allow this conversion process are mostly driven by the components of the cytoskeleton: (i) directed (polar) polymerization of filaments (either actin or microtubules) and (ii) molecular motors. The forces and motions produced by these two components of the cytoskeleton give rise to the formation of cellular shapes, and drive the intracellular transport and organization. It is clear that these systems present a multi-scale challenge, from the physics of the molecular processes to the organization of many interacting units. Understanding the physical nature of these systems will have a large impact on many fundamental problems in biology and break new grounds in the field of non-equilibrium physics. This field of research has seen a rapid development over the last ten years. Activities in this area range from theoretical and experimental work on the underlying fundamental (bio)physics at the single-molecule level, to investigations (in vivo and in vitro) of the dynamics and patterns of macroscopic pieces of 'living matter'. In this special issue we have gathered contributions that span the whole spectrum of length- and complexity-scales in this field. Some of the works demonstrate how active forces self-organize within the polymerizing cytoskeleton, on the level of cooperative cargo transport via motors or due to active fluxes at the cell membrane. On a larger scale, it is shown that polar filaments coupled to molecular motors give rise to a huge variety of surprising dynamics and patterns: spontaneously looping rings of gliding microtubules, and emergent phases of self-organized filaments and motors in different geometries. All of these articles share the common feature of being out-of-equilibrium, driven by metabolism. As demonstrated here, the biological problems that inspire these physical studies cover fundamental processes, from cell division up to the dynamics within axons and neurons. We would like to acknowledge and thank all contributors for their submissions, which made this special issue possible in the first place. Moreover, we would like to thank the staff at IOP Publishing for helping us with the administrative aspects and for co-ordinating the refereeing process. We hope that readers will enjoy this collection of papers and that it will trigger them to further explore the endless open physics questions presented by biological systems. Cooperative dynamics contents How does the antagonism between capping and anti-capping proteins affect actin network dynamics? Longhua Hu and Garegin A Papoian The emergence of sarcomeric, graded-polarity and spindle-like patterns in bundles of short cytoskeletal polymers and two opposite molecular motorsE M Craig, S Dey and A Mogilner Model of myosin node aggregation into a contractile ring: the effect of local alignmentNikola Ojkic, Jian-Qiu Wu and Dimitrios Vavylonis Loop formation of microtubules during gliding at high densityLynn Liu, Erkan Tüzel and Jennifer L Ross Protein-coat dynamics and cluster phases in intracellular traffickingGreg Huber, Hui Wang and Ranjan Mukhopadhyay Conformational changes, diffusion and collective behavior in monomeric kinesin-based motilityKerwyn Casey Huang, Christian Vega and Ajay Gopinathan One-dimensional deterministic transport in neurons measured by dispersion-relation phase spectroscopyRu Wang, Zhuo Wang, Joe Leigh, Nahil Sobh, Larry Millet, Martha U Gillette, Alex J Levine and Gabriel Popescu

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.

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

New physical concepts for cell amoeboid motion.

PubMed Central

Evans, E

1993-01-01

Amoeboid motion of cells is an essential mechanism in the function of many biological organisms (e.g., the regiment of scavenger cells in the immune defense system of animals). This process involves rapid chemical polymerization (with numerous protein constituents) to create a musclelike contractile network that advances the cell over the surface. Significant progress has been made in the biology and biochemistry of motile cells, but the physical dynamics of cell spreading and contraction are not well understood. The reason is that general approaches are formulated from complex mass, momentum, and chemical reaction equations for multiphase-multicomponent flow with the nontrivial difficulty of moving boundaries. However, there are strong clues to the dynamics that allow bold steps to be taken in simplifying the physics of motion. First, amoeboid cells often exhibit exceptional kinematics, i.e., steady advance and retraction of local fixed-shape patterns. Second, recent evidence has shown that cell projections "grow" by polymerization along the advancing boundary of the cell. Together, these characteristics represent a local growth process pinned to the interfacial contour of a contractile network. As such, the moving boundary becomes tractable, but subtle features of the motion lead to specific requirements for the chemical nature of the boundary polymerization process. To demonstrate these features, simple examples for limiting conditions of substrate interaction (i.e., "strong" and "weak" adhesion) are compared with data from experimental studies of yeast particle engulfment by blood granulocytes and actin network dynamics in fishscale keratocytes. Images FIGURE 2 FIGURE 4 PMID:8494986

New physical concepts for cell amoeboid motion.

PubMed

Evans, E

1993-04-01

Amoeboid motion of cells is an essential mechanism in the function of many biological organisms (e.g., the regiment of scavenger cells in the immune defense system of animals). This process involves rapid chemical polymerization (with numerous protein constituents) to create a musclelike contractile network that advances the cell over the surface. Significant progress has been made in the biology and biochemistry of motile cells, but the physical dynamics of cell spreading and contraction are not well understood. The reason is that general approaches are formulated from complex mass, momentum, and chemical reaction equations for multiphase-multicomponent flow with the nontrivial difficulty of moving boundaries. However, there are strong clues to the dynamics that allow bold steps to be taken in simplifying the physics of motion. First, amoeboid cells often exhibit exceptional kinematics, i.e., steady advance and retraction of local fixed-shape patterns. Second, recent evidence has shown that cell projections "grow" by polymerization along the advancing boundary of the cell. Together, these characteristics represent a local growth process pinned to the interfacial contour of a contractile network. As such, the moving boundary becomes tractable, but subtle features of the motion lead to specific requirements for the chemical nature of the boundary polymerization process. To demonstrate these features, simple examples for limiting conditions of substrate interaction (i.e., "strong" and "weak" adhesion) are compared with data from experimental studies of yeast particle engulfment by blood granulocytes and actin network dynamics in fishscale keratocytes.

A Probabilistic Framework for Constructing Temporal Relations in Replica Exchange Molecular Trajectories.

PubMed

Chattopadhyay, Aditya; Zheng, Min; Waller, Mark Paul; Priyakumar, U Deva

2018-05-23

Knowledge of the structure and dynamics of biomolecules is essential for elucidating the underlying mechanisms of biological processes. Given the stochastic nature of many biological processes, like protein unfolding, it's almost impossible that two independent simulations will generate the exact same sequence of events, which makes direct analysis of simulations difficult. Statistical models like Markov Chains, transition networks etc. help in shedding some light on the mechanistic nature of such processes by predicting long-time dynamics of these systems from short simulations. However, such methods fall short in analyzing trajectories with partial or no temporal information, for example, replica exchange molecular dynamics or Monte Carlo simulations. In this work we propose a probabilistic algorithm, borrowing concepts from graph theory and machine learning, to extract reactive pathways from molecular trajectories in the absence of temporal data. A suitable vector representation was chosen to represent each frame in the macromolecular trajectory (as a series of interaction and conformational energies) and dimensionality reduction was performed using principal component analysis (PCA). The trajectory was then clustered using a density-based clustering algorithm, where each cluster represents a metastable state on the potential energy surface (PES) of the biomolecule under study. A graph was created with these clusters as nodes with the edges learnt using an iterative expectation maximization algorithm. The most reactive path is conceived as the widest path along this graph. We have tested our method on RNA hairpin unfolding trajectory in aqueous urea solution. Our method makes the understanding of the mechanism of unfolding in RNA hairpin molecule more tractable. As this method doesn't rely on temporal data it can be used to analyze trajectories from Monte Carlo sampling techniques and replica exchange molecular dynamics (REMD).

Analysis of l-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli

PubMed Central

2013-01-01

Background Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. Results We constructed an l-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for l-glutamic acid production; the results of this process corresponded with previous experimental data regarding l-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of l-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model l-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in l-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. Conclusions In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation. PMID:24053676

Analysis of L-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli.

PubMed

Nishio, Yousuke; Ogishima, Soichi; Ichikawa, Masao; Yamada, Yohei; Usuda, Yoshihiro; Masuda, Tadashi; Tanaka, Hiroshi

2013-09-22

Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. We constructed an L-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for L-glutamic acid production; the results of this process corresponded with previous experimental data regarding L-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of L-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model L-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in L-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation.

High-resolution time series of Pseudomonas aeruginosa gene expression and rhamnolipid secretion through growth curve synchronization.

PubMed

van Ditmarsch, Dave; Xavier, João B

2011-06-17

Online spectrophotometric measurements allow monitoring dynamic biological processes with high-time resolution. Contrastingly, numerous other methods require laborious treatment of samples and can only be carried out offline. Integrating both types of measurement would allow analyzing biological processes more comprehensively. A typical example of this problem is acquiring quantitative data on rhamnolipid secretion by the opportunistic pathogen Pseudomonas aeruginosa. P. aeruginosa cell growth can be measured by optical density (OD600) and gene expression can be measured using reporter fusions with a fluorescent protein, allowing high time resolution monitoring. However, measuring the secreted rhamnolipid biosurfactants requires laborious sample processing, which makes this an offline measurement. Here, we propose a method to integrate growth curve data with endpoint measurements of secreted metabolites that is inspired by a model of exponential cell growth. If serial diluting an inoculum gives reproducible time series shifted in time, then time series of endpoint measurements can be reconstructed using calculated time shifts between dilutions. We illustrate the method using measured rhamnolipid secretion by P. aeruginosa as endpoint measurements and we integrate these measurements with high-resolution growth curves measured by OD600 and expression of rhamnolipid synthesis genes monitored using a reporter fusion. Two-fold serial dilution allowed integrating rhamnolipid measurements at a ~0.4 h-1 frequency with high-time resolved data measured at a 6 h-1 frequency. We show how this simple method can be used in combination with mutants lacking specific genes in the rhamnolipid synthesis or quorum sensing regulation to acquire rich dynamic data on P. aeruginosa virulence regulation. Additionally, the linear relation between the ratio of inocula and the time-shift between curves produces high-precision measurements of maximum specific growth rates, which were determined with a precision of ~5.4%. Growth curve synchronization allows integration of rich time-resolved data with endpoint measurements to produce time-resolved quantitative measurements. Such data can be valuable to unveil the dynamic regulation of virulence in P. aeruginosa. More generally, growth curve synchronization can be applied to many biological systems thus helping to overcome a key obstacle in dynamic regulation: the scarceness of quantitative time-resolved data.

The relative importance of physical and biological energy in landscape evolution

NASA Astrophysics Data System (ADS)

Turowski, J. M.; Schwanghart, W.

2017-12-01

Landscapes are formed by the interplay of uplift and geomorphic processes, including interacting and competing physical and biological processes. For example, roots re-inforce soil and thereby stabilize hillslopes and the canopy cover of the forest may mediate the impact of precipitation. Furthermore, plants and animals act as geomorphic agents, directly altering landscape response and dynamics by their actions: tree roots may crack rocks, thus changing subsurface water flows and exposing fresh material for denudation; fungi excrete acids that accelerate rates of chemical weathering, and burrowing animals displace soil and rocks while digging holes for shelter or in search of food. Energetically, landscapes can be viewed as open systems in which topography stores potential energy above a base level. Tectonic processes add energy to the system by uplift and mechanically altering rock properties. Especially in unvegetated regions, erosion and transport by wind can be an important geomorphic process. Advection of atmospheric moisture in high altitudes provides potential energy that is converted by water fluxes through catchments. At the same time, the conversion of solar energy through atmospheric and biological processes drives primary production of living organisms. If we accept that biota influence geomorphic processes, then what is their energetic contribution to landscape evolution relative to physical processes? Using two case studies, we demonstrate that all components of energy input are negligible apart from biological production, quantified by net primary productivity (NPP) and potential energy conversion by water that is placed high up in the landscape as rainfall and leaves it as runoff. Assuming that the former is representative for biological energy and the latter for physical energy, we propose that the ratio of these two values can be used as a proxy for the relative importance of biological and physical processes in landscape evolution. All necessary parameters needed to calculate the ratio (NPP, runoff, elevation) are available globally. We find that biological processes are more important in arid and semiarid regions. The wide-spread lack of water strongly limits the energy available for fluvial erosion, while biota are geomorphic engineers less sensitive to water shortage.

The Dynamic Process of Drug-GPCR Binding at Either Orthosteric or Allosteric Sites Evaluated by Metadynamics

PubMed Central

Schneider, Sebastian; Provasi, Davide; Filizola, Marta

2015-01-01

Major advances in G Protein-Coupled Receptor (GPCR) structural biology over the past few years have yielded a significant number of high-resolution crystal structures for several different receptor subtypes. This dramatic increase in GPCR structural information has underscored the use of automated docking algorithms for the discovery of novel ligands that can eventually be developed into improved therapeutics. However, these algorithms are often unable to discriminate between different, yet energetically similar, poses because of their relatively simple scoring functions. Here, we describe a metadynamics-based approach to study the dynamic process of ligand binding to/unbinding from GPCRs with a higher level of accuracy and yet satisfying efficiency. PMID:26260607

Modelling and enhanced molecular dynamics to steer structure-based drug discovery.

PubMed

Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

2014-05-01

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Differential dynamic microscopy to characterize Brownian motion and bacteria motility

NASA Astrophysics Data System (ADS)

Germain, David; Leocmach, Mathieu; Gibaud, Thomas

2016-03-01

We have developed a lab module for undergraduate students, which involves the process of quantifying the dynamics of a suspension of microscopic particles using Differential Dynamic Microscopy (DDM). DDM is a relatively new technique that constitutes an alternative method to more classical techniques such as dynamic light scattering (DLS) or video particle tracking (VPT). The technique consists of imaging a particle dispersion with a standard light microscope and a camera and analyzing the images using a digital Fourier transform to obtain the intermediate scattering function, an autocorrelation function that characterizes the dynamics of the dispersion. We first illustrate DDM in the textbook case of colloids under Brownian motion, where we measure the diffusion coefficient. Then we show that DDM is a pertinent tool to characterize biological systems such as motile bacteria.

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.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Using Movies to Analyse Gene Circuit Dynamics in Single Cells

PubMed Central

Locke, James CW; Elowitz, Michael B

2010-01-01

Preface Many bacterial systems rely on dynamic genetic circuits to control critical processes. A major goal of systems biology is to understand these behaviours in terms of individual genes and their interactions. However, traditional techniques based on population averages wash out critical dynamics that are either unsynchronized between cells or driven by fluctuations, or ‘noise,’ in cellular components. Recently, the combination of time-lapse microscopy, quantitative image analysis, and fluorescent protein reporters has enabled direct observation of multiple cellular components over time in individual cells. In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic circuit behaviour in diverse microbial systems. PMID:19369953

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

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

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

Pattern formation and collective effects in populations of magnetic microswimmers

NASA Astrophysics Data System (ADS)

Vach, Peter J.; Walker, Debora; Fischer, Peer; Fratzl, Peter; Faivre, Damien

2017-03-01

Self-propelled particles are one prototype of synthetic active matter used to understand complex biological processes, such as the coordination of movement in bacterial colonies or schools of fishes. Collective patterns such as clusters were observed for such systems, reproducing features of biological organization. However, one limitation of this model is that the synthetic assemblies are made of identical individuals. Here we introduce an active system based on magnetic particles at colloidal scales. We use identical but also randomly-shaped magnetic micropropellers and show that they exhibit dynamic and reversible pattern formation.

Complex Processes from Dynamical Architectures with Time-Scale Hierarchy

PubMed Central

Perdikis, Dionysios; Huys, Raoul; Jirsa, Viktor

2011-01-01

The idea that complex motor, perceptual, and cognitive behaviors are composed of smaller units, which are somehow brought into a meaningful relation, permeates the biological and life sciences. However, no principled framework defining the constituent elementary processes has been developed to this date. Consequently, functional configurations (or architectures) relating elementary processes and external influences are mostly piecemeal formulations suitable to particular instances only. Here, we develop a general dynamical framework for distinct functional architectures characterized by the time-scale separation of their constituents and evaluate their efficiency. Thereto, we build on the (phase) flow of a system, which prescribes the temporal evolution of its state variables. The phase flow topology allows for the unambiguous classification of qualitatively distinct processes, which we consider to represent the functional units or modes within the dynamical architecture. Using the example of a composite movement we illustrate how different architectures can be characterized by their degree of time scale separation between the internal elements of the architecture (i.e. the functional modes) and external interventions. We reveal a tradeoff of the interactions between internal and external influences, which offers a theoretical justification for the efficient composition of complex processes out of non-trivial elementary processes or functional modes. PMID:21347363

Dynamic and rheological properties of soft biological cell suspensions

PubMed Central

Yazdani, Alireza; Li, Xuejin

2016-01-01

Quantifying dynamic and rheological properties of suspensions of soft biological particles such as vesicles, capsules, and red blood cells (RBCs) is fundamentally important in computational biology and biomedical engineering. In this review, recent studies on dynamic and rheological behavior of soft biological cell suspensions by computer simulations are presented, considering both unbounded and confined shear flow. Furthermore, the hemodynamic and hemorheological characteristics of RBCs in diseases such as malaria and sickle cell anemia are highlighted. PMID:27540271

A visual metaphor describing neural dynamics in schizophrenia.

PubMed

van Beveren, Nico J M; de Haan, Lieuwe

2008-07-09

In many scientific disciplines the use of a metaphor as an heuristic aid is not uncommon. A well known example in somatic medicine is the 'defense army metaphor' used to characterize the immune system. In fact, probably a large part of the everyday work of doctors consists of 'translating' scientific and clinical information (i.e. causes of disease, percentage of success versus risk of side-effects) into information tailored to the needs and capacities of the individual patient. The ability to do so in an effective way is at least partly what makes a clinician a good communicator. Schizophrenia is a severe psychiatric disorder which affects approximately 1% of the population. Over the last two decades a large amount of molecular-biological, imaging and genetic data have been accumulated regarding the biological underpinnings of schizophrenia. However, it remains difficult to understand how the characteristic symptoms of schizophrenia such as hallucinations and delusions are related to disturbances on the molecular-biological level. In general, psychiatry seems to lack a conceptual framework with sufficient explanatory power to link the mental- and molecular-biological domains. Here, we present an essay-like study in which we propose to use visualized concepts stemming from the theory on dynamical complex systems as a 'visual metaphor' to bridge the mental- and molecular-biological domains in schizophrenia. We first describe a computer model of neural information processing; we show how the information processing in this model can be visualized, using concepts from the theory on complex systems. We then describe two computer models which have been used to investigate the primary theory on schizophrenia, the neurodevelopmental model, and show how disturbed information processing in these two computer models can be presented in terms of the visual metaphor previously described. Finally, we describe the effects of dopamine neuromodulation, of which disturbances have been frequently described in schizophrenia, in terms of the same visualized metaphor. The conceptual framework and metaphor described offers a heuristic tool to understand the relationship between the mental- and molecular-biological domains in an intuitive way. The concepts we present may serve to facilitate communication between researchers, clinicians and patients.

An emerging synthesis between community ecology and evolutionary biology.

PubMed

Johnson, Marc T J; Stinchcombe, John R

2007-05-01

A synthesis between community ecology and evolutionary biology is emerging that identifies how genetic variation and evolution within one species can shape the ecological properties of entire communities and, in turn, how community context can govern evolutionary processes and patterns. This synthesis incorporates research on the ecology and evolution within communities over short timescales (community genetics and diffuse coevolution), as well as macroevolutionary timescales (community phylogenetics and co-diversification of communities). As we discuss here, preliminary evidence supports the hypothesis that there is a dynamic interplay between ecology and evolution within communities, yet researchers have not yet demonstrated convincingly whether, and under what circumstances, it is important for biologists to bridge community ecology and evolutionary biology. Answering this question will have important implications for both basic and applied problems in biology.

Portable low-coherence interferometry for quantitatively imaging fast dynamics with extended field of view

NASA Astrophysics Data System (ADS)

Shaked, Natan T.; Girshovitz, Pinhas; Frenklach, Irena

2014-06-01

We present our recent advances in the development of compact, highly portable and inexpensive wide-field interferometric modules. By a smart design of the interferometric system, including the usage of low-coherence illumination sources and common-path off-axis geometry of the interferometers, spatial and temporal noise levels of the resulting quantitative thickness profile can be sub-nanometric, while processing the phase profile in real time. In addition, due to novel experimentally-implemented multiplexing methods, we can capture low-coherence off-axis interferograms with significantly extended field of view and in faster acquisition rates. Using these techniques, we quantitatively imaged rapid dynamics of live biological cells including sperm cells and unicellular microorganisms. Then, we demonstrated dynamic profiling during lithography processes of microscopic elements, with thicknesses that may vary from several nanometers to hundreds of microns. Finally, we present new algorithms for fast reconstruction (including digital phase unwrapping) of off-axis interferograms, which allow real-time processing in more than video rate on regular single-core computers.

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

Treesearch

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

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...

PHYSICAL AND BIOLOGICAL CONTROLS ON DISSOLVED OXYGEN DYNAMICS IN A NORTHEASTERN GULF OF MEXICO ESTUARY

EPA Science Inventory

Nutrient over-enrichment is one of the most often cited causes of 305b impairment in coastal waters. Excessive nutrients affect designated uses of the nation's aquatic resources, and pose risks to human health and the environment. The process of developing nutrient criteria for e...

Simulations of molecular self-assembled monolayers on surfaces: packing structures, formation processes and functions tuned by intermolecular and interfacial interactions.

PubMed

Wen, Jin; Li, Wei; Chen, Shuang; Ma, Jing

2016-08-17

Surfaces modified with a functional molecular monolayer are essential for the fabrication of nano-scale electronics or machines with novel physical, chemical, and/or biological properties. Theoretical simulation based on advanced quantum chemical and classical models is at present a necessary tool in the development, design, and understanding of the interfacial nanostructure. The nanoscale surface morphology, growth processes, and functions are controlled by not only the electronic structures (molecular energy levels, dipole moments, polarizabilities, and optical properties) of building units but also the subtle balance between intermolecular and interfacial interactions. The switchable surfaces are also constructed by introducing stimuli-responsive units like azobenzene derivatives. To bridge the gap between experiments and theoretical models, opportunities and challenges for future development of modelling of ferroelectricity, entropy, and chemical reactions of surface-supported monolayers are also addressed. Theoretical simulations will allow us to obtain important and detailed information about the structure and dynamics of monolayer modified interfaces, which will guide the rational design and optimization of dynamic interfaces to meet challenges of controlling optical, electrical, and biological functions.

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.

Aging of clean foams

NASA Astrophysics Data System (ADS)

Weon, Byung Mook; Stewart, Peter S.

2014-11-01

Aging is an inevitable process in living systems. Here we show how clean foams age with time through sequential coalescence events: in particular, foam aging resembles biological aging. We measure population dynamics of bubbles in clean foams through numerical simulations with a bubble network model. We demonstrate that death rates of individual bubbles increase exponentially with time, independent on initial conditions, which is consistent with the Gompertz mortality law as usually found in biological aging. This consistency suggests that clean foams as far-from-equilibrium dissipative systems are useful to explore biological aging. This work (NRF-2013R1A22A04008115) was supported by Mid-career Researcher Program through NRF grant funded by the MEST.

Modern methods and systems for precise control of the quality of agricultural and food production

NASA Astrophysics Data System (ADS)

Bednarjevsky, Sergey S.; Veryasov, Yuri V.; Akinina, Evgeniya V.; Smirnov, Gennady I.

1999-01-01

The results on the modeling of non-linear dynamics of strong continuous and impulse radiation in the laser nephelometry of polydisperse biological systems, important from the viewpoint of applications in biotechnologies, are presented. The processes of nonlinear self-action of the laser radiation by the multiple scattering in the disperse biological agro-media are considered. The simplified algorithms of the calculation of the parameters of the biological media under investigation are indicated and the estimates of the errors of the laser-nephelometric measurements are given. The universal high-informative optical analyzers and the standard etalon specimens of agro- objects make the technological foundation of the considered methods and systems.

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

USSR Space Life Sciences Digest, issue 1

NASA Technical Reports Server (NTRS)

Hooke, L. R.; Radtke, M.; Rowe, J. E.

1985-01-01

The first issue of the bimonthly digest of USSR Space Life Sciences is presented. Abstracts are included for 49 Soviet periodical articles in 19 areas of aerospace medicine and space biology, published in Russian during the first quarter of 1985. Translated introductions and table of contents for nine Russian books on topics related to NASA's life science concerns are presented. Areas covered include: botany, cardiovascular and respiratory systems, cybernetics and biomedical data processing, endocrinology, gastrointestinal system, genetics, group dynamics, habitability and environmental effects, health and medicine, hematology, immunology, life support systems, man machine systems, metabolism, musculoskeletal system, neurophysiology, perception, personnel selection, psychology, radiobiology, reproductive system, and space biology. This issue concentrates on aerospace medicine and space biology.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

PubMed Central

Ferguson, Jake M; Ponciano, José M

2014-01-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. PMID:24304946

Programmable assembly of pressure sensors using pattern-forming bacteria

PubMed Central

Cao, Yangxiaolu; Feng, Yaying; Ryser, Marc D.; Zhu, Kui; Herschlag, Gregory; Cao, Changyong; Marusak, Katherine; Zauscher, Stefan; You, Lingchong

2017-01-01

Biological systems can generate microstructured materials that combine organic and inorganic components and possess diverse physical and chemical properties. However, these natural processes in materials fabrication are not readily programmable. Here, we use a synthetic-biology approach to mimic such natural processes to assemble patterned materials.. We demonstrate programmable fabrication of three-dimensional (3D) materials by printing engineered self-patterning bacteria on permeable membranes that serve as a structural scaffold. Application of gold nanoparticles to the colonies creates hybrid organic-inorganic dome structures. The dynamics of the dome structures' response to pressure is determined by their geometry (colony size, dome height and pattern), which is easily modified by varying the properties of the membrane (e.g., pore size and hydrophobicity). We generate resettable pressure sensors that process signals in response to varying pressure intensity and duration. PMID:28991268

Discrimination of dynamical system models for biological and chemical processes.

PubMed

Lorenz, Sönke; Diederichs, Elmar; Telgmann, Regina; Schütte, Christof

2007-06-01

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics.

Mitochondrial Dynamics in Diabetes

PubMed Central

Galloway, Chad A.; Jhun, Bong Sook; Yu, Tianzheng

2011-01-01

Abstract Mitochondria are at the center of cellular energy metabolism and regulate cell life and death. The cell biological aspect of mitochondria, especially mitochondrial dynamics, has drawn much attention through implications in human pathology, including neurological disorders and metabolic diseases. Mitochondrial fission and fusion are the main processes governing the morphological plasticity and are controlled by multiple factors, including mechanochemical enzymes and accessory proteins. Emerging evidence suggests that mitochondrial dynamics plays an important role in metabolism–secretion coupling in pancreatic β-cells as well as complications of diabetes. This review describes an overview of mechanistic and functional aspects of mitochondrial fission and fusion, and comments on the recent advances connecting mitochondrial dynamics with diabetes and diabetic complications. Antioxid. Redox Signal. 14, 439–457. PMID:20518704

Gromita: a fully integrated graphical user interface to gromacs 4.

PubMed

Sellis, Diamantis; Vlachakis, Dimitrios; Vlassi, Metaxia

2009-09-07

Gromita is a fully integrated and efficient graphical user interface (GUI) to the recently updated molecular dynamics suite Gromacs, version 4. Gromita is a cross-platform, perl/tcl-tk based, interactive front end designed to break the command line barrier and introduce a new user-friendly environment to run molecular dynamics simulations through Gromacs. Our GUI features a novel workflow interface that guides the user through each logical step of the molecular dynamics setup process, making it accessible to both advanced and novice users. This tool provides a seamless interface to the Gromacs package, while providing enhanced functionality by speeding up and simplifying the task of setting up molecular dynamics simulations of biological systems. Gromita can be freely downloaded from http://bio.demokritos.gr/gromita/.

Matrix product algorithm for stochastic dynamics on networks applied to nonequilibrium Glauber dynamics

NASA Astrophysics Data System (ADS)

Barthel, Thomas; De Bacco, Caterina; Franz, Silvio

2018-01-01

We introduce and apply an efficient method for the precise simulation of stochastic dynamical processes on locally treelike graphs. Networks with cycles are treated in the framework of the cavity method. Such models correspond, for example, to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon ideas from quantum many-body theory, our approach is based on a matrix product approximation of the so-called edge messages—conditional probabilities of vertex variable trajectories. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the matrix product edge messages (MPEM) in truncations. In contrast to Monte Carlo simulations, the algorithm has a better error scaling and works for both single instances as well as the thermodynamic limit. We employ it to examine prototypical nonequilibrium Glauber dynamics in the kinetic Ising model. Because of the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations.

Time-resolved observation of protein allosteric communication

PubMed Central

Buchenberg, Sebastian; Sittel, Florian; Stock, Gerhard

2017-01-01

Allostery represents a fundamental mechanism of biological regulation that is mediated via long-range communication between distant protein sites. Although little is known about the underlying dynamical process, recent time-resolved infrared spectroscopy experiments on a photoswitchable PDZ domain (PDZ2S) have indicated that the allosteric transition occurs on multiple timescales. Here, using extensive nonequilibrium molecular dynamics simulations, a time-dependent picture of the allosteric communication in PDZ2S is developed. The simulations reveal that allostery amounts to the propagation of structural and dynamical changes that are genuinely nonlinear and can occur in a nonlocal fashion. A dynamic network model is constructed that illustrates the hierarchy and exceeding structural heterogeneity of the process. In compelling agreement with experiment, three physically distinct phases of the time evolution are identified, describing elastic response (≲0.1 ns), inelastic reorganization (∼100 ns), and structural relaxation (≳1μs). Issues such as the similarity to downhill folding as well as the interpretation of allosteric pathways are discussed. PMID:28760989

Dramatic effect of single-base mutation on the conformational dynamics of human telomeric G-quadruplex

PubMed Central

Lee, Ja Yil; Kim, D. S.

2009-01-01

Guanine-rich DNA sequences can form G-quadruplexes. These four-stranded structures are known to form in several genomic regions and to influence certain biological activities. Sometimes, the instability of G-quadruplexes causes the abnormal biological processes. Mutation is a culprit for the destabilization of G-quadruplexes, but the details of mutated G-quadruplexes are poorly understood. In this article, we investigated the conformational dynamics of single-base mutated human telomeric G-quadruplexes in the presence of K+ with single-molecule FRET spectroscopy. We observed that the replacement of single guanine by thymine in a G-track induces various folded structures, i.e. structural polymorphism. Moreover, direct observation of their dynamics revealed that a single-base mutation causes fast unfolding of folded states under physiological conditions. Furthermore, we found that the degree of destabilization varies according to mutation positions. When the central guanine of a G-track is replaced, the G-quadruplexes unfold quickly at any K+ concentrations and temperature. Meanwhile, outer-quartet mutated G-quadruplexes have heterogeneous dynamics at intermediate K+ concentrations and longstanding folded states at high K+ concentrations. Several factors such as base-stacking interaction and K+ coordination are responsible for the different dynamics according to the mutation position. PMID:19359361

Optically modulated fluorescence bioimaging: visualizing obscured fluorophores in high background.

PubMed

Hsiang, Jung-Cheng; Jablonski, Amy E; Dickson, Robert M

2014-05-20

Fluorescence microscopy and detection have become indispensible for understanding organization and dynamics in biological systems. Novel fluorophores with improved brightness, photostability, and biocompatibility continue to fuel further advances but often rely on having minimal background. The visualization of interactions in very high biological background, especially for proteins or bound complexes at very low copy numbers, remains a primary challenge. Instead of focusing on molecular brightness of fluorophores, we have adapted the principles of high-sensitivity absorption spectroscopy to improve the sensitivity and signal discrimination in fluorescence bioimaging. Utilizing very long wavelength transient absorptions of kinetically trapped dark states, we employ molecular modulation schemes that do not simultaneously modulate the background fluorescence. This improves the sensitivity and ease of implementation over high-energy photoswitch-based recovery schemes, as no internal dye reference or nanoparticle-based fluorophores are needed to separate the desired signals from background. In this Account, we describe the selection process for and identification of fluorophores that enable optically modulated fluorescence to decrease obscuring background. Differing from thermally stable photoswitches using higher-energy secondary lasers, coillumination at very low energies depopulates transient dark states, dynamically altering the fluorescence and giving characteristic modulation time scales for each modulatable emitter. This process is termed synchronously amplified fluorescence image recovery (SAFIRe) microscopy. By understanding and optically controlling the dye photophysics, we selectively modulate desired fluorophore signals independent of all autofluorescent background. This shifts the fluorescence of interest to unique detection frequencies with nearly shot-noise-limited detection, as no background signals are collected. Although the fluorescence brightness is improved slightly, SAFIRe yields up to 100-fold improved signal visibility by essentially removing obscuring, unmodulated background (Richards, C. I.; J. Am. Chem. Soc. 2009, 131, 4619). While SAFIRe exhibits a wide, linear dynamic range, we have demonstrated single-molecule signal recovery buried within 200 nM obscuring dye. In addition to enabling signal recovery through background reduction, each dye exhibits a characteristic modulation frequency indicative of its photophysical dynamics. Thus, these characteristic time scales offer opportunities not only to expand the dimensionality of fluorescence imaging by using dark-state lifetimes but also to distinguish the dynamics of subpopulations on the basis of photophysical versus diffusional time scales, even within modulatable populations. The continued development of modulation for signal recovery and observation of biological dynamics holds great promise for studying a range of transient biological phenomena in natural environments. Through the development of a wide range of fluorescent proteins, organic dyes, and inorganic emitters that exhibit significant dark-state populations under steady-state illumination, we can drastically expand the applicability of fluorescence imaging to probe lower-abundance complexes and their dynamics.

Using LabView for real-time monitoring and tracking of multiple biological objects

NASA Astrophysics Data System (ADS)

Nikolskyy, Aleksandr I.; Krasilenko, Vladimir G.; Bilynsky, Yosyp Y.; Starovier, Anzhelika

2017-04-01

Today real-time studying and tracking of movement dynamics of various biological objects is important and widely researched. Features of objects, conditions of their visualization and model parameters strongly influence the choice of optimal methods and algorithms for a specific task. Therefore, to automate the processes of adaptation of recognition tracking algorithms, several Labview project trackers are considered in the article. Projects allow changing templates for training and retraining the system quickly. They adapt to the speed of objects and statistical characteristics of noise in images. New functions of comparison of images or their features, descriptors and pre-processing methods will be discussed. The experiments carried out to test the trackers on real video files will be presented and analyzed.

A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: Linking ecological, population genetics and evolutionary processes.

PubMed

Ponciano, José Miguel

2017-11-22

Using a nonparametric Bayesian approach Palacios and Minin (2013) dramatically improved the accuracy, precision of Bayesian inference of population size trajectories from gene genealogies. These authors proposed an extension of a Gaussian Process (GP) nonparametric inferential method for the intensity function of non-homogeneous Poisson processes. They found that not only the statistical properties of the estimators were improved with their method, but also, that key aspects of the demographic histories were recovered. The authors' work represents the first Bayesian nonparametric solution to this inferential problem because they specify a convenient prior belief without a particular functional form on the population trajectory. Their approach works so well and provides such a profound understanding of the biological process, that the question arises as to how truly "biology-free" their approach really is. Using well-known concepts of stochastic population dynamics, here I demonstrate that in fact, Palacios and Minin's GP model can be cast as a parametric population growth model with density dependence and environmental stochasticity. Making this link between population genetics and stochastic population dynamics modeling provides novel insights into eliciting biologically meaningful priors for the trajectory of the effective population size. The results presented here also bring novel understanding of GP as models for the evolution of a trait. Thus, the ecological principles foundation of Palacios and Minin (2013)'s prior adds to the conceptual and scientific value of these authors' inferential approach. I conclude this note by listing a series of insights brought about by this connection with Ecology. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling

PubMed Central

Matsuura, Tomoaki; Tanimura, Naoki; Hosoda, Kazufumi; Yomo, Tetsuya; Shimizu, Yoshihiro

2017-01-01

To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions. PMID:28167777

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

Incorporating evolutionary processes into population viability models.

PubMed

Pierson, Jennifer C; Beissinger, Steven R; Bragg, Jason G; Coates, David J; Oostermeijer, J Gerard B; Sunnucks, Paul; Schumaker, Nathan H; Trotter, Meredith V; Young, Andrew G

2015-06-01

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence. © 2014 Society for Conservation Biology.

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.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

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

The AquaDEB project (phase I): Analysing the physiological flexibility of aquatic species and connecting physiological diversity to ecological and evolutionary processes by using Dynamic Energy Budgets

NASA Astrophysics Data System (ADS)

Alunno-Bruscia, Marianne; van der Veer, Henk W.; Kooijman, Sebastiaan A. L. M.

2009-08-01

The European Research Project AquaDEB (2007-2011, http://www.ifremer.fr/aquadeb/) is joining skills and expertise of some French and Dutch research institutes and universities to analyse the physiological flexibility of aquatic organisms and to link it to ecological and evolutionary processes within a common theoretical framework for quantitative bioenergetics [Kooijman, S.A.L.M., 2000. Dynamic energy and mass budgets in biological systems. Cambridge University Press, Cambridge]. The main scientific objectives in AquaDEB are i) to study and compare the sensitivity of aquatic species (mainly molluscs and fish) to environmental variability of natural or human origin, and ii) to evaluate the related consequences at different biological levels (individual, population, ecosystem) and temporal scales (life cycle, population dynamics, evolution). At mid-term life, the AquaDEB collaboration has already yielded interesting results by quantifying bio-energetic processes of various aquatic species (e.g. molluscs, fish, crustaceans, algae) with a single mathematical framework. It has also allowed to federate scientists with different backgrounds, e.g. mathematics, microbiology, ecology, chemistry, and working in different fields, e.g. aquaculture, fisheries, ecology, agronomy, ecotoxicology, climate change. For the two coming years, the focus of the AquaDEB collaboration will be in priority: (i) to compare energetic and physiological strategies among species through the DEB parameter values and to identify the factors responsible for any differences in bioenergetics and physiology; and to compare dynamic (DEB) versus static (SEB) energy models to study the physiological performance of aquatic species; (ii) to consider different scenarios of environmental disruption (excess of nutrients, diffuse or massive pollution, exploitation by man, climate change) to forecast effects on growth, reproduction and survival of key species; (iii) to scale up the models for a few species from the individual level up to the level of evolutionary processes.

Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial systems biology.

PubMed

Malina, Carl; Larsson, Christer; Nielsen, Jens

2018-08-01

Mitochondria are dynamic organelles of endosymbiotic origin that are essential components of eukaryal cells. They contain their own genetic machinery, have multicopy genomes and like their bacterial ancestors they consist of two membranes. However, the majority of the ancestral genome has been lost or transferred to the nuclear genome of the host, preserving only a core set of genes involved in oxidative phosphorylation. Mitochondria perform numerous biological tasks ranging from bioenergetics to production of protein co-factors, including heme and iron-sulfur clusters. Due to the importance of mitochondria in many cellular processes, mitochondrial dysfunction is implicated in a wide variety of human disorders. Much of our current knowledge on mitochondrial function and dysfunction comes from studies using Saccharomyces cerevisiae. This yeast has good fermenting capacity, rendering tolerance to mutations that inactivate oxidative phosphorylation and complete loss of mitochondrial DNA. Here, we review yeast mitochondrial metabolism and function with focus on S. cerevisiae and its contribution in understanding mitochondrial biology. We further review how systems biology studies, including mathematical modeling, has allowed gaining new insight into mitochondrial function, and argue that this approach may enable us to gain a holistic view on how mitochondrial function interacts with different cellular processes.

Pathways to Aging: The Mitochondrion at the Intersection of Biological and Psychosocial Sciences

PubMed Central

Picard, Martin

2011-01-01

Compelling evidence suggests that both biological and psychosocial factors impact the process of aging. However, our understanding of the dynamic interplay among biological and psychosocial factors across the life course is still fragmentary. For example, it needs to be established how the interaction of individual factors (e.g., genetic and epigenetic endowment and personality), behavioral factors (e.g., physical activity, diet, and stress management), and psychosocial experiences (e.g., social support, well-being, socioeconomic status, and marriage) in perinatal, childhood, and adulthood influence health across the aging continuum. This paper aims to outline potential intersection points serving as an interface between biological and psychosocial factors, with an emphasis on the mitochondrion. Mitochondria are cellular organelles which play a critical role in cellular senescence. Both chronic exposure to psychosocial stress and genetic-based mitochondrial dysfunction have strikingly similar biological consequences; both predispose individuals to adverse age-related health disorders and early mortality. Exploring the interactive nature of the factors resulting in pathways to normal healthy aging, as well as those leading to morbidity and early mortality, will continue to enhance our ability to translate research into effective practices that can be implemented throughout the life course to optimise the aging process. PMID:21961065

Pathways to aging: the mitochondrion at the intersection of biological and psychosocial sciences.

PubMed

Picard, Martin

2011-01-01

Compelling evidence suggests that both biological and psychosocial factors impact the process of aging. However, our understanding of the dynamic interplay among biological and psychosocial factors across the life course is still fragmentary. For example, it needs to be established how the interaction of individual factors (e.g., genetic and epigenetic endowment and personality), behavioral factors (e.g., physical activity, diet, and stress management), and psychosocial experiences (e.g., social support, well-being, socioeconomic status, and marriage) in perinatal, childhood, and adulthood influence health across the aging continuum. This paper aims to outline potential intersection points serving as an interface between biological and psychosocial factors, with an emphasis on the mitochondrion. Mitochondria are cellular organelles which play a critical role in cellular senescence. Both chronic exposure to psychosocial stress and genetic-based mitochondrial dysfunction have strikingly similar biological consequences; both predispose individuals to adverse age-related health disorders and early mortality. Exploring the interactive nature of the factors resulting in pathways to normal healthy aging, as well as those leading to morbidity and early mortality, will continue to enhance our ability to translate research into effective practices that can be implemented throughout the life course to optimise the aging process.

Multiple hypothesis tracking for cluttered biological image sequences.

PubMed

Chenouard, Nicolas; Bloch, Isabelle; Olivo-Marin, Jean-Christophe

2013-11-01

In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.

Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems

NASA Astrophysics Data System (ADS)

Hurtado, A.; Schires, K.; Henning, I. D.; Adams, M. J.

2012-03-01

We report an approach based upon vertical cavity surface emitting lasers (VCSELs) to reproduce optically different behaviors exhibited by biological neurons but on a much faster timescale. The technique proposed is based on the polarization switching and nonlinear dynamics induced in a single VCSEL under polarized optical injection. The particular attributes of VCSELs and the simple experimental configuration used in this work offer prospects of fast, reconfigurable processing elements with excellent fan-out and scaling potentials for use in future computational paradigms and artificial neural networks.

A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems

USGS Publications Warehouse

Collins, Scott L.; Belnap, Jayne; Grimm, N. B.; Rudgers, J. A.; Dahm, Clifford N.; D'Odorico, P.; Litvak, M.; Natvig, D. O.; Peters, Douglas C.; Pockman, W. T.; Sinsabaugh, R. L.; Wolf, B. O.

2014-01-01

Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.

Quantitative mass imaging of single biological macromolecules.

PubMed

Young, Gavin; Hundt, Nikolas; Cole, Daniel; Fineberg, Adam; Andrecka, Joanna; Tyler, Andrew; Olerinyova, Anna; Ansari, Ayla; Marklund, Erik G; Collier, Miranda P; Chandler, Shane A; Tkachenko, Olga; Allen, Joel; Crispin, Max; Billington, Neil; Takagi, Yasuharu; Sellers, James R; Eichmann, Cédric; Selenko, Philipp; Frey, Lukas; Riek, Roland; Galpin, Martin R; Struwe, Weston B; Benesch, Justin L P; Kukura, Philipp

2018-04-27

The cellular processes underpinning life are orchestrated by proteins and their interactions. The associated structural and dynamic heterogeneity, despite being key to function, poses a fundamental challenge to existing analytical and structural methodologies. We used interferometric scattering microscopy to quantify the mass of single biomolecules in solution with 2% sequence mass accuracy, up to 19-kilodalton resolution, and 1-kilodalton precision. We resolved oligomeric distributions at high dynamic range, detected small-molecule binding, and mass-imaged proteins with associated lipids and sugars. These capabilities enabled us to characterize the molecular dynamics of processes as diverse as glycoprotein cross-linking, amyloidogenic protein aggregation, and actin polymerization. Interferometric scattering mass spectrometry allows spatiotemporally resolved measurement of a broad range of biomolecular interactions, one molecule at a time. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Spatial Patterns in the Efficiency of the Biological Pump: What Controls Export Ratios at the Global Scale?

NASA Astrophysics Data System (ADS)

Moore, J. K.

2016-02-01

The efficiency of the biological pump is influenced by complex interactions between chemical, biological, and physical processes. The efficiency of export out of surface waters and down through the water column to the deep ocean has been linked to a number of factors including biota community composition, production of mineral ballast components, physical aggregation and disaggregation processes, and ocean oxygen concentrations. I will examine spatial patterns in the export ratio and the efficiency of the biological pump at the global scale using the Community Earth System Model (CESM). There are strong spatial variations in the export efficiency as simulated by the CESM, which are strongly correlated with new nutrient inputs to the euphotic zone and their impacts on phytoplankton community structure. I will compare CESM simulations that include dynamic, variable export ratios driven by the phytoplankton community structure, with simulations that impose a near-constant export ratio to examine the effects of export efficiency on nutrient and surface chlorophyll distributions. The model predicted export ratios will also be compared with recent satellite-based estimates.

Live Imaging of Centriole Dynamics by Fluorescently Tagged Proteins in Starfish Oocyte Meiosis.

PubMed

Borrego-Pinto, Joana; Somogyi, Kálmán; Lénárt, Péter

2016-01-01

High throughput DNA sequencing, the decreasing costs of DNA synthesis, and universal techniques for genetic manipulation have made it much easier and quicker to establish molecular tools for any organism than it has been 5 years ago. This opens a great opportunity for reviving "nonconventional" model organisms, which are particularly suited to study a specific biological process and many of which have already been established before the era of molecular biology. By taking advantage of transcriptomics, in particular, these systems can now be easily turned into full fetched models for molecular cell biology.As an example, here we describe how we established molecular tools in the starfish Patiria miniata, which has been a popular model for cell and developmental biology due to the synchronous and rapid development, transparency, and easy handling of oocytes, eggs, and embryos. Here, we detail how we used a de novo assembled transcriptome to produce molecular markers and established conditions for live imaging to investigate the molecular mechanisms underlying centriole elimination-a poorly understood process essential for sexual reproduction of animal species.

The GP problem: quantifying gene-to-phenotype relationships.

PubMed

Cooper, Mark; Chapman, Scott C; Podlich, Dean W; Hammer, Graeme L

2002-01-01

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Resolving biomolecular motion and interactions by R2 and R1ρ relaxation dispersion NMR.

PubMed

Walinda, Erik; Morimoto, Daichi; Sugase, Kenji

2018-04-26

Among the tools of structural biology, NMR spectroscopy is unique in that it not only derives a static three-dimensional structure, but also provides an atomic-level description of the local fluctuations and global dynamics around this static structure. A battery of NMR experiments is now available to probe the motions of proteins and nucleic acids over the whole biologically relevant timescale from picoseconds to hours. Here we focus on one of these methods, relaxation dispersion, which resolves dynamics on the micro- to millisecond timescale. Key biological processes that occur on this timescale include enzymatic catalysis, ligand binding, and local folding. In other words, relaxation-dispersion-resolved dynamics are often closely related to the function of the molecule and therefore highly interesting to the structural biochemist. With an astounding sensitivity of ∼0.5%, the method detects low-population excited states that are invisible to any other biophysical method. The kinetics of the exchange between the ground state and excited states are quantified in the form of the underlying exchange rate, while structural information about the invisible excited state is obtained in the form of its chemical shift. Lastly, the population of the excited state can be derived. This diversity in the information that can be obtained makes relaxation dispersion an excellent method to study the detailed mechanisms of conformational transitions and molecular interactions. Here we describe the two branches of relaxation dispersion, R 2 and R 1ρ , discussing their applicability, similarities, and differences, as well as recent developments in pulse sequence design and data processing. Copyright © 2018 Elsevier Inc. All rights reserved.

An individual-based model of zebrafish population dynamics accounting for energy dynamics.

PubMed

Beaudouin, Rémy; Goussen, Benoit; Piccini, Benjamin; Augustine, Starrlight; Devillers, James; Brion, François; Péry, Alexandre R R

2015-01-01

Developing population dynamics models for zebrafish is crucial in order to extrapolate from toxicity data measured at the organism level to biological levels relevant to support and enhance ecological risk assessment. To achieve this, a dynamic energy budget for individual zebrafish (DEB model) was coupled to an individual based model of zebrafish population dynamics (IBM model). Next, we fitted the DEB model to new experimental data on zebrafish growth and reproduction thus improving existing models. We further analysed the DEB-model and DEB-IBM using a sensitivity analysis. Finally, the predictions of the DEB-IBM were compared to existing observations on natural zebrafish populations and the predicted population dynamics are realistic. While our zebrafish DEB-IBM model can still be improved by acquiring new experimental data on the most uncertain processes (e.g. survival or feeding), it can already serve to predict the impact of compounds at the population level.

An Individual-Based Model of Zebrafish Population Dynamics Accounting for Energy Dynamics

PubMed Central

Beaudouin, Rémy; Goussen, Benoit; Piccini, Benjamin; Augustine, Starrlight; Devillers, James; Brion, François; Péry, Alexandre R. R.

2015-01-01

Developing population dynamics models for zebrafish is crucial in order to extrapolate from toxicity data measured at the organism level to biological levels relevant to support and enhance ecological risk assessment. To achieve this, a dynamic energy budget for individual zebrafish (DEB model) was coupled to an individual based model of zebrafish population dynamics (IBM model). Next, we fitted the DEB model to new experimental data on zebrafish growth and reproduction thus improving existing models. We further analysed the DEB-model and DEB-IBM using a sensitivity analysis. Finally, the predictions of the DEB-IBM were compared to existing observations on natural zebrafish populations and the predicted population dynamics are realistic. While our zebrafish DEB-IBM model can still be improved by acquiring new experimental data on the most uncertain processes (e.g. survival or feeding), it can already serve to predict the impact of compounds at the population level. PMID:25938409

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

A Computational Approach to Increase Time Scales in Brownian Dynamics–Based Reaction-Diffusion Modeling

PubMed Central

Frazier, Zachary

2012-01-01

Abstract Particle-based Brownian dynamics simulations offer the opportunity to not only simulate diffusion of particles but also the reactions between them. They therefore provide an opportunity to integrate varied biological data into spatially explicit models of biological processes, such as signal transduction or mitosis. However, particle based reaction-diffusion methods often are hampered by the relatively small time step needed for accurate description of the reaction-diffusion framework. Such small time steps often prevent simulation times that are relevant for biological processes. It is therefore of great importance to develop reaction-diffusion methods that tolerate larger time steps while maintaining relatively high accuracy. Here, we provide an algorithm, which detects potential particle collisions prior to a BD-based particle displacement and at the same time rigorously obeys the detailed balance rule of equilibrium reactions. We can show that for reaction-diffusion processes of particles mimicking proteins, the method can increase the typical BD time step by an order of magnitude while maintaining similar accuracy in the reaction diffusion modelling. PMID:22697237

Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient.

PubMed

An, Gary; Faeder, James; Vodovotz, Yoram

2008-01-01

The pathophysiology of the burn patient manifests the full spectrum of the complexity of the inflammatory response. In the acute phase, inflammation may have negative effects via capillary leak, the propagation of inhalation injury, and development of multiple organ failure. Attempts to mediate these processes remain a central subject of burn care research. Conversely, inflammation is a necessary prologue and component in the later stage processes of wound healing. Despite the volume of information concerning the cellular and molecular processes involved in inflammation, there exists a significant gap between the knowledge of mechanistic pathophysiology and the development of effective clinical therapeutic regimens. Translational systems biology (TSB) is the application of dynamic mathematical modeling and certain engineering principles to biological systems to integrate mechanism with phenomenon and, importantly, to revise clinical practice. This study will review the existing applications of TSB in the areas of inflammation and wound healing, relate them to specific areas of interest to the burn community, and present an integrated framework that links TSB with traditional burn research.

Corrections to chance fluctuations: quantum mind in biological evolution?

PubMed

Damiani, Giuseppe

2009-01-01

According to neo-Darwinian theory, biological evolution is produced by natural selection of random hereditary variations. This assumption stems from the idea of a mechanical and deterministic world based on the laws of classic physics. However, the increased knowledge of relationships between metabolism, epigenetic systems, and editing of nucleic acids suggests the existence of self-organized processes of adaptive evolution in response to environmental stresses. Living organisms are open thermodynamic systems which use entropic decay of external source of electromagnetic energy to increase their internal dynamic order and to generate new genetic and epigenetic information with a high degree of coherency and teleonomic creativity. Sensing, information processing, and decision making of biological systems might be mainly quantum phenomena. Amplification of microscopic quantum events using the long-range correlation of fractal structures, at the borderline between deterministic order and unpredictable chaos, may be used to direct a reproducible transition of the biological systems towards a defined macroscopic state. The discoveries of many natural genetic engineering systems, the ability to choose the most effective solutions, and the emergence of complex forms of consciousness at different levels confirm the importance of mind-action directed processes in biological evolution, as suggested by Alfred Russel Wallace. Although the main Darwinian principles will remain a crucial component of our understanding of evolution, a radical rethinking of the conceptual structure of the neo-Darwinian theory is needed.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Photon spectroscopy by picoseconds differential Geiger-mode Si photomultiplier

NASA Astrophysics Data System (ADS)

Yamamoto, Masanobu; Hernandez, Keegan; Robinson, J. Paul

2018-02-01

The pixel array silicon photomultiplier (SiPM) is known as an excellent photon sensor with picoseconds avalanche process with the capacity for millions amplification of photoelectrons. In addition, a higher quantum efficiency(QE), small size, low bias voltage, light durability are attractive features for biological applications. The primary disadvantage is the limited dynamic range due to the 50ns recharge process and a high dark count which is an additional hurdle. We have developed a wide dynamic Si photon detection system applying ultra-fast differentiation signal processing, temperature control by thermoelectric device and Giga photon counter with 9 decimal digits dynamic range. The tested performance is six orders of magnitude with 600ps pulse width and sub-fW sensitivity. Combined with 405nm laser illumination and motored monochromator, Laser Induced Fluorescence Photon Spectrometry (LIPS) has been developed with a scan range from 200 900nm at maximum of 500nm/sec and 1nm FWHM. Based on the Planck equation E=hν, this photon counting spectrum provides a fundamental advance in spectral analysis by digital processing. Advantages include its ultimate sensitivity, theoretical linearity, as well as quantitative and logarithmic analysis without use of arbitrary units. Laser excitation is also useful for evaluation of photobleaching or oxidation in materials by higher energy illumination. Traditional typical photocurrent detection limit is about 1pW which includes millions of photons, however using our system it is possible to evaluate the photon spectrum and determine background noise and auto fluorescence(AFL) in optics in any cytometry or imaging system component. In addition, the photon-stream digital signal opens up a new approach for picosecond time-domain analysis. Photon spectroscopy is a powerful method for analysis of fluorescence and optical properties in biology.

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.

Logic-based models in systems biology: a predictive and parameter-free network analysis method†

PubMed Central

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

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.

Engineering Microbial Metabolite Dynamics and Heterogeneity.

PubMed

Schmitz, Alexander C; Hartline, Christopher J; Zhang, Fuzhong

2017-10-01

As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling Dynamic Regulatory Processes in Stroke.

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

McDermott, Jason E.; Jarman, Kenneth D.; Taylor, Ronald C.

2012-10-11

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to developmore » dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.« less

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

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

PubMed

Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Network dynamics and systems biology

NASA Astrophysics Data System (ADS)

Norrell, Johannes A.

The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase. Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in A. thaliana. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORT-ROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.

Structure-based control of complex networks with nonlinear dynamics.

PubMed

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

How does "not left" become "right"? Electrophysiological evidence for a dynamic conflict-bound negation processing account.

PubMed

Dudschig, Carolin; Kaup, Barbara

2018-05-01

Human thought and language is traditionally considered as abstract, amodal, and symbolic. However, recent theories propose that high-level human cognition is directly linked to basic, modal biological systems such as sensorimotor areas. Despite this influential representational debate very little is known regarding whether the mechanisms involved in sensorimotor control are also shared with higher-level cognitive processes, such as language comprehension. We investigated negation as a universal of human language, addressing two key questions: (a) Does negation result in a conflict-like representation? (b) Does negation trigger executive control adjustments in a similar manner as standard information processing conflicts do (e.g., Simon, Flanker)? Electrophysiological data indicated that phrases such as "not left/not right" result in initial activation of the to-be-negated information and subsequently the outcome of the negation process. More importantly, our findings also suggest that negation triggers conflict-related adjustments in information processing in line with traditional conflict tasks. Trial-by-trial conflict adaptation patterns in both behavioral and electrophysiological data indicated that negation processing dynamically changes depending on the current cognitive state. In summary, negation processing results in cognitive conflict, and dynamic influences of the cognitive state determine conflict resolution, that is, negation implementation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A global "imaging'' view on systems approaches in immunology.

PubMed

Ludewig, Burkhard; Stein, Jens V; Sharpe, James; Cervantes-Barragan, Luisa; Thiel, Volker; Bocharov, Gennady

2012-12-01

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures

PubMed Central

de la Fuente, Ildefonso Martínez

2010-01-01

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life. PMID:20957111

Reaction-diffusion systems in natural sciences and new technology transfer

NASA Astrophysics Data System (ADS)

Keller, André A.

2012-12-01

Diffusion mechanisms in natural sciences and innovation management involve partial differential equations (PDEs). This is due to their spatio-temporal dimensions. Functional semi-discretized PDEs (with lattice spatial structures or time delays) may be even more adapted to real world problems. In the modeling process, PDEs can also formalize behaviors, such as the logistic growth of populations with migration, and the adopters’ dynamics of new products in innovation models. In biology, these events are related to variations in the environment, population densities and overcrowding, migration and spreading of humans, animals, plants and other cells and organisms. In chemical reactions, molecules of different species interact locally and diffuse. In the management of new technologies, the diffusion processes of innovations in the marketplace (e.g., the mobile phone) are a major subject. These innovation diffusion models refer mainly to epidemic models. This contribution introduces that modeling process by using PDEs and reviews the essential features of the dynamics and control in biological, chemical and new technology transfer. This paper is essentially user-oriented with basic nonlinear evolution equations, delay PDEs, several analytical and numerical methods for solving, different solutions, and with the use of mathematical packages, notebooks and codes. The computations are carried out by using the software Wolfram Mathematica®7, and C++ codes.

Regulatory effects of cotranscriptional RNA structure formation and transitions.

PubMed

Liu, Sheng-Rui; Hu, Chun-Gen; Zhang, Jin-Zhi

2016-09-01

RNAs, which play significant roles in many fundamental biological processes of life, fold into sophisticated and precise structures. RNA folding is a dynamic and intricate process, which conformation transition of coding and noncoding RNAs form the primary elements of genetic regulation. The cellular environment contains various intrinsic and extrinsic factors that potentially affect RNA folding in vivo, and experimental and theoretical evidence increasingly indicates that the highly flexible features of the RNA structure are affected by these factors, which include the flanking sequence context, physiochemical conditions, cis RNA-RNA interactions, and RNA interactions with other molecules. Furthermore, distinct RNA structures have been identified that govern almost all steps of biological processes in cells, including transcriptional activation and termination, transcriptional mutagenesis, 5'-capping, splicing, 3'-polyadenylation, mRNA export and localization, and translation. Here, we briefly summarize the dynamic and complex features of RNA folding along with a wide variety of intrinsic and extrinsic factors that affect RNA folding. We then provide several examples to elaborate RNA structure-mediated regulation at the transcriptional and posttranscriptional levels. Finally, we illustrate the regulatory roles of RNA structure and discuss advances pertaining to RNA structure in plants. WIREs RNA 2016, 7:562-574. doi: 10.1002/wrna.1350 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

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.

Comparing Computer-Supported Dynamic Modeling and "Paper & Pencil" Concept Mapping Technique in Students' Collaborative Activity

ERIC Educational Resources Information Center

Komis, Vassilis; Ergazaki, Marida; Zogza, Vassiliki

2007-01-01

This study aims at highlighting the collaborative activity of two high school students (age 14) in the cases of modeling the complex biological process of plant growth with two different tools: the "paper & pencil" concept mapping technique and the computer-supported educational environment "ModelsCreator". Students' shared activity in both cases…

Visually Tracking Translocations in Living Cells | Center for Cancer Research

Cancer.gov

Chromosomal translocations, the fusion of pieces of DNA from different chromosomes, are often observed in cancer cells and can even cause cancer. However, little is known about the dynamics and regulation of translocation formation. To investigate this critical process, Tom Misteli, Ph.D., in CCR’s Laboratory of Receptor Biology and Gene Expression, and his colleague Vassilis

Trends in snag populations in drought-stressed mixed-conifer and ponderosa pine forests (1997-2007)

Treesearch

Joseph L. Ganey; Scott C. Vojta

2012-01-01

Snags provide important biological legacies, resources for numerous species of native wildlife, and contribute to decay dynamics and ecological processes in forested ecosystems. We monitored trends in snag populations from 1997 to 2007 in drought-stressed mixed-conifer and ponderosa pine (Pinus ponderosa Dougl. ex Laws) forests, northern Arizona. Median snag density...

Quantifying carbon sequestration in forest plantations by modeling the dynamics of above and below ground carbon pools

Treesearch

Chris A. Maier; Kurt H. Johnsen

2010-01-01

Intensive pine plantation management may provide opportunities to increase carbon sequestration in the Southeastern United States. Developing management options that increase fiber production and soil carbon sequestration require an understanding of the biological and edaphic processes that control soil carbon turnover. Belowground carbon resides primarily in three...

The theory, direction, and magnitude of ecosystem fire probability as constrained by precipitation and temperature

Treesearch

Richard Guyette; Michael C. Stambaugh; Daniel Dey; Rose Marie Muzika; Ben Bond-Lamberty

2017-01-01

The effects of climate on wildland fire confronts society across a range of different ecosystems. Water and temperature affect the combustion dynamics, irrespective of whether those are associated with carbon fueled motors or ecosystems, but through different chemical, physical, and biological processes. We use an ecosystem combustion equation developed with the...

Tree harvest in an experimental sand ecosystem: plant effects on nutrient dynamics and solute generation.

Treesearch

C. K. Keller; R. O' Brien; J. R. Havig; J. L. Smith; B. T. Bormann; D. Wang

2006-01-01

The hydrochemical signatures of forested ecosystems are known to be determined by a time-variant combination of physical-hydrologic, geochemical, and biologic processes. We studied subsurface potassium (K), calcium (Ca), and nitrate (NO3) in an experimental red-pine mesocosm to determine how trees affect the behavior of these nutrients in soil...

Noise shaping in populations of coupled model neurons.

PubMed

Mar, D J; Chow, C C; Gerstner, W; Adams, R W; Collins, J J

1999-08-31

Biological information-processing systems, such as populations of sensory and motor neurons, may use correlations between the firings of individual elements to obtain lower noise levels and a systemwide performance improvement in the dynamic range or the signal-to-noise ratio. Here, we implement such correlations in networks of coupled integrate-and-fire neurons using inhibitory coupling and demonstrate that this can improve the system dynamic range and the signal-to-noise ratio in a population rate code. The improvement can surpass that expected for simple averaging of uncorrelated elements. A theory that predicts the resulting power spectrum is developed in terms of a stochastic point-process model in which the instantaneous population firing rate is modulated by the coupling between elements.

A model of cell-wall dynamics during sporulation in Bacillus subtilis

NASA Astrophysics Data System (ADS)

Yap, Li-Wei; Endres, Robert G.

To survive starvation, Bacillus subtilis forms durable spores. After asymmetric cell division, the septum grows around the forespore in a process called engulfment, but the mechanism of force generation is unknown. Here, we derived a novel biophysical model for the dynamics of cell-wall remodeling during engulfment based on a balancing of dissipative, active, and mechanical forces. By plotting phase diagrams, we predict that sporulation is promoted by a line tension from the attachment of the septum to the outer cell wall, as well as by an imbalance in turgor pressures in the mother-cell and forespore compartments. We also predict that significant mother-cell growth hinders engulfment. Hence, relatively simple physical principles may guide this complex biological process.

Dynamic nanoplatforms in biosensor and membrane constitutional systems.

PubMed

Mahon, Eugene; Aastrup, Teodor; Barboiu, Mihail

2012-01-01

Molecular recognition in biological systems occurs mainly at interfacial environments such as membrane surfaces, enzyme active sites, or the interior of the DNA double helix. At the cell membrane surface, carbohydrate-protein recognition principles apply to a range of specific non-covalent interactions including immune response, cell proliferation, adhesion and death, cell-cell interaction and communication. Protein-protein recognition meanwhile accounts for signalling processes and ion channel structure. In this chapter we aim to describe such constitutional dynamic interfaces for biosensing and membrane transport applications. Constitutionally adaptive interfaces may mimic the recognition capabilities intrinsic to natural recognition processes. We present some recent examples of 2D and 3D constructed sensors and membranes of this type and describe their sensing and transport capabilities.

The allosteric communication pathways in KIX domain of CBP.

PubMed

Palazzesi, Ferruccio; Barducci, Alessandro; Tollinger, Martin; Parrinello, Michele

2013-08-27

Allosteric regulation plays an important role in a myriad of biomacromolecular processes. Specifically, in a protein, the process of allostery refers to the transmission of a local perturbation, such as ligand binding, to a distant site. Decades after the discovery of this phenomenon, models built on static images of proteins are being reconsidered with the knowledge that protein dynamics plays an important role in its function. Molecular dynamics simulations are a valuable tool for studying complex biomolecular systems, providing an atomistic description of their structure and dynamics. Unfortunately, their predictive power has been limited by the complexity of the biomolecule free-energy surface and by the length of the allosteric timescale (in the order of milliseconds). In this work, we are able to probe the origins of the allosteric changes that transcription factor mixed lineage leukemia (MLL) causes to the interactions of KIX domain of CREB-binding protein (CBP) with phosphorylated kinase inducible domain (pKID), by combing all-atom molecular dynamics with enhanced sampling methods recently developed in our group. We discuss our results in relation to previous NMR studies. We also develop a general simulations protocol to study allosteric phenomena and many other biological processes that occur in the micro/milliseconds timescale.

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Microbial phylogeny determines transcriptional response of resistome to dynamic composting processes.

PubMed

Wang, Cheng; Dong, Da; Strong, P J; Zhu, Weijing; Ma, Zhuang; Qin, Yong; Wu, Weixiang

2017-08-16

Animal manure is a reservoir of antibiotic resistance genes (ARGs) that pose a potential health risk globally, especially for resistance to the antibiotics commonly used in livestock production (such as tetracycline, sulfonamide, and fluoroquinolone). Currently, the effects of biological treatment (composting) on the transcriptional response of manure ARGs and their microbial hosts are not well characterized. Composting is a dynamic process that consists of four distinct phases that are distinguished by the temperature resulting from microbial activity, namely the mesophilic, thermophilic, cooling, and maturing phases. In this study, changes of resistome expression were determined and related to active microbiome profiles during the dynamic composting process. This was achieved by integrating metagenomic and time series metatranscriptomic data for the evolving microbial community during composting. Composting noticeably reduced the aggregated expression level of the manure resistome, which primarily consisted of genes encoding for tetracycline, vancomycin, fluoroquinolone, beta-lactam, and aminoglycoside resistance, as well as efflux pumps. Furthermore, a varied transcriptional response of resistome to composting at the ARG levels was highlighted. The expression of tetracycline resistance genes (tetM-tetW-tetO-tetS) decreased during composting, where distinctive shifts in the four phases of composting were related to variations in antibiotic concentration. Composting had no effect on the expression of sulfonamide and fluoroquinolone resistance genes, which increased slightly during the thermophilic phase and then decreased to initial levels. As indigenous populations switched greatly throughout the dynamic composting, the core resistome persisted and their reservoir hosts' composition was significantly correlated with dynamic active microbial phylogenetic structure. Hosts for sulfonamide and fuoroquinolone resistance genes changed notably in phylognetic structure and underwent an initial increase and then a decrease in abundance. By contrast, hosts for tetracycline resistance genes (tetM-tetW-tetO-tetS) exhibited a constant decline through time. The transcriptional patterns of a core resistome over the course of composting were identified, and microbial phylogeny was the key determinant in defining the varied transcriptional response of resistome to this dynamic biological process. This research demonstrated the benefits of composting for manure treatment. It reduced the risk of emerging environmental contaminants such as tetracyclines, tetracycline resistance genes, and clinically relevant pathogens carrying ARGs, as well as RNA viruses and bacteriophages.

Real-Time Single Molecule Visualization of SH2 Domain Membrane Recruitment in Growth Factor Stimulated Cells.

PubMed

Oh, Dongmyung

2017-01-01

In the last decade, single molecule tracking (SMT) techniques have emerged as a versatile tool for molecular cell biology research. This approach allows researchers to monitor the real-time behavior of individual molecules in living cells with nanometer and millisecond resolution. As a result, it is possible to visualize biological processes as they occur at a molecular level in real time. Here we describe a method for the real-time visualization of SH2 domain membrane recruitment from the cytoplasm to epidermal growth factor (EGF) induced phosphotyrosine sites on the EGF receptor. Further, we describe methods that utilize SMT data to define SH2 domain membrane dynamics parameters such as binding (τ), dissociation (k d ), and diffusion (D) rates. Together these methods may allow us to gain greater understanding of signal transduction dynamics and the molecular basis of disease-related aberrant pathways.

Theoretical Investigation of Optical Detection and Recognition of Single Biological Molecules Using Coherent Dynamics of Exciton-Plasmon Coupling

PubMed Central

Sadeghi, S. M.; Hood, B.; Patty, K. D.; Mao, C.-B.

2013-01-01

We use quantum coherence in a system consisting of one metallic nanorod and one semi-conductor quantum dot to investigate a plasmonic nanosensor capable of digital optical detection and recognition of single biological molecules. In such a sensor the adsorption of a specific molecule to the nanorod turns off the emission of the system when it interacts with an optical pulse having a certain intensity and temporal width. The proposed quantum sensors can count the number of molecules of the same type or differentiate between molecule types with digital optical signals that can be measured with high certainty. We show that these sensors are based on the ultrafast upheaval of coherent dynamics of the system and the removal of coherent blockage of energy transfer from the quantum dot to the nanorod once the adsorption process has occurred. PMID:24040424

Monitoring the synthesis of biomolecules using mass spectrometry.

PubMed

Miyagi, Masaru; Kasumov, Takhar

2016-10-28

The controlled and selective synthesis/clearance of biomolecules is critical for most cellular processes. In most high-throughput 'omics' studies, we measure the static quantities of only one class of biomolecules (e.g. DNA, mRNA, proteins or metabolites). It is, however, important to recognize that biological systems are highly dynamic in which biomolecules are continuously renewed and different classes of biomolecules interact and affect each other's production/clearance. Therefore, it is necessary to measure the turnover of diverse classes of biomolecules to understand the dynamic nature of biological systems. Herein, we explain why the kinetic analysis of a diverse range of biomolecules is important and how such an analysis can be done. We argue that heavy water ((2)H2O) could be a universal tracer for monitoring the synthesis of biomolecules on a global scale.This article is part of the themed issue 'Quantitative mass spectrometry'. © 2016 The Author(s).

Dynamics and biological relevance of DNA demethylation in Arabidopsis antibacterial defense.

PubMed

Yu, Agnès; Lepère, Gersende; Jay, Florence; Wang, Jingyu; Bapaume, Laure; Wang, Yu; Abraham, Anne-Laure; Penterman, Jon; Fischer, Robert L; Voinnet, Olivier; Navarro, Lionel

2013-02-05

DNA methylation is an epigenetic mark that silences transposable elements (TEs) and repeats. Whereas the establishment and maintenance of DNA methylation are relatively well understood, little is known about their dynamics and biological relevance in plant and animal innate immunity. Here, we show that some TEs are demethylated and transcriptionally reactivated during antibacterial defense in Arabidopsis. This effect is correlated with the down-regulation of key transcriptional gene silencing factors and is partly dependent on an active demethylation process. DNA demethylation restricts multiplication and vascular propagation of the bacterial pathogen Pseudomonas syringae in leaves and, accordingly, some immune-response genes, containing repeats in their promoter regions, are negatively regulated by DNA methylation. This study provides evidence that DNA demethylation is part of a plant-induced immune response, potentially acting to prime transcriptional activation of some defense genes linked to TEs/repeats.

The simulation approach to lipid-protein interactions.

PubMed

Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

2013-01-01

The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

Monitoring the synthesis of biomolecules using mass spectrometry

PubMed Central

2016-01-01

The controlled and selective synthesis/clearance of biomolecules is critical for most cellular processes. In most high-throughput ‘omics’ studies, we measure the static quantities of only one class of biomolecules (e.g. DNA, mRNA, proteins or metabolites). It is, however, important to recognize that biological systems are highly dynamic in which biomolecules are continuously renewed and different classes of biomolecules interact and affect each other's production/clearance. Therefore, it is necessary to measure the turnover of diverse classes of biomolecules to understand the dynamic nature of biological systems. Herein, we explain why the kinetic analysis of a diverse range of biomolecules is important and how such an analysis can be done. We argue that heavy water (2H2O) could be a universal tracer for monitoring the synthesis of biomolecules on a global scale. This article is part of the themed issue ‘Quantitative mass spectrometry’. PMID:27644976

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.

Regulated expression of the lncRNA TERRA and its impact on telomere biology.

PubMed

Oliva-Rico, Diego; Herrera, Luis A

2017-10-01

The telomere protects against genomic instability by minimizing the accelerated end resection of the genetic material, a phenomenon that results in severe chromosome instability that could favor the transformation of a cell by enabling the emergence of tumor-promoting mutations. Some mechanisms that avoid this fate, such as capping and loop formation, have been very well characterized; however, telomeric non-coding transcripts, such as long non-coding RNAs (lncRNAs), should also be considered in this context because they play roles in the organization of telomere dynamics, involving processes such as replication, degradation, extension, and heterochromatin stabilization. Although the mechanism through which the expression of telomeric transcripts regulates telomere dynamics is not yet clear, a non-coding RNA component opens the research options in telomere biology and the impact that it can have on telomere-associated diseases such as cancer. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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

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

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

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.

Focused fluorescence excitation with time-reversed ultrasonically encoded light and imaging in thick scattering media

NASA Astrophysics Data System (ADS)

Lai, Puxiang; Suzuki, Yuta; Xu, Xiao; Wang, Lihong V.

2013-07-01

Scattering dominates light propagation in biological tissue, and therefore restricts both resolution and penetration depth in optical imaging within thick tissue. As photons travel into the diffusive regime, typically 1 mm beneath human skin, their trajectories transition from ballistic to diffusive due to the increased number of scattering events, which makes it impossible to focus, much less track, photon paths. Consequently, imaging methods that rely on controlled light illumination are ineffective in deep tissue. This problem has recently been addressed by a novel method capable of dynamically focusing light in thick scattering media via time reversal of ultrasonically encoded (TRUE) diffused light. Here, using photorefractive materials as phase conjugate mirrors, we show a direct visualization and dynamic control of optical focusing with this light delivery method, and demonstrate its application for focused fluorescence excitation and imaging in thick turbid media. These abilities are increasingly critical for understanding the dynamic interactions of light with biological matter and processes at different system levels, as well as their applications for biomedical diagnosis and therapy.